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Dissertations / Theses on the topic 'Deforestation Forests and forestry Remote sensing'
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Briggs, Nathan A. "Forest Cover Change and Assessment of Drivers of Forest Conversion in Midcoast Maine between 2000 and 2006." Fogler Library, University of Maine, 2008. http://www.library.umaine.edu/theses/pdf/BriggsNA2008.pdf.
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Gilruth, Peter Thomas. "Modelling deforestation and land degradation in the Guinea highlands of West Africa using remote sensing and geographic information systems." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185708.
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A dynamic spatial model of deforestation and land-use change was developed from remotely sensed data for the Fouta Djallon mountain range in the Republic of Guinea, West Africa. The objective was to simulate patterns of land clearing for shifting cultivation in terms of farmers' selection behavior for new fields based on topography and proximity to villages. Data describing the current and historic condition of the vegetation cover, land use, and erosion for a watershed in Guinea were derived from aerial photography and ground sampling. Maps of these conditions were prepared and entered in a geographic information system (GIS) together with topographic data. From these data, maps of secondary variables (slope, village proximity, site productivity, and labor) were derived using the spatial operators contained in the GIS. These variables were ranked for agricultural preference and combined following a pair-wise hierarchy to generate a composite agricultural site-preference surface. This ranking was done in iterations, using a two-year time increment, which corresponds to the typical duration of cultivation for any one field. Different variable combinations and underlying assumptions of model logic were tested to determine influence on simulation results. To validate the model, the projected landscape was compared with land-use data collected in 1989. Although the model did not simulate the farmers' selection behavior for topography and village proximity successfully, test results with individual variables suggest that site productivity as determined by the length of fallow is a critical variable in the site selection process. The addition of site quality data should improve model results. The watershed in which this study was performed is the focus of a development initiative supported by the U.S. Agency for International Development (USAID), in which viable options are being sought for regional application. Thus, aside from documenting the dynamics of shifting cultivation, this model allows planners to evaluate alternative strategies of land-use conversion with a graphic display of zones of potential hazards. Finally, the data contained in the GIS serve as a structure for monitoring long-term change in the region.
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Näsström, Rickard. "Reaching the 2014 UN New York Declaration on Forests Goals, using satellites to monitor global value chains." Thesis, Stockholms universitet, Kulturgeografiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-128585.
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This master thesis in geography investigates how remote sens- ing can be used in Transnational Corporations (TNC) global Corporate Social Responsibility (CSR) initiatives. The study aims to delineate an accurate method in remote sensing to be used to monitor deforestation in global value chains. Research questions asked are 1) What are the current monitoring practises used by TNCs to monitor global value chains? 2) Which is the most user-friendly and accurate remote sensing technique to map deforestation? 3) How can remote sensing successfully be implemented in TNCs CSR-initiatives? The study is approached from two perspectives, building on theories of value chains, and qualitative methods to answer the first research question. While the second question is a method study, investigating how well a spectral approach versus a contextual approach can map deforest- ation in Landsat scenes. The results are compared with Global Forest Watch (GFW), and the highest accuracy were acquired from the WICS (Window Indipendent Context Segmentation) technique. Conclusions includes that remote sensing can be used in CSR initiatives, to establish a baseline level or as a fifth dimen- sion in a score sheet approach. However, inconclusive mapping of value chains are a big hinder today.
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Dyne, Matthew Aaron. "Drivers of Land Cover Change via Deforestation in Selected Post-Soviet Russian Cities." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1550616624452609.
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Lehnert, Matthew R. "Ghost Hunting and A Moroccan Forest: a geography of Madness." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1372856199.
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Ndegwa, Lucy W. "Monitoring the Status of Mt. Kenya Forest Using Multi-Temporal Landsat Data." Miami University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=miami1125426520.
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Cassells, Gemma Fiona. "Can remote sensing be used to support sustainable forestry in Malawi?" Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8050.
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Sustainable forest management is a key issue in Malawi. Malawi is a relatively small, resource poor, densely populated country, which in some areas is close to exceeding the energy capacity of the environment to support it. Despite the importance of forestry in Malawi, there is a severe lack of knowledge about the current state of Malawi’s forest resources. Remote sensing has the potential to provide current and historical insights into forest cover change. However, Malawi faces a number of key challenges with regards to in-country remote sensing. These include technical capacity for obtaining accurate and consistent forest area and biomass estimates, with errors at acceptable levels, as well as the necessary supporting capacity development for individuals and institutions. This thesis examines how remote sensing can be used to support sustainable forestry in Malawi, by assessing the use of both optical and Synthetic Aperture Radar (SAR) data for mapping forest cover, forest cover change and aboveground biomass (AGB). L-band SAR data was used to try and establish a relationship between radar backscatter and biomass, which has been achieved many times in other areas. However, no correlations between any field-based forest metric and backscatter explained enough of the variability in the datasets to be used to develop empirical relationships between the variables. There were also differences between my field measured AGB and AGB values predicted by a published backscatter-biomass relationship for African dry forests. The speckle inherent in SAR imagery, the heterogeneity of Malawi’s dominant miombo savanna, and Malawi’s variable topography are likely to have played a significant role in this. Two different MODIS products were investigated for their potential for mapping forest cover change, with regards to potential REDD+ schemes. As part of this, a published equation was used to calculate the break-even point for REDD+ schemes in Malawi, using estimates of forest area and deforestation for the United Nations Forest Resources Assessment 2010. The results of this equation show that measurement error is the most important factor in determining whether or not Malawi can make REDD+ economically viable, particularly at lower levels of deforestation. While neither of the MODIS products were able to produce a verifiable forest cover change map, they do confirm that Malawi is experiencing some level of forest loss, and help to narrow down the range of possible forest loss rates Malawi is experiencing to between 1-3% net forest loss per year. Finally, this thesis examines global trends in the engagement of developing country researchers with global academic remote sensing research, to investigate differences in in-country capacity for monitoring forests using remote sensing. The results of this found that while a significant proportion of Earth observation research (44%) has developing countries as their object of research, less than 3% of publications have authors working, or affiliated to, a developing country (excluding China, India and Brazil, which are not only countries in transition, but have well established EO capacity). These patterns appear consistent over the past 20 years, despite the increasing awareness of the importance of capacity development over this period. Despite inconclusive results from the approaches examined here, remote sensing can play a role in improving understanding about the dynamics of Malawi’s forest resources. There is a need for nationwide accurate, validated forest maps that can be repeated at least on a yearly basis, and remote sensing could produced these without the resources needed to conduct full national ground inventories each year. If remote sensing is to be useful as a forest mapping tool in Malawi, it needs to provide consistent, verifiable and updatable estimates of forest cover and biomass change. This ideally needs to be achieved using free or low cost data, and by using open source or open access software, as this will better enable incountry researchers to conduct on-going forest mapping activities.
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Wang, Wanting. "Satellite remote sensing of forest disturbances caused by hurricanes and wildland fires." Fairfax, VA : George Mason University, 2009. http://hdl.handle.net/1920/4579.
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Thesis (Ph.D.)--George Mason University, 2009.<br>Vita: p. 151. Thesis director: John J. Qu. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Earth Systems and Geoinformation Sciences. Title from PDF t.p. (viewed Oct. 11, 2009). Includes bibliographical references (p. 136-150). Also issued in print.
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Holmström, Hampus. "Data acquisition for forestry planning by remote sensing based sample plot imputation /." Umeå : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 2001. http://epsilon.slu.se/avh/2001/91-576-6086-7.pdf.
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Ike, Felix. "Evaluation of the impact of climate and human induced changes on the Nigerian forest using remote sensing." Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/22127.
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The majority of the impact of climate and human induced changes on forest are related to climate variability and deforestation. Similarly, changes in forest phenology due to climate variability and deforestation has been recognized as being among the most important early indicators of the impact of environmental change on forest ecosystem functioning. Comprehensive data on baseline forest cover changes including deforestation is required to provide background information needed for governments to make decision on Reducing Emissions from Deforestation and Forest Degradation (REED). Despite the fact that Nigeria ranks among the countries with highest deforestation rates based on Food and Agricultural Organization estimates, only a few studies have aimed at mapping forest cover changes at country scales. However, recent attempts to map baseline forest cover and deforestation in Nigeria has been based on global scale remote sensing techniques which do not confirm with ground based observations at country level. The aim of this study is two-fold: firstly, baseline forest cover was estimated using an ‘adaptive’ remote sensing model that classified forest cover with high accuracies at country level for the savanna and rainforest zones. The first part of this study also compared the potentials of different MODIS data in detecting forest cover changes at regional (cluster level) scale. The second part of this study explores the trends and response of forest phenology to rainfall across four forest clusters from 2002 to 2012 using vegetation index data from the MODIS and rainfall data obtained from the TRMM.
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Souza, César Salgado Vieira de. "Classify-normalize-classify : a novel data-driven framework for classifying forest pixels in remote sensing images." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/158390.
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O monitoramento do meio ambiente e suas mudanças requer a análise de uma grade quantidade de imagens muitas vezes coletadas por satélites. No entanto, variações nos sinais devido a mudanças nas condições atmosféricas frequentemente resultam num deslocamento da distribuição dos dados para diferentes locais e datas. Isso torna difícil a distinção dentre as várias classes de uma base de dados construída a partir de várias imagens. Neste trabalho introduzimos uma nova abordagem de classificação supervisionada, chamada Classifica-Normaliza-Classifica (CNC), para amenizar o problema de deslocamento dos dados. A proposta é implementada usando dois classificadores. O primeiro é treinado em imagens não normalizadas de refletância de topo de atmosfera para distinguir dentre pixels de uma classe de interesse (CDI) e pixels de outras categorias (e.g. floresta versus não-floresta). Dada uma nova imagem de teste, o primeiro classificador gera uma segmentação das regiões da CDI e então um vetor mediano é calculado para os valores espectrais dessas áreas. Então, esse vetor é subtraído de cada pixel da imagem e portanto fixa a distribuição de dados de diferentes imagens num mesmo referencial. Finalmente, o segundo classificador, que é treinado para minimizar o erro de classificação em imagens já centralizadas pela mediana, é aplicado na imagem de teste normalizada no segundo passo para produzir a segmentação binária final. A metodologia proposta foi testada para detectar desflorestamento em pares de imagens co-registradas da Landsat 8 OLI sobre a floresta Amazônica. Experimentos usando imagens multiespectrais de refletância de topo de atmosfera mostraram que a CNC obteve maior acurácia na detecção de desflorestamento do que classificadores aplicados em imagens de refletância de superfície fornecidas pelo United States Geological Survey. As acurácias do método proposto também se mostraram superiores às obtidas pelas máscaras de desflorestamento do programa PRODES.<br>Monitoring natural environments and their changes over time requires the analysis of a large amount of image data, often collected by orbital remote sensing platforms. However, variations in the observed signals due to changing atmospheric conditions often result in a data distribution shift for different dates and locations making it difficult to discriminate between various classes in a dataset built from several images. This work introduces a novel supervised classification framework, called Classify-Normalize-Classify (CNC), to alleviate this data shift issue. The proposed scheme uses a two classifier approach. The first classifier is trained on non-normalized top-of-the-atmosphere reflectance samples to discriminate between pixels belonging to a class of interest (COI) and pixels from other categories (e.g. forest vs. non-forest). At test time, the estimated COI’s multivariate median signal, derived from the first classifier segmentation, is subtracted from the image and thus anchoring the data distribution from different images to the same reference. Then, a second classifier, pre-trained to minimize the classification error on COI median centered samples, is applied to the median-normalized test image to produce the final binary segmentation. The proposed methodology was tested to detect deforestation using bitemporal Landsat 8 OLI images over the Amazon rainforest. Experiments using top-of-the-atmosphere multispectral reflectance images showed that the deforestation was mapped by the CNC framework more accurately as compared to running a single classifier on surface reflectance images provided by the United States Geological Survey (USGS). Accuracies from the proposed framework also compared favorably with the benchmark masks of the PRODES program.
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Soda, Miho. "Using remote sensing to detect forest change associated with timber processing mills in West Virginia." Morgantown, W. Va. : [West Virginia University Libraries], 2003. http://etd.wvu.edu/templates/showETD.cfm?recnum=2976.
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Thesis (M.A.)--West Virginia University, 2003.<br>Title from document title page. Document formatted into pages; contains vi, 18 p. : ill. (some col.), col. map. Includes abstract. Includes bibliographical references (p. 18).
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Baldauf, Thomas [Verfasser], and Michael [Akademischer Betreuer] Köhl. "Monitoring Reduced Emissions from Deforestation and Forest Degradation (REDD+) : Capabilities of High- Resolution Active Remote Sensing / Thomas Baldauf. Betreuer: Michael Köhl." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2013. http://d-nb.info/1036729591/34.
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Moriarty, Kaleen S. "Automated image-to-image rectification for use in change detection analysis as applied to forest clearcut mapping /." Online version of thesis, 1993. http://hdl.handle.net/1850/11738.
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Blessing, Sithole Vhusomuzi. "A multiscale remote sensing assessment of subtropical indigenous forests along the wild coast, South Africa." Thesis, Nelson Mandela Metropolitan University, 2015. http://hdl.handle.net/10948/d1021169.
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The subtropical forests located along South Africa’s Wild Coast region, declared as one of the biodiversity hotspots, provide benefits to the local and national economy. However, there is evidence of increased pressure exerted on the forests by growing population and reduced income from activities not related to forest products. The ability of remote sensing to quantify subtropical forest changes over time, perform species discrimination (using field spectroscopy) and integrating field spectral and multispectral data were all assessed in this study. Investigations were conducted at pixel, leaf and sub-pixel levels. Both per-pixel and sub-pixel classification methods were used for improved forest characterisation. Using SPOT 6 imagery for 2013, the study determined the best classification algorithm for mapping sub-tropical forest and other land cover types to be the maximum likelihood classifier. Maximum likelihood outperformed minimum distance, spectral angle mapper and spectral information divergence algorithms, based on overall accuracy and Kappa coefficient values. Forest change analysis was made based on spectral measurements made at top of the atmosphere (TOC) level. When applied to the 2005 and 2009 SPOT 5 images, subtropical forest changes between 2005-2009 and 2009-2013 were quantified. A temporal analysis of forest cover trends in the periods 2005-2009 and 2009-2013 identified a decreasing trend of -3648.42 and -946.98 ha respectively, which translated to 7.81 percent and 2.20 percent decrease. Although there is evidence of a trend towards decreased rates of forest loss, more conservation efforts are required to protect the Wild Coast ecosystem. Using field spectral measurements data, the hierarchical method (comprising One-way ANOVA with Bonferroni correction, Classification and Regression Trees (CART) and Jeffries Matusita method) successfully selected optimal wavelengths for species discrimination at leaf level. Only 17 out of 2150 wavelengths were identified, thereby reducing the complexities related to data dimensionality. The optimal 17 wavelength bands were noted in the visible (438, 442, 512 and 695 nm), near infrared (724, 729, 750, 758, 856, 936, 1179, 1507 and 1673 nm) and mid-infrared (2220, 2465, 2469 and 2482 nm) portions of the electromagnetic spectrum. The Jeffries-Matusita (JM) distance method confirmed the separability of the selected wavelength bands. Using these 17 wavelengths, linear discriminant analysis (LDA) classified subtropical species at leaf level more accurately than partial least squares discriminant analysis (PLSDA) and random forest (RF). In addition, the study integrated field-collected canopy spectral and multispectral data to discriminate proportions of semi-deciduous and evergreen subtropical forests at sub-pixel level. By using the 2013 land cover (using MLC) to mask non-forested portions before sub-pixel classification (using MTMF), the proportional maps were a product of two classifiers. The proportional maps show higher proportions of evergreen forests along the coast while semi-deciduous subtropical forest species were mainly on inland parts of the Wild Coast. These maps had high accuracy, thereby proving the ability of an integration of field spectral and multispectral data in mapping semi-deciduous and evergreen forest species. Overall, the study has demonstrated the importance of the MLC and LDA and served to integrate field spectral and multispectral data in subtropical forest characterisation at both leaf and top-of-atmosphere levels. The success of both the MLC and LDA further highlighted how essential parametric classifiers are in remote sensing forestry applications. Main subtropical characteristics highlighted in this study were species discrimination at leaf level, quantifying forest change at pixel level and discriminating semi-deciduous and evergreen forests at sub-pixel level.
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Johnson, Ryan L., and University of Lethbridge Faculty of Arts and Science. "Airborne remote sensing of forest leaf area index in mountainous terrain." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2000, 2000. http://hdl.handle.net/10133/90.
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Leaf area index (LAI) provides forestry information that is important for regional scale ecological models and in studies of global change. This research examines the effects of mountainous terrain on the radiometric properties of multispectral CASI imagery in estimating ground-based optical measurements of LAI, obtained using the TRAC and LAI- 2000 systems. Field and image data were acquired summer 1998 in Kananaskis, Alberta, Canada. To account for the influence of terrain a new modified approach using the Li and Strahler Geometric Optical Mutual Shadowing (GOMS) model in 'multiple forward mode' (MFM) was developed. This new methodology was evaluated against four traditional radiometric corrections used in comination with spectral mixture analysis (SMA) and NDVI. The MFM approach provided the best overall predictions of LAI measured with ground-based optical instruments, followed by terrain normalized SMA, SMA without terrain normalization and NDVI.<br>xiv, 151 leaves : ill. (some col.), map ; 29 cm.
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Soenen, Scott, and University of Lethbridge Faculty of Arts and Science. "Remote sensing of montane forest structure and biomass : a canopy relectance model inversion approach." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2006, 2006. http://hdl.handle.net/10133/281.
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The multiple-forward-mode (MFM) inversion procedure is a set of methods for indirect canopy relectance model inversion using look-up tables (LUT). This thesis refines the MFM technique with regard to: 1) model parameterization for the MFM canopy reflectance model executions and 2) methods for limiting or describing multiple solutions. Forest stand structure estimates from the inversion were evaluated using 40 field validation sites in the Canadian Rocky Mountains. Estimates of horizontal and vertical crown radius were within 0.5m and 0.9m RMSE for both conifer and deciduous species. Density estimates were within 590 stems/ha RMSE for conifer and 310 stems/ha RMSE for deciduous. The most effective inversion method used a variable spectral domain with constrained, fine increment LUTs. A biomass estimation method was also developed using empirical relationships with crown area. Biomass density estimates using the MFM method were similar to estimates produced using other multispectral analysis methods (RMSE=50t/ha).<br>xvi, 156 leaves : ill. (some col.), maps ; 29 cm.
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Pilger, Neal, and University of Lethbridge Faculty of Arts and Science. "Canopy reflectance modeling of forest stand volume." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2004, 2004. http://hdl.handle.net/10133/230.
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Three-dimensional canopy relectance models provide a physical-structural basis to satellite image analysis, representing a potentially more robust, objective and accurate approach for obtaining forest cover type and structural information with minimal ground truth data. The Geometric Optical Mutual Shadowing (GOMS) canopy relectance model was run in multiple-forward-mode (MFM) using digital multispectral IKONOS satellite imagery to estimate tree height and stand volume over 100m2 homogeneous forest plots in mountainous terrain, Kananaskis, Alberta. Height was computed within 2.7m for trembling aspen and 1.8m fr lodgepole pine, with basal area estimated within 0.05m2. Stand volume, estimated as the product of mean tree height and basal area, had an absolute mean difference from field measurements of 0.85m3/100m2 and 0.61m3/100m2 for aspen and pine, respectively.<br>xiii, 143 leaves : ill. (some col.) ; 29 cm.
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Grift, Jeroen. "Forest Change Mapping in Southwestern Madagascar using Landsat-5 TM Imagery, 1990 –2010." Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-22606.
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The main goal of this study was to map and measure forest change in the southwestern part of Madagascar near the city of Toliara in the period 1990-2010. Recent studies show that forest change in Madagascar on a regional scale does not only deal with forest loss, but also with forest growth However, it is unclear how the study area is dealing with these patterns. In order to select the right classification method, pixel-based classification was compared with object-based classification. The results of this study shows that the object-based classification method was the most suitable method for this landscape. However, the pixel-based approaches also resulted in accurate results. Furthermore, the study shows that in the period 1990–2010, 42% of the forest cover disappeared and was converted into bare soil and savannahs. Next to the change in forest, stable forest regions were fragmented. This has negative effects on the amount of suitable habitats for Malagasy fauna. Finally, the scaling structure in landscape patches was investigated. The study shows that the patch size distribution has long-tail properties and that these properties do not change in periods of deforestation.
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Bender, John Richard. "Identifying structural differences in mixed mesophytic and northern hardwood forests on the Monongahela National Forest using remote sensing data." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=976.
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Thesis (M.A.)--West Virginia University, 1999.<br>Title from document title page. Document formatted into pages; contains viii, 55 p. : ill. (some col.), maps. Includes abstract. Includes bibliographical references (p. 44-48).
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Phyu, Phyu Lwin. "Land-use changes caused by livelihood transitions and their impact on tropical lower montane forest in Shan State, Myanmar." Kyoto University, 2018. http://hdl.handle.net/2433/231019.
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Trisasongko, Bambang Physical Environmental & Mathematical Sciences Australian Defence Force Academy UNSW. "Monitoring a mine-influenced environment in Indonesia through radar polarimetry." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/39747.
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Although remotely sensed data have been employed to assess various environmental problems, relatively few previous studies have focused on the impacts of mining. In Indonesia, mining activities have increasingly become one of major drivers of land cover change. The majority of remote sensing research projects on mining environments have exploited optical data which are frequently complicated by tmospheric disturbance, especially in tropical territories. Active remote sensors such as Synthetic Aperture Radar (SAR) are invaluable in this case. Monitoring by Independent SAR data has been limited due to single polarisation. Dual-polarised data have been employed considerably, although for some forestry applications the data were found insufficient to retrieve basic information. This Masters thesis is devoted to assess fully polarimetric SAR data for environmental monitoring of the tailings deposition zone of the PT Freeport Indonesia Grasberg mine in Papua, Indonesia. The main data were two granules of the AIRSAR datasets acquired during the PACRIM-II campaign. To support the interpretation and analysis, a scene of Landsat ETM February 2001) was used, juxtaposed with classified aerial photographs and a series of SPOT VEGETATION images. Both backscattering information and complex coherence matrices, as common representations of polarimetric data, were studied. Primary applications of this research were on degraded forest and environmental rehabilitation. Most parts of Indonesian forests have experienced abrupt changes as an impact of clear-cut deforestation. Gradual changes such as those due to fire or flooded tailings, however, are least studied. It was shown that the Cloude-Pottier polarimetric decomposition provided a convenient way to interpret various stages of forest disturbance. The result suggested that the Entropy parameter of the Cloude-Pottier decomposition could be used as a disturbance indicator. Using the fully polarimetric dataset combined with Support Vector Machine learning, the outcomes were generally acceptable. It was possible to improve classification accuracy by incorporating decomposition parameters, although it seemed insignificant. Land rehabilitation on tailings deposits has been a central concern of the government and the mining operator. Indigenous plant pioneers such as reeds (Phragmites) can naturally grow on dry tailings where soil structure is fairly well developed. To assist such efforts, a part of this research involved identification of dry tailings. On the first assessment, interpretation of surface scatterers was aided by polarimetric signatures. Apparently, longer wavelengths such as L- and P-band were overpenetrated; hence, growing reeds on dry tailings were less detectable. In this case, the use of C-band data was found fairly robust. Employing Mahalanobis statistics, the combination of HH and VV performed well on classification, having similar accuracy with quad polarimetric data. Extension on previous results was made through the Freeman-Durden decomposition. Interpretation using a three-component image of odd, even bounce and volume scattering showed that dry and wet tailings could be well distinguished. The application was benefited from unique responses of dielectric materials in the tailings deposit on SAR signals; hence it is possible to discriminate tailings with different moisture levels. However, further assessment of tailings moisture was not possible due to security reasons and access limitations at the study site. Fully polarimetric data were also employed to support rehabilitation of stressed mangrove forest on the southern coast. In this case, the Cloude-Pottier decomposition was employed along with textural parameters. Inclusion of textural properties was found invaluable for the classification using various statistical trees, and more important than decomposition parameters. It was concluded that incorporating polarimetric decompositions and textural parameters into coherence matrix leads to profound accuracy.
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Bourgoin, Clément. "A framework for evaluating forest ecological vulnerability in tropical deforestation fronts from the assessment of forest degradation in a landscape approach : Case studies from Brazil and Vietnam." Electronic Thesis or Diss., Paris, Institut agronomique, vétérinaire et forestier de France, 2019. http://www.theses.fr/2019IAVF0027.
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La conservation du couvert forestier tropical est essentielle pour assurer la fourniture durable de services écosystémiques. Dans les paysages anthropisés, la conservation des forêts doit également être conciliée avec la productivité agricole. Toutefois, l'accroissement de la démographie, la demande de produits agricoles et les changements dans l'utilisation des terres affectent la durabilité des forêts. Une première étape pour adapter la gestion efficace des forêts par les décideurs locaux consiste à identifier les forêts les plus vulnérables et à caractériser ce qui la génère. L'objectif de cette thèse est de développer une approche multidimensionnelle utilisant la télédétection pour évaluer la dégradation des forêts et les relations avec la dynamique de l'utilisation des terres afin d’estimer la vulnérabilité écologique des forêts. La thèse a été appliquée à Paragominas (Brésil) et Di Linh (Vietnam), où la déforestation à grande échelle due à l'agriculture commerciale a façonné le paysage en mosaïques d'utilisation des terres. A Paragominas, la dégradation est liée à l'accumulation de l’exploitation sélective du bois et au feu impliquant des changements dans la structure forestière. Nous avons estimé le potentiel de la télédétection multisource pour cartographier la biomasse forestière aérienne (AGB) à partir de données de stock de carbone. Nous avons amélioré la précision de la cartographie de l'AGB par rapport aux données pantropicales et révélé que 87 % des forêts étaient dégradées. À une plus petite échelle, nous avons étudié les conséquences de 33 ans de dégradation sur les structures forestières à l'aide de drone. Nous avons constaté que les textures de la canopée capturaient le grain, l'hétérogénéité et les gradients d'ouverture de la canopée, corrélés à la variabilité de la structure forestière et pouvaient être utilisés comme indicateurs pour caractériser les forêts dégradées. Nous avons également évalué le potentiel des images satellites à très haute résolution pour cartographier les structures des forêts dégradées à l'échelle de la municipalité. En nous basant sur des facteurs environnementaux, géographiques et de structure du paysage dérivés de la classification de l'utilisation des terres, nous avons démontré que 80 % de la dégradation des forêts était principalement due à l'accessibilité, la géomorphologie, la fréquence des incendies et à la fragmentation. Les facteurs de dégradation sont interconnectés et agissent en séquence au sein de différentes cascades d'effets. L'évolution de la structure du paysage a permis de reconstituer des trajectoires informant sur la dynamique des frontières agricoles. La combinaison de l'état actuel des forêts, de la dynamique du paysage et de la distribution des facteurs de dégradation permettra d’évaluation la vulnérabilité. A Di Linh, la dégradation concerne principalement les lisières forestières et est due à l'empiètement de l'agriculture (café). L'inventaire sur le terrain des différents types de forêts et d'autres éléments, combiné aux images Sentinel-2, a permis de cartographier avec une grande précision la couverture terrestre actuelle. Nous avons cartographié l'évolution de la couverture terrestre sur 45 ans à l'aide de séries chronologiques Landsat. Nous avons construit des trajectoires de dynamique paysagère afin de caractériser l'expansion de la frontière agricole et mis en évidence l'empiétement agricole sur les zones forestières. Nous avons également identifié des trajectoires de dégradation et de fragmentation qui affectent le couvert forestier à différentes intensités. Ensemble, ces indicateurs ont mis en évidence des points chauds de vulnérabilité. Grâce aux approches et aux indicateurs élaborés à multiples échelles, nous avons fourni un diagnostic holistique des forêts dans les paysages anthropisés, englobant l'état des forêts et des dynamiques à plus larges échelles. Cette thèse vise à orienter une gestion adaptée des forêts dégradées à l'échelle du paysage<br>The conservation of tropical forest cover is a key to ensuring sustainable provision of multiple ecosystem services. In human-modified landscapes, forest conservation must also be reconciled with agricultural productivity. However, increasing demography, demand for agricultural products and changes in land uses are affecting forest sustainability through degradation processes. A first step to tailor effective forest management by local decision makers is to identify most vulnerable forests and to characterize what is driving this vulnerability. The objective of this thesis is to develop a multidimensional approach using remote sensing to assess forest degradation and the relations with the broader dynamics of land use/cover towards the evaluation of forest ecological vulnerability. The thesis was applied in old-deforestation fronts of Paragominas (Brazil) and Di Linh (Vietnam) where large-scale deforestation driven by commercial agriculture shaped the landscape into land use mosaics with increasing degradation pressures. In Paragominas, degradation is linked with long-term accumulation of selective logging and fire implying changes in forest structure. We estimated the potential of multisource remote sensing to map forest aboveground biomass (AGB) from large-scale field assessment of carbon stock. We improved the accuracy of AGB mapping compared to pantropical datasets and revealed that 87% of forest was degraded. At a lower scale, we investigated the consequences of 33 years of degradation history from Landsat on forest structures using Unmanned Aerial Vehicle. We found that canopy textures captured canopy grain, heterogeneity and openness gradients, correlated with forest structure variability and could be used as proxies to characterize degraded forests. We also assessed the potential of very high resolution satellite images and derived canopy textures to upscale texture-structure relations at the municipality scale. Based on environmental, geographical factors and landscape structure metrics derived from land use/cover classification, we demonstrated that 80% of forest degradation was mainly driven by accessibility, geomorphology, fire occurrence and fragmentation. The drivers of degradation acted together and in sequence and clustering analysis disentangled different cascades of effects. Changes in landscape structure allowed reconstructing trajectories informing on agricultural frontier dynamics. The combination of current forest state, landscape dynamics and distribution of degradation drivers would be at the basis of ecological vulnerability assessment. In Di Linh, degradation mostly concerns forest edges and is driven by encroachment of coffee-based agriculture. Field inventory of the different forest types and other landscape elements combined with Sentinel-2 images allowed to map with high precision the current land cover. We then mapped land cover changes over 45 years using Landsat time series. We constructed trajectories of landscape structure dynamics from which we characterized the expansion of the agricultural frontier and highlighted heterogeneous agricultural encroachment on forested areas. We also identified degradation and fragmentation trajectories that affect forest cover at different rates and intensity. Combined, these indicators pinpointed hotspots of forest ecological vulnerability. Most vulnerable forest areas were experiencing rapid and recent forest cover loss associated with landscape fragmentation, land use competition due to coffee production and degradation. Through the developed remote sensing approaches and indicators at forest and landscape scales, we provided a holistic diagnosis of forests in human-modified landscapes encompassing forest state and broader dynamics and drivers. This thesis aims to pave the way for tailored and prioritized management of degraded forests at the landscape scale
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Ashworth, Andrew Lee. "Predicting Southeastern forest canopy heights and fire fuel models using Geoscience Laser Altimeter System data." Master's thesis, Mississippi State : Mississippi State University, 2008. http://library.msstate.edu/etd/show.asp?etd=etd-05102008-141659.
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Ndlovu, Nomzamo Bonisiwe. "Quantifying indigenous forest change in Dukuduku from 1960 to 2008 using GIS and remote sensing techniques to support sustainable forest management planning." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85622.
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Thesis (MSc)--Stellenbosch University, 2013.<br>ENGLISH ABSTRACT: This study aimed to understand how Dukuduku Forest in Kwa-Zulu Natal has changed from 1960
to 2008 and whether the change in political regimes, during and post apartheid eras might have
contributed to changes in forest extent.
To achieve the aims, the following analyses were made:
- Qualitative and quantitative spatial analyses of forest change;
- Analyses of the correspondence of change with political changes in the country;
- Assessment of perception of people living in the Dukuduku forest area.
The Dukuduku land cover was mapped from aerial photos using ArcGIS 9.3 to determine whether
or not there has been a significant change in the area from 1960-2008, in response to resource use
pressures and to come up with the strategic sustainable management plan from the results found.
Five aerial photographs were used to determine the changes in land cover from the year: 1960,
1970, 1992, 2005 and 2008. The Land cover types were classified into four classes, Indigenous
Forests, Plantation Forests, Water Bodies and Other (open areas, cultivated land, and all the human
disturbed and transformed land). The percentage of cover per class was compared across the years
to determine overall change in land cover and the rate of change per year was also calculated.
The results from the study showed that:
- Natural Forest increased by 11% (700 ha), at the rate of 20.56 hectares per year between 1960
and 1992, which is the apartheid era. Between 1992 and 2008, the democratic era, the forest
decreased by 34.4% (2472.31ha), at the rate of 168 hectares per year.
- The Dukuduku forest community gains resources (timber and grass for construction, art,
firewood, medicinal plants, grazing of livestock and food) from the forest. The people are
willing to contribute in protecting the forest only if the governing authorities would include them in decisions made, as the NFA demands Participatory Forest Management, but which
does not currently exist in Dukuduku.<br>AFRIKAANSE OPSOMMING: Hierdie studie ondersoek die verandering van die Dukuduku woud in Kwa-Zulu Natal vanaf
1960 tot 2008, en vernaamlik of die verandering in politieke regimes tydens en in die postapartheid
eras tot verandering bygedra het in die woud se vorm.
Om hierdie doelwitte te breik is die volgende analises gedoen:
- Kwalitatiewe en kwantitatiewe ruimtelike analises van woudverandering;
- Analises van die korrelasie tussen hierdie fisiese omgewingsverandering en politieke
verandering in die land;
- Analise van die persepsie van mense wat in die Dukuduku woudgebied woon.
Die Dukuduku gronddekking is gekarteer met behulp van lugfotos, waarvoor ArcGIS 9.3
gebruik is om te bepaal of daar noemenswaardige verandering in die gebied plaasgevind het
van 1960 tot 2008, in reaksie op hulpbrongebruike, en om ‘n volhoubare bestuursplan gestel
voor wat op die bevindinge gebaseer is. Vyf lugfotos is gebruik om verandering in
gronddekking te bepaal vir die jare: 1960, 1970, 1992, 2005 en 2008. Die Gronddekking tipes
is geklassifiseer in vier klasse naamlik Inheemse Woude, Plantasiebosse, Waterliggame en
Ander (oop gebiede, landerye en al die mens-versteurde en getransfomeerde gebiede). Die
persentasie van elke dekkingsklas is oor die jare vergelyk om die verandering in algehele
grond-dekking te bepaal, en die tempo van verandering is ook bepaal, asook die tempo van
verandering.
Die resultate van die studie wys dat: - Die natuurlike woud toegeneem het met 11% (700 ha), teen ‘n tempo van 20.56 hektaar
per jaar tussen 1960 en 1992, tgedurende die apartheidsera. Tussen 1992 en 2008, die
demokratiese era, het die woude verminder met 34.4% (2472.31 ha), teen ‘n tempo van
168 hektaar per jaar.
- Die gemeenskap wat in die Dukuduku woud woon verkry hulpbronne van die woud
(hout en gras vir konstruksie, kuns, brandhout, medisinale plante, weiding vir vee, en
voedsel). Die mense is gewillig om by te dra tot beskerming van die woud indien die
owerhede hulle sou betrek in besluite wat geneem word, veral omdat die nasionale Wet
op Bosse voorsiening maak vir Deelnemende Bosbestuur, wat tans nie by Dukuduku
gebeur nie.
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Davidson, Diedre P., and University of Lethbridge Faculty of Arts and Science. "Sensitivity of ecosystem net primary productivity models to remotely sensed leaf area index in a montane forest environment." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2002, 2002. http://hdl.handle.net/10133/155.
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Net primary productivity (NPP) is a key ecological parameter that is important in estimating carbon stocks in large forested areas. NPP is estimated using models of which leaf area index (LAI) is a key input. This research computes a variety of ground-based and remote sensing LAI estimation approaches and examines the impact of these estimates on modeled NPP. A relative comparison of ground-based LAI estimates from optical and allometric techniques showed that the integrated LAI-2000 and TRAC method was preferred. Spectral mixture analysis (SMA), accounting for subpixel influences on reflectance, outperformed vegetation indices in LAI prediction from remote sensing. LAI was shown to be the most important variable in modeled NPP in the Kananaskis, Alberta region compared to soil water content (SWC) and climate inputs. The variability in LAI and NPP estimates were not proportional, from which a threshold was suggested where first LAI is limiting than water availability.<br>xii, 181 leaves : ill. ; 28 cm.
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Mlotha, McArd Joseph. "Analysis of Land Use/Land Cover Change Impacts Upon Ecosystem Services in Montane Tropical Forest of Rwanda: Forest Carbon Assessment and REDD+ Preparedness." Antioch University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=antioch1527773591460797.
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Chiang, Yang-Sheng. "Estimating landscape level leaf area index and net primary productivity using field measurements, satellite imagery, and a 2-D ecophysiological model." Virtual Press, 2004. http://liblink.bsu.edu/uhtbin/catkey/1294241.
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This study has provided a landscape level estimate of leaf area index (LAI) and net primary productivity (NPP) for a temperate broadleaf forest ecosystem in south-central Indiana. The estimates were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical products LAI and NPP from both spatial and temporal perspectives. The evidence suggests that field-based estimates were poorly correlated with global MODIS data due to the simplifying assumptions of the MODIS global applicability, saturation problems of the red reflectance in highly vegetated areas, homogeneous land cover types of the study area, and other design assumptions of the field-based estimates. To improve the localized applicability of MODIS product algorithms, an empirical and localized algorithm combining in-situ measurements, the buildup of a localized biophysical model, and remote sensing-derived data were suggested for each local-scaled ecosystem.<br>Department of Natural Resources and Environmental Management
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Fotis, Alexander T. "Exploring canopy structure and function as a potential mechanism of sustained carbon sequestration in aging forests." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503231521023889.
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Junior, Edgard Marino. "Análise integrada dos efeitos do uso da terra em fragmentos florestais da bacia do rio Corumbataí, SP." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/91/91131/tde-16032007-165103/.
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A degradação ou conservação das florestas tropicais dependem, em grande parte, da ação humana no meio ambiente. O presente estudo tem como objetivo geral avaliar de forma integrada os efeitos do uso da terra em fragmentos florestais na bacia do rio Corumbataí. Destaca-se, neste estudo, a importância do uso da interdisciplinaridade envolvendo, no caso, conhecimentos das Ciências Agrárias, Humanas e Biológicas. Sob tal perspectiva, este estudo pode ser considerado uma somatória de esforços visando à compreensão dos fatores de degradação ou conservação dos recursos naturais renováveis na bacia do rio Corumbataí. Os métodos utilizados envolvem o sensoriamento remoto (por meio da classificação de imagens de satélite) e sistemas de informações geográficas (SIG), para coleta e organização de informações sobre a cobertura do solo, bem como dados primários, para coleta e organização de dados do uso da terra. Os dados primários foram obtidos a partir do Levantamento Cadastral das Unidades de Produção Agropecuária ? LUPA, realizado pela Coordenadoria de Assistência Técnica Integral ? CATI. Para este estudo, as coberturas do solo estão relacionadas com a ocupação da superfície do solo, tais como água, floresta e plantação. O uso da terra está relacionado com os fatores socioeconômicos e tecnológicos, que podem determinar a conservação ou a degradação dos fragmentos florestais. A análise dos resultados está baseada na correlação entre uma série de variáveis que representam a variação ou não na conservação dos fragmentos florestais e os indicadores do uso da terra ocorridos no período de estudo. Os resultados encontrados demonstram existir diferenças regionais na bacia do Corumbataí em termos de: nível educacional do produtor rural, mão-de-obra utilizada nas UPAs, condição socioeconômica do produtor rural e, ainda, tecnologias utilizada nas UPAs. Análises realizadas evidenciaram que o emprego da tecnologia agropecuária nas UPAs contribui para a conservação dos fragmentos florestais na bacia do Corumbataí.<br>The degradation or preservation of the tropical forest depends mostly of human action on the environment. The purpose of this study was to investigate the land use effects on forest fragments of the Corumbataí river basin, SP, by using integrated analysis. The present study emphasizes an interdisciplinary approach involving human, agrarian end biological sciences. In this way, the present study intends to understand the forces driving degradation or preservation of the natural resources of the studied area. The methods involve remote sensing (satellite images) and geographic information systems for collecting and organizing information related to land cover. In addition, land use data were also collected by using primary data and surveys. In this study, land cover is related to forest and plantations while land use is related to socioeconomic and technological factors that can lead to deforestation or conservation of the forest fragments. The analysis were based on the relationship among variables representing changes on the areas of the forest fragments and land use indicators from the period covered by this study. The findings revealed regional differences in the Corumbataí river basin in terms of: farmer?s educational level, rural worker employment, farmer?s socioeconomic status as well as farm technology. The results indicated that forest conservation in the Corumbataí river basin is mostly related to the use of technology in the farms.
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Mananze, Sosdito Estevão. "Análise da dinâmica de alteração do coberto florestal na Reserva Florestal de Mecurubi - Moçambique." Master's thesis, Universidade de Évora, 2012. http://hdl.handle.net/10174/15394.
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A desflorestação nos países em desenvolvimento contribui com 20 a 25% das emissões globais de dióxido de carbono. Em 2006 foi lançado o mecanismo para a Redução de Emissões por Desflorestação e Degradação Florestal, o qual preconiza o estabelecimento de parcerias entre países desenvolvidos e em desenvolvimento para a redução da desflorestação. O presente estudo pretende contribuir para a avaliação da elegibilidade da reserva florestal de Mecuburi como área de intervenção nesse mecanismo. Para cartografar as alterações do coberto/uso do solo e determinar as taxas histórica de desflorestação, classificaram-se imagens de satélite de três datas na década de 2000 (2002, 2007 e 2011). Posteriormente, através da aplicação do modelo GEOMOD, produziu-se uma previsão da localização da desflorestação para o ano 2020. O rigor estimado para a classificação das imagens de satélite foi superior a 95% para todas as datas, contudo, não foi possível realizar uma validação formal da classificação devido à falta de dados de campo. Na totalidade da reserva verificou-se um aumento da floresta, todavia, a área cartografada como floresta em 2002 sofreu uma redução significativa durante o período em análise a uma taxa bruta de desflorestação de 2165 ha/ano. A desflorestação projectada para 2020 incide na zona norte da reserva. Um projecto REDD na reserva contribuiria para reduzir a desflorestação; ABSTRACT: Deforestation in developing countries accounts for 20 to 25% of the global carbon emissions. Since 2006 a mechanism for reduction of emissions from deforestation and forest degradation is under discussion at the UNFCCC. The aim is to promote the partnership between developed and developing countries in order to reduce deforestation. This study intends to contribute to the assessing the eligibility of the Mecuburi forest reserve for REDD intervention. Remote sensing was used to map the land cover/use changes between three dates, 2002, 2007 and 2011. Gross and net deforestation rates were calculated and the location of deforestation in 2020 was projected using GEOMOD. The classification algorithm yielded an overall accuracy above 95% for the three images however, no field data was available to formally validate the classification. There was an overall increase of forest area during the analyzed time period. However, the benchmark forest area (2002) was reduced at a gross rate of 2165 ha/year. Most of the projected deforestation is located to the north of the reserve. A REDD project could contribute to reduce deforestation within the reserve.
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Clarkson, Matthew Thomas. "An evaluation of a 3D sampling technique and LiDAR for the determination of understory vegetation density levels in pine plantations." Master's thesis, Mississippi State : Mississippi State University, 2007. http://library.msstate.edu/etd/show.asp?etd=etd-03122007-192344.
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Kamwi, Jonathan Mutau. "The use of high-resolution satellite imagery in forest inventory : a case of Hans Kanyinga Community Forest - Namibia." Thesis, Link to the online version, 2007. http://hdl.handle.net/10019/650.
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Kabulu, Djibu Jean-Pierre. "Impacts des activités minières sur les écosystèmes forestiers au Katanga, République Démocratique du Congo." Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209822.
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En dépit de ses ressources naturelles, le Katanga connaît une déforestation inquiétante durant ces dernières années. Avec 157.525 km², la forêt au Katanga ne recouvre plus que moins de 4% environ du territoire. Les forêts sont considérablement en régression, soit un taux de déforestation annuelle d’environ -0,20%. Ce processus s’est récemment accéléré, tout particulièrement dans les zones minières et dans les hinterlands de tous les grands centres urbains, suite à une forte pression démographique et aux activités minières qui en résultent. Le paysage forestier est donc dynamique et change, en composition et en configuration spatiale au cours du temps. L’objectif principal de cette étude était d’évaluer l’état de la fragmentation de la forêt au Katanga, en utilisant les techniques et méthodes de l‘écologie du paysage. Un accent particulier a été mis sur l’impact des activités minières sur les paysages forestiers et sur l’exploitation artisanale de bois énergie. Cette quantification de l’état de la fragmentation peut être utile d’une part aux services publics de l'État congolais pour évaluer de manière différenciée les processus de déforestation et la vulnérabilité du paysage, et d’en tirer les conséquences opérationnelles. D’autre part elle est utile aux services de recherche et de conservation pour prendre en considération les facteurs de risque de la déforestation au côté des autres (vent, pluie, érosion, crues, ruissellement, etc.). Dans la présente étude, nous nous sommes basés sur l’hypothèse selon laquelle les actions anthropiques sont les principales causes de la fragmentation de l’habitat. <p><p>Les résultats ont montré que l’exploitation minière et la croissance démographique sont deux facteurs de la déforestation, considérés comme les paramètres principaux du processus de changement du paysage au Katanga. L’analyse spatiale, faite grâce à la cartographie et aux systèmes d’information géographique, a permis de faire le calcul d’indices de composition et de configuration du paysage afin d’analyser la structure spatiale des forêts. La structure a été ensuite modélisée pour évaluer les impacts de concessions, des routes et des sites miniers sur la forêt. En fonction de résultats obtenus, on constate que les forêts du Katanga subissent une pression anthropique assez forte et la tendance générale de la fragmentation des forêts est inquiétante. Les habitats forestiers sont entrain d’être transformés en formations savanicoles. Les activités minières ont un impact défavorable sur les forêts de la province. La présence des plusieurs compagnies minières a favorisé l'augmentation des sites miniers et la densification du réseau routier. <p><br>Doctorat en Sciences<br>info:eu-repo/semantics/nonPublished
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Portillo, Carlos. "Assessing the Conservation status of Neotropical Dry forests using Geographic Information Systems and Optical Remote Sensing." Phd thesis, 2010. http://hdl.handle.net/10048/1083.
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Planet Earth is undergoing a rapid rate of ecosystem conversion and degradation and one of the major challenges of current environmental science is to contribute to the management and conservation of biodiversity through the development of tools for assessing environmental change. The main goal of this doctoral dissertation is to contribute to the scientific literature on remote sensing tools for monitoring tropical dry forests, which is one of most important global change frontiers. This thesis is composed of five chapters which have the goals of covering the following specific goals: 1) To estimate the extent and geographic distribution of the neotropical dry forest. 2) To evaluate the potential use of satellite-detected fires as deforestation predictors in tropical dry forest and 3) To evaluate the potential of remote sensing techniques to detect edge effects in tropical dry forest. Before assessing the main goals of the thesis, in chapter two, Integrating Remote Sensing and Biodiversity research, we stress out the necessity of integrated assessments using multiple spatial and spectral resolution sensors over a wide array of ecosystems in order to find relevant ecosystem properties that would be sensitive to species richness. Chapter three, Extent and Conservation of tropical dry forests in the Americas, describes a regional scale mapping effort using coarse-scale imagery (MODIS 500-m) of the extent and geographical distribution of tropical dry forests that introduces several innovations to previous assessments. Based on these techniques, the total current extent of tropical dry forest in the Americas is 519,597 Km2. I also found that 66% of the ecosystem has been already converted to other land uses while only 4.5 % of is under protected areas. Chapter four, MODIS Active fires and deforestation in tropical dry forest landscapes, we show correlations patterns between the number of MODIS Active Fires and forest cover change in four tropical dry forest landscapes in Latin America. At the Santa Cruz site (Bolivia), correlations were strong and significant while at Chamela Site (Mexico) and the Mata Seca site (Brazil) correlations were moderate but significant as well. In the Machango site (Venezuela), active fires showed no correlation to deforestation events. In general, our findings show that fires detected by the MODIS sensor may be used as predictors of deforestation in tropical dry forest ecosystems. Chapter five, Edge influence on canopy openness and understory microclimate in two Neotropical dry forest fragments, addresses one of the most characteristic features of fragmented tropical forests: the increase in disturbance near the edges of the fragment or what is known as edge effects. Results in gap fraction and Fraction of Intercepted Photosynthetically Active Radiation (FiPAR) show that edge influence at tropical dry forest sites extend to at least 300-m. Finally, Chapter Six, Remote sensing of edge effects in dry forest fragments using CHRIS/Proba Imagery, shows an assessment of changes in the fraction of intercepted photosynthetically active radiation (FiPAR) across four edge-to-interior transects in tropical dry forests fragments and their correlation to spectral vegetation indices (SVIs) computed from the hyperspectral and multiangular CHRIS sensor on board the Proba platform. Results show that the use of spectral vegetation indices for identifying and quantifying edge effects in tropical forests have the potential to improve modeling of forest disturbance in fragmented landscapes. The work contained in these five chapters address issues that are critical to the advancement of tropical dry forest monitoring. These studies contribute to the current scientific literature on the use and application of optical remote sensing tools, not only applicable in tropical dry forests, but for tropical forest conservation at the continental, regional and local level.
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Mumbere, Mbasa Ndemo. "Climate change mitigation strategies in relation to the forestry and energy sectors in SACD region with emphasis in DRC and RSA as case studies." Thesis, 2016. http://hdl.handle.net/10500/22640.
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The main objective of this study is to evaluate climate change mitigation strategies in the forestry and energy sectors in the SADC with emphasis on the DRC and the RSA. This study is evaluative and cross-sectional. Its results were got through interviews of 56 key informants using the interview guide, and four focus group discussions in the DRC based on the focus group guide. The non-probability sample, mainly the purposive sample and the snowballing sampling were used. After the data analysis, the following results were found:
In terms of the strategies for fighting drivers of deforestation both in the DRC and in RSA, it was revealed that the DRC focuses more on the REDD+ projects and NGO activities while the RSA protects its small existing natural forests through Acts, laws, advanced research and establishment of commercial plantations.
The results which are related to the contribution of REDD programmes and NGOs to climate change mitigation in the RSA and the DRC have revealed that there are no REDD programmes in the RSA for carbon stock. In the DRC, the NOVACEL REDD+ pilot project has a carbon stock of 60 000 tons which continues to grow with 8 tons of CO2 /ha/year; 210 tons/ha/year on the left side of the Congo River, and on the right side 195 tons/ha/year is generated by the Isangi Geographically Integrated REDD+ pilot project. The WCS Mambasa Forestry REDD+ pilot project has 230 tons/ha/year, while 16 000 tons of CO2/year are stocked under the Luki REDD+ pilot project. The Eco-Makala and Equatorial REDD+ pilot projects have not yet estimated their carbon stocks.
Regarding the involvement of the civil society in activities of climate change mitigation in the DRC, people are more involved in REDD’s alternative activities which are funded by the projects. However, in the RSA, people are used as labour in commercial plantations. The RSA derives its major energy from coal (94%) but the DRC has a high potential in hydropower that can generate up to 100 000 MGW. On the use of remote sensing, both the DRC and the RSA employ remote sensing but the RSA has a Spatial Agency while the DRC does not<br>College of Agriculture and Environmental Sciences<br>D. Litt. et Phil. (Environmental Science)
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Chen, Hao. "An advanced classification system for processing multitemporal landsat imagery and producing Kyoto Protocol products." 2004. http://hdl.handle.net/1828/453.
Full textAbstract:
Canada has 418 million hectares of forests, representing 10% of the forested land in the world [I]. In 1997, Canada signed the Kyoto Protocol and agreed to cut greenhouse gas emissions by six percent below the 1990 level between 2008 and 2012 [2]. This agreement was ratified in December 2002. It requires Canada to report Canada's sustainable forest resources, including information about forest carbon, afforestation, reforestation, and deforestation (ARD). To fulfill this commitment, effective and accurate measuring tools are needed. One of these tools is satellite remote sensing, a cost-effective way to examine large forested areas in Canada for timely forest information. Historically, the study of forest aboveground carbon was carried out with detailed forest inventory and field sampling from temporary and permanent sample plots, which severely limited the forest area that could be studied. For regional and global scales, it is necessary to use remote sensing for aboveground carbon and ARD mapping due to time and financial constraints. Therefore, the purpose of this research is to develop, implement, and evaluate a computing system that uses multitemporal Landsat satellite images [3] to estimate the Kyoto-Protocol-related forest parameters and create geo-referenced maps, showing the spatial distribution of these parameters in a Geographic Information System (GIs). The new computing system consists of a segment-based and supervised classification engine with feature selection functionality and a Kyoto-Protocol-products estimation unit. The inclusion of the feature selection reduces the large dimensionality of the feature space of multitemporal remote Landsat data sets. Thus, more images could be added into the data sets for analysis. The implementation of the segment-based classifiers provides more accurate forest cover classifications for estimating the Kyoto Protocol products than pixel-based classifiers. It is expected that this approach will be a new addition to the current existing methodologies for supporting Canada's reporting commitments on the sustainability of the forest resources in Canada. This approach can also be used by other countries to monitor Canada's compliance with international agreements.
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Tang, Xiaojing. "Near real-time monitoring of forest disturbance: a multi-sensor remote sensing approach and assessment framework." Thesis, 2018. https://hdl.handle.net/2144/27562.
Full textAbstract:
Fast and accurate monitoring of tropical forest disturbance is essential for understanding current patterns of deforestation as well as helping eliminate illegal logging. This dissertation explores the use of data from different satellites for near real-time monitoring of forest disturbance in tropical forests, including: development of new monitoring methods; development of new assessment methods; and assessment of the performance and operational readiness of existing methods.
Current methods for accuracy assessment of remote sensing products do not address the priority of near real-time monitoring of detecting disturbance events as early as possible. I introduce a new assessment framework for near real-time products that focuses on the timing and the minimum detectable size of disturbance events. The new framework reveals the relationship between change detection accuracy and the time needed to identify events.
In regions that are frequently cloudy, near real-time monitoring using data from a single sensor is difficult. This study extends the work by Xin et al. (2013) and develops a new time series method (Fusion2) based on fusion of Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) data. Results of three test sites in the Amazon Basin show that Fusion2 can detect 44.4% of the forest disturbance within 13 clear observations (82 days) after the initial disturbance. The smallest event detected by Fusion2 is 6.5 ha. Also, Fusion2 detects disturbance faster and has less commission error than more conventional methods.
In a comparison of coarse resolution sensors, MODIS Terra and Aqua combined provides faster and more accurate detection of disturbance events than VIIRS (Visible Infrared Imaging Radiometer Suite) and MODIS single sensor data. The performance of near real-time monitoring using VIIRS is slightly worse than MODIS Terra but significantly better than MODIS Aqua.
New monitoring methods developed in this dissertation provide forest protection organizations the capacity to monitor illegal logging events promptly. In the future, combining two Landsat and two Sentinel-2 satellites will provide global coverage at 30 m resolution every 4 days, and routine monitoring may be possible at high resolution. The methods and assessment framework developed in this dissertation are adaptable to newly available datasets.
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Barnett, Jennifer S. "Estimating volume and value on standing timber in hybrid poplar plantations using terrestrial laser scanning : a case study." Thesis, 2012. http://hdl.handle.net/1957/30216.
Full textAbstract:
Terrestrial laser scanning (TLS) may provide a way to increase timber value recovery by replacing manual timber cruising with a simple-to-use, cost-effective alternative. TLS has been studied in several trials worldwide. Past studies have not compared TLS based estimates with mill estimates of stem value and volume.
Three differently stocked stands of hybrid poplar were selected for diameter, stem sinuosity and height measurement using manual cruising and TLS. Selected trees were harvested and transported to a mill where they were scanned and then processed into lumber and chips. Data gathered using both manual and TLS methods were used to obtain stem volume and value estimates to compare with mill estimates.
Results indicated that TLS diameter measurements were more accurately matched to mill and manual measurements up to about 7.5 meters on the stem than above 7.5 meters on the stem in all three stands. Stem curvature comparisons indicated that the variation between TLS and mill centerline measurements was similar to the variation between repeat mill scan measurements of the same stems.
Using TLS as a pre-harvest inventory tool showed that additional revenue could be obtained from the reallocation of saw-log and chip log volume to veneer logs of various sizes in all three stands. It was also shown that the sampling error required to estimate stand value was greater than was required to estimate stand volume within the same error limits.<br>Graduation date: 2012
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Fiorella, Maria R. "Forest and wildlife habitat analysis using remote sensing and geographic information systems /." 1992. http://hdl.handle.net/1957/10275.
Full text
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"Hyperspectral data analysis of typical surface covers in Hong Kong." 1999. http://library.cuhk.edu.hk/record=b5890094.
Full textAbstract:
Ma Fung-yan.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.<br>Includes bibliographical references (leaves 137-141).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Acknowledgements --- p.iv<br>Table of Contents --- p.v<br>List of Tables --- p.ix<br>List of Figures --- p.x<br>Chapter CHAPTER 1 --- INTRODUCTION<br>Chapter 1.1 --- Introduction and background --- p.1<br>Chapter 1.2 --- Objectives --- p.4<br>Chapter 1.3 --- Significance --- p.5<br>Chapter 1.4 --- Organization of the thesis --- p.5<br>Chapter CHAPTER 2 --- LITERATURE REVIEW<br>Chapter 2.1 --- Introduction --- p.7<br>Chapter 2.2 --- Hyperspectral remote sensing --- p.7<br>Chapter 2.2.1 --- Current imaging spectrometers available --- p.8<br>Chapter 2.2.2 --- Applications of hyperspectral remote sensing --- p.9<br>Chapter 2.2.2.1 --- Biochemistry of vegetation --- p.10<br>Chapter 2.2.2.2 --- Spatial and temporal patterns of vegetation --- p.12<br>Chapter 2.3 --- Tree species recognition --- p.12<br>Chapter 2.3.1 --- Factors affecting spectral reflectance of vegetation --- p.14<br>Chapter 2.3.1.1 --- Optical properties of leaf --- p.14<br>Chapter 2.3.1.2 --- Canopy structure --- p.15<br>Chapter 2.3.1.3 --- Canopy cover --- p.16<br>Chapter 2.3.1.4 --- Illumination and viewing geometry --- p.16<br>Chapter 2.3.1.5 --- Spatial and temporal dynamics of plants --- p.17<br>Chapter 2.3.2 --- Classification algorithms for hyperspectral analysis --- p.17<br>Chapter 2.3.2.1 --- Use of derivative spectra for tree species recognition --- p.17<br>Chapter 2.3.2.2 --- Linear discriminant analysis --- p.18<br>Chapter 2.3.2.3 --- Artificial neural network --- p.19<br>Chapter 2.3.3 --- Tree species recognition using hyperspectral data --- p.21<br>Chapter 2.4 --- Data compression and feature extraction --- p.22<br>Chapter 2.4.1 --- Analytical techniques of data compression --- p.23<br>Chapter 2.4.2 --- Analytical techniques of feature extraction --- p.25<br>Chapter 2.4.2.1 --- Feature selection by correlation with biochemical and biophysical data --- p.25<br>Chapter 2.4.2.2 --- Spatial autocorrelation-based feature selection --- p.27<br>Chapter 2.4.2.3 --- Spectral autocorrelation-based feature selection --- p.29<br>Chapter 2.4.2.3.1 --- Optimization with distance metrics --- p.29<br>Chapter 2.4.2.3.2 --- Stepwise linear discriminant analysis --- p.30<br>Chapter 2.5 --- Summary --- p.31<br>Chapter CHAPTER 3 --- METHODOLOGY<br>Chapter 3.1 --- Introduction --- p.33<br>Chapter 3.2 --- Study site --- p.33<br>Chapter 3.3 --- Instrumentation --- p.34<br>Chapter 3.4 --- Data collection --- p.35<br>Chapter 3.4.1 --- Laboratory measurement --- p.36<br>Chapter 3.4.2 --- In situ measurement --- p.39<br>Chapter 3.5 --- Methods of data analysis --- p.40<br>Chapter 3.5.1 --- Preprocessing of data --- p.40<br>Chapter 3.5.2 --- Compilation of hyperspectral database --- p.42<br>Chapter 3.5.3 --- Tree species recognition --- p.42<br>Chapter 3.5.3.1 --- Linear discriminant analysis --- p.44<br>Chapter 3.5.3.2 --- Artificial neural network --- p.44<br>Chapter 3.5.3.3 --- Accuracy assessment --- p.45<br>Chapter 3.5.3.4 --- Comparison of different data processing strategies and classifiers --- p.45<br>Chapter 3.5.3.5 --- Comparison of data among different seasons --- p.46<br>Chapter 3.5.3.6 --- Comparison of laboratory and in situ data --- p.46<br>Chapter 3.5.4 --- Data compression --- p.47<br>Chapter 3.5.5 --- Band selection --- p.47<br>Chapter 3.6 --- Summary --- p.48<br>Chapter CHAPTER 4 --- RESULTS AND DISCUSSIONS OF TREE SPECIES RECOGNITION<br>Chapter 4.1 --- Introduction --- p.50<br>Chapter 4.2 --- Characteristics of hyperspectral data --- p.50<br>Chapter 4.3 --- Tree species recognition --- p.79<br>Chapter 4.3.1 --- Comparison of different classifiers --- p.82<br>Chapter 4.3.1.1 --- Efficiency of the classifiers --- p.83<br>Chapter 4.3.1.2 --- Discussions --- p.83<br>Chapter 4.3.2 --- Comparison of different data processing strategies --- p.84<br>Chapter 4.3.3 --- Comparison of data among different seasons --- p.86<br>Chapter 4.3.4 --- Comparison of laboratory and in situ data --- p.88<br>Chapter 4.4 --- Summary --- p.92<br>Chapter CHAPTER 5 --- RESULTS AND DISCUSSIONS OF DATA COMPRESSION AND BAND SELECTION<br>Chapter 5.1 --- Introduction --- p.93<br>Chapter 5.2 --- Data compression --- p.93<br>Chapter 5.2.1 --- PCA using in situ spectral data --- p.93<br>Chapter 5.2.1.1 --- Characteristics of PC loadings --- p.95<br>Chapter 5.2.1.2 --- Scatter plots of PC scores --- p.96<br>Chapter 5.2.2 --- PCA using laboratory spectral data --- p.99<br>Chapter 5.2.2.1 --- Characteristics of PC loadings --- p.102<br>Chapter 5.2.2.2 --- Scatter plots of PC scores --- p.103<br>Chapter 5.2.2.3 --- Results of tree species recognition using PC scores --- p.107<br>Chapter 5.2.3 --- Implications --- p.107<br>Chapter 5.3 --- Band selection --- p.108<br>Chapter 5.3.1 --- Preliminary band selection using stepwise discriminant analysis --- p.108<br>Chapter 5.3.1.1 --- Selection of spectral bands --- p.109<br>Chapter 5.3.1.2 --- Classification results of the selected bands --- p.109<br>Chapter 5.3.1.3 --- Seasonal comparison using stepwise linear discriminant analysis --- p.114<br>Chapter 5.3.1.4 --- Implications --- p.116<br>Chapter 5.3.2 --- Band selection using hierarchical clustering technique --- p.116<br>Chapter 5.3.2.1 --- Hierarchical clustering procedure --- p.116<br>Chapter 5.3.2.2 --- Selection of spectral band sets --- p.119<br>Chapter 5.3.2.3 --- Classification results of the selected band sets --- p.124<br>Chapter 5.4 --- Summary --- p.127<br>Chapter CHAPTER 6 --- SUMMARY AND CONCLUSION<br>Chapter 6.1 --- Introduction --- p.129<br>Chapter 6.2 --- Summary --- p.129<br>Chapter 6.2.1 --- Tree species recognition --- p.129<br>Chapter 6.2.2 --- Data compression --- p.130<br>Chapter 6.2.3 --- Band selection --- p.131<br>Chapter 6.3 --- Limitations of this study --- p.132<br>Chapter 6.4 --- Recommendations for further studies --- p.133<br>Chapter 6.5 --- Conclusion --- p.136<br>BIBLIOGRAPHY --- p.137<br>APPENDICES<br>Appendix 1 Reflectance of the 25 tree species in four seasons with three levels of leaf density --- p.142-166<br>"Appendix 2 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by linear discriminant analysis for each season" --- p.167-178<br>"Appendix 3 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by neural networks for each season" --- p.179-190<br>Appendix 4 Confusion matrices of tree species recognition using 21 tree species with original spectra classified by linear discriminant analysis for seasonal comparison --- p.191-193<br>Appendix 5 Confusion matrices of tree species recognition using the first eight PC scores classified by linear discriminant analysis for each season --- p.194-197<br>"Appendix 6 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 2) for each season" --- p.198-209<br>"Appendix 7 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 3) for each season" --- p.210-220<br>"Appendix 8 Confusion matrices of tree species recognition using 21 tree species with original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis for seasonal comparison" --- p.221-229<br>Appendix 9 Confusion matrices of tree species recognition using the spectral bands selected by hierarchical clustering procedures and classified by linear discriminant analysis for each season --- p.230-257
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Li, Jing Yang. "A system for estimating water content of conifer forests using hyperspectral remote sensing data." Thesis, 2006. http://hdl.handle.net/1828/1870.
Full textAbstract:
Estimation of foliage water content from remote sensing data is critical to wildfire management and monitoring forest health. Several efforts to estimate vegetation water content have relied on empirical relationships and data-specific calibrations. Therefore, the approaches used by these studies are not applicable to larger scales and different species. This work was undertaken to develop systems for retrieving foliage water content of Douglas-fir stands with closed canopy. The canopy structural parameters were constrained by forest dynamic relationships. Sensitivity analysis was used to quantify the influence of foliage water content and other factors (LAI, canopy closure, soil) on canopy reflectance simulated in the spectral range between 400 and 2400 nm. Lookup tables were generated using a forest radiative transfer model. Fuel moisture content (FMC) of Douglas-fir can then be determined from airborne hyperspectral imagery (AVIRIS) by the lookup table method. We achieved an accuracy of R2 of 0.74 for FMC which was assessed through comparisons of the estimated foliage water content with field measurements. A software system. FMAS (Fuel Moisture Content Mapping System), was developed for the estimation of fuel moisture content of Douglas-fir forests. Conclusions and further research issues were discussed.
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Dye, Michelle. "Examining the utility of the random ensemble and remotely sensed image data to predict Pinus patula forest age in KwaZulu-Natal, South Africa." Thesis, 2010. http://hdl.handle.net/10413/4916.
Full textAbstract:
The mapping of forest age is important for effective forest inventory as age is indicative of a number of plant physiological processes. Field survey techniques have traditionally been used to collect forest inventory data, but these methods are costly and time-consuming. Remote sensing offers an alternative which is time-effective and cost-effective and can cover large areas. The aim of this research was to assess the capabilities of multispectral and hyperspectral remotely sensed image data and the statistical method, random forest, for Pinus patula age prediction. The
first section of this study used spatial and spectral data derived from multispectral QuickBird imagery to predict forest age. Five co-occurrence texture measures (variance, contrast, correlation, homogeneity, and dissimilarity) were calculated on QuickBird panchromatic imagery (0.6 m spatial resolution) using 12 moving window sizes. The spectral data was extracted from visible and near infrared (NIR) QuickBird imagery (2.4 m spatial resolution). Using the random forest ensemble, various
methods of combining the spectral and texture variables were evaluated. The best model was achieved using backward variable selection which aims to find the fewest number of input bands while maintaining the highest predictive accuracy. Only five of the original 64 variables were used in the final model (R2 = 0.68).
The second part of this study examined the utility of the random forest
ensemble and AISA Eagle hyperspectral image data to predict P. patula age.
Random forest was used to determine the optimal subset of hyperspectral bands that could predict P. patula age. Two sequential variable selection methods were tested: forward and backward variable selection. Although both methods resulted in the same root mean square error (3.097), the backward variable selection method was unable to significantly reduce the large hyperspectral dataset and selected 206 variables for the model. The forward variable selection method successfully reduced the large dataset to only nine optimal bands while maintaining the highest predictive
accuracy from the hyperspectral dataset (R2 = 0.6).
Overall, we concluded that (i) remotely sensed data can produce accurate
models for P. patula age prediction, (ii) random forest is an effective tool for the combination of spectral and spatial multispectral data, (iii) random forest is an effective tool for variable selection of a high dimensional hyperspectral dataset, and (iv), although random forest has mainly been used as a classifier, it is also a very
effective tool for prediction.<br>Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.
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McDonald, Sarah Elizabeth Alice. "Hyperspectral remote sensing of conifer biochemistry in the Greater Victoria Watershed District, British Columbia." Thesis, 2004. http://hdl.handle.net/1828/1070.
Full textAbstract:
The biochemical composition of conifer foliage in the Greater Victoria Watershed District (GVWD), Vancouver Island, Canada, was examined using hyperspectral remote sensing data. Imagery acquired from the airborne sensor Advanced Visible/Infrared Imaging Spectrometer (AVIRIS) was compared to sampled foliar chemical measurements to provide regional maps of biochemical distribution. The biochemical concentrations of nitrogen, chlorophyll and moisture derived from AVIRIS data were analyzed to provide an analysis of the forest canopy, comprised predominantly of Douglas-fir.
The AVIRIS data were preprocessed to correct for atmospheric and geometric distortion, degradation, and noise inherent in the data in order to properly represent the forest canopy at the time of image acquisition. The AVIRIS data were used to investigate the relationship between the reflectance, absorbance and derivative values present in the imagery corresponding to the sampled chemical data. A total of 29 plots were used in a partial least squares regression analysis to analyze the relationship between the data sets to extract chemical constituents in the forest canopy. Nitrogen and total chlorophyll models have r2
values of 0.73 and 0.68 respectively. Due to the complexity of moisture interaction with hyperspectral data, regression models were unable to be computed for the AVIRIS data over the GVWD. Regression models were then applied to the entire AVIRIS dataset for regional mapping of the canopy biochemistry. The distribution of nitrogen and total chlorophyll in the forested areas of the GVWD was mapped.
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Norris-Rogers, Mark. "An investigation into using textural analysis and change detection techniques on medium and high spatial resolution imagery for monitoring plantation forestry operations." Thesis, 2006. http://hdl.handle.net/10413/5517.
Full textAbstract:
Plantation forestry involves the management of man-made industrial forests for the
purpose of producing raw materials for the pulp and paper, saw milling and other
related wood products industries. Management of these forests is based on the cycle
of planting, tending and felling of forest stands such that a sustainable operation is
maintained. The monitoring and reporting of these forestry operations is critical to
the successful management of the forestry industry. The aim of this study was to test
whether the forestry operations of clear-felling, re-establishment and weed control
could be qualitatively and quantitatively monitored through the application of
classification and change detection techniques to multi-temporal medium (15-30 m)
and a combination of textural analysis and change detection techniques on high
resolution (0.6-2.4 m) satellite imagery.
For the medium resolution imagery, four Landsat 7 multi-spectral images covering
the period from March 2002 to April 2003 were obtained over the midlands of
KwaZulu-Natal, South Africa, and a supervised classification, based on the
Maximum Likelihood classifier, as well as two unsupervised classification routines
were applied to each of these images. The supervised classification routine used 12
classes identified from ground-truthing data, while the unsupervised classification
was done using 10 and 4 classes. NDVI was also calculated and used to estimate
vegetation status. Three change detection techniques were applied to the
unsupervised classification images, in order to determine where clear-felling,
planting and weed control operations had occurred. An Assisted "Classified" Image
change detection technique was applied to the Ten-Class Unsupervised
Classification images, while an Assisted "Quantified Classified" change detection
technique was applied to the Four-Class Unsupervised Classification images. An
Image differencing technique was applied to the NDVI images. For the high
resolution imagery, a series of QuickBird images of a plantation forestry site were
used and a combination of textural analysis and change detection techniques was
tested to quantify weed development in replanted forest stands less than 24 months
old. This was achieved by doing an unsupervised classification on the multi-spectral
bands, and an edge-enhancement on the panchromatic band. Both the resultant
datasets were then vectorised, unioned and a matrix derived to determine areas of
high weed.
It was found that clear-felling operations could be identified with accuracy in excess
of 95%. However, using medium resolution imagery, newly planted areas and the
weed status of forest stands were not definitively identified as the spatial resolution
was too coarse to separate weed growth from tree stands. Planted stands younger
than one year tended to be classified in the same class as bare ground or ground
covered with dead branches and leaves, even if weeds were present. Stands older
than one year tended to be classified together in the same class as weedy stands,
even where weeds were not present. The NDVI results indicated that further
research into this aspect could provide more useful information regarding the
identification of weed status in forest stands. Using the multi-spectral bands of the
high resolution imagery it was possible to identify areas of strong vegetation, while
crop rows were identifiable on the panchromatic band. By combining these two
attributes, areas of high weed growth could be identified. By applying a post-classification
change detection technique on the high weed growth classes, it was
possible to identify and quantify areas of weed increase or decrease between
consecutive images. A theoretical canopy model was also derived to test whether it
could identify thresholds from which weed infestations could be determined.
The conclusions of this study indicated that medium resolution imagery was
successful in accurately identifying clear-felled stands, but the high resolution
imagery was required to identify replanted stands, and the weed status of those
stands. However, in addition to identifying the status of these stands, it was also
possible to quantify the level of weed infestation. Only wattle (Acacia mearnsii)
stands were tested in this manner but it was recommended that in addition to
applying these procedures to wattle stands, they also are tested in Eucalyptus and
Pinus stands. The combination of textural analysis on the panchromatic band and
classification of multi-spectral bands was found to be a suitable process to achieve
the aims of this study, and as such were recommended as standard procedures that
could be applied in an operational plantation forest monitoring environment.<br>Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2006.
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Marlier, Miriam E. "Public Health Impacts from Fires in Tropical Landscapes." Thesis, 2014. https://doi.org/10.7916/D83N21CM.
Full textAbstract:
Fires are the primary method of deforestation and agricultural management in the tropics, but associated emissions such as aerosols, ozone, and carbon monoxide can have negative impacts on ecosystems, climate, and public health. Recent advances in satellite monitoring of fire activity, including using thermal anomalies for active fire detections and burn scar mapping of post-fire effects, have offered an unprecedented level of detail in understanding the magnitude and extent of fire activity. This dissertation aims to quantify the human health impact across populations in tropical regions by determining which areas are the most susceptible to transported fire emissions and how this exposure varies over time. The following chapters can be used to highlight critical conservation regions, not only for conserving ecosystems for biodiversity and climate benefits, but also for protecting public health. To address how fire emissions can affect regional populations, satellite observations of fire activity are combined with models of how tropical fire emissions are transported in the atmosphere. Satellites provide two primary pieces of information for this approach: 1) measurements of the distribution and magnitude of fire activity, and 2) categorization of fire types (such as agricultural burning or deforestation) by overlaying observed fire patterns on land use maps. Atmospheric models perform the crucial step of simulating how emissions evolve and where they are transported after release into the atmosphere. The following dissertation chapters are linked through exploration of fire emissions impacts from continental to local scales, including implementing fire emissions inventories into atmospheric models, quantifying population exposure to fire activity in Equatorial Asia, and projecting fire emissions associated with various future land use scenarios in Sumatra. Model estimates of aerosol concentrations are more influenced than trace gases by using finer temporal resolution fire emissions, due to interactions between emissions and modeled meteorology and transport. This in turn can impact air quality estimates by permitting higher peak concentrations. In addition, model results show that population exposure to fire emissions in Equatorial Asia is highly variable over time depending on the phase of the El Niño cycle; strong El Niño years can have fire contributions to fine particulate matter of up to 200 µg/m³ near fire sources, corresponding to 200 additional days per year over the World Health Organization 50 µg/m³ 24-hour fine particulate matter air quality target. These risks are not confined to people living near fire sources, but expose broad regional populations due to the atmospheric transport of emissions. Health impacts also depend on underlying fuel characteristics, with the future magnitude of Equatorial Asian fire emissions estimated to be strongly dependent on the level of protection given to fuel-rich peatswamp forests (contributing 33-48% of future emissions in the absence of protection). Collectively, these chapters emphasize variability in how tropical fire emissions affect regional population exposures to outdoor air pollution, and the need to consider the dependence of this public health effect on different fuel types and year-to-year variations in climate. The results described in this dissertation quantify direct benefits of conservation for people living near fire areas.
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Melati, Dian Nuraini. "The use of remote sensing data to monitor land use systems and forest variables of the tropical rainforest landscape under transformation in Jambi Province, Sumatra, Indonesia." Doctoral thesis, 2017. http://hdl.handle.net/11858/00-1735-0000-002E-E323-E.
Full text
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Oumar, Zakariyyaa. "Remote sensing of forest health : the detection and mapping of Thaumastocoris peregrinus damage in plantation forests." Thesis, 2012. http://hdl.handle.net/10413/9483.
Full textAbstract:
Thaumastocoris peregrinus (T. peregrinus) is a sap-sucking insect that feeds on Eucalyptus
leaves. It poses a major threat to the forest sector by reducing the photosynthetic ability of the
tree, resulting in stunted growth and even death of severely infested trees. The foliage of the
tree infested with T. peregrinus turns into a deep red-brown colour starting at the northern
side of the canopy but progressively spreads to the entire canopy. The monitoring of T.
peregrinus and the effect it has on plantation health is essential to ensure productivity and
future sustainability of forest yields. Insitu hyperspectral remote sensing combined with
greater availability and lower cost of new generation multispectral satellite data, provides
opportunities to detect and map T. peregrinus damage in plantation forests. This research
advocates the development of remote sensing techniques to accurately detect and map T.
peregrinus damage, an assessment that is critically needed to monitor plantation health in
South Africa.
The study first provides an overview of how improvements in multispectral and hyperspectral
technology can be used to detect and map T. peregrinus damage, based on the previous work
done on the remote sensing of forest pests. Secondly, the utility of field hyperspectral remote
sensing in predicting T. peregrinus damage was tested. High resolution field spectral data that
was resampled to the Hyperion sensor successfully predicted T. peregrinus damage with high
accuracies using narrowband normalized indices and vegetation indices. Field spectroscopy
was further tested in predicting water stress induced by T. peregrinus infestation, in order to
identify early physiological stages of damage. A neural network algorithm successfully
predicted plant water content and equivalent water thickness in T. peregrinus infested
plantations. The result is promising for forest health monitoring programmes in detecting
previsual physiological stages of damage.
The analysis was then upscaled from field hyperspectral sensing to spaceborne sensing using
the new generation WorldView-2 multispectral sensor, which contains key vegetation
wavelengths. Partial least squares regression models were developed from the WorldView-2
bands and indices and significant predictors were identified by variable importance scores.
The red edge and near-infrared bands of the WorldView-2 sensor, together with pigment
specific indices predicted and mapped T. peregrinus damage with high accuracies. The study
further combined environmental variables and vegetation indices calculated from the
WorldView-2 imagery to improve the prediction and mapping of T. peregrinus damage using
a multiple stepwise regression approach. The regression model selected the near infrared
band 8 of the WorldView-2 sensor and the temperature dataset to predict and map T.
peregrinus damage with high accuracies on an independent test dataset. This research
contributes to the field of knowledge by developing innovative remote sensing techniques
that can accurately detect and map T. peregrinus damage using the new generation
WorldView-2 sensor. The result is significant for forest health monitoring and highlights the
importance of improved sensors which contain key vegetation wavelengths for plantation
health assessments.<br>Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2012.
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Ndyamboti, Kuhle Siseko. "Land cover classification in a heterogeneous environment : testing the perfomance of multispectral remote sensing data and the random forest ensemble algorithm." Thesis, 2013. http://hdl.handle.net/10413/10847.
Full textAbstract:
Land use/land cover (LULC) information is essential for a plethora of applications including environmental monitoring and natural resource management. Traditionally, field surveying techniques were the sole source of acquiring such information; however, these methods are labour intensive, costly and time consuming. With the advent of remote sensing, LULC information can be acquired in an economical, less tedious and non-time consuming manner at shorter temporal cycles and over larger areas. The aim of this study was to assess the utility of multispectral remote sensing data and the Random Forest (RF) algorithm to improve accuracy of LULC maps in heterogeneous ecosystems.
The first part of this study used moderate resolution SPOT-5 data to compare the performance of the RF algorithm to that of the commonly used Maximum Likelihood (ML) classifier. Results indicated that RF performed significantly better than ML (66.1%) and yielded an overall accuracy of 80.2%. Moreover, RF variable importance measures were able to provide an insight on the bands that played a pivotal role in the classification process. Due to the fact that moderate resolution satellite data was used, both classifiers seemed to experience some difficulties in discriminating amongst classes that exhibited similar spectral responses such as Eucalyptus grandis and Pinus tree plantations, young sugarcane and mature sugarcane, as well as river and ocean water. In that regard, the next section attempted to address this shortfall.
The second part of the study used high resolution multispectral data acquired from the WorldView-2 sensor to discriminate amongst six spectrally similar LULC classes using the advanced RF algorithm. Results suggested that the use of WorldView-2 data together with the RF ensemble algorithm is a robust and accurate method for separating classes exhibiting similar spectral responses. The classification process yielded an overall accuracy of 91.23% and also provided valuable insight into WorldView-2 bands that were most suitable for discriminating the LULC categories.
Overall, the study concluded that: (i) multispectral remote sensing data is an effective tool for obtaining accurate and timely LULC information, (ii) moderate resolution multispectral data can be used to map broad LULC categories whereas high resolution multispectral data can be used to separate LULC at finer levels of detail, (iii) RF is a robust and effective tool for producing LULC maps that are less prone to error.<br>Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.
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Malahlela, Oupa. "Intergrating environmental variables with worldview-2 data to model the probability of occurence of invasive chromolena odata in forest canopy gaps : Dukuduku forest in KwaZulu-Natal, South Africa." Thesis, 2013. http://hdl.handle.net/10413/10562.
Full textAbstract:
Several alien plants are invading subtropical forest ecosystems through canopy gaps,
resulting in the loss of native species biodiversity. The loss of native species in such habitats
may result in reduced ecosystem functioning. The control and eradication of these invaders
requires accurate mapping of the levels of invasion in canopy gaps. Our study tested (i) the
utility of WorldView-2 imagery to map forest canopy gaps, and (ii) an integration of
WorldView-2 data with environmental data to model the probability of occurrence of
invasive Chromolaena odorata (triffid weed) in Dukuduku forest canopy gaps of KwaZulu-
Natal, South Africa. Both pixel-based classification and object-based classification were
explored for the delineation of forest canopy gaps. The overall classification accuracies
increased by ± 12% from a spectrally resampled 4 band image similar to Landsat (74.64%) to
an 8 band WorldView-2 imagery (86.90%). This indicates that the new bands of WorldView
such as the red edge band can improve on the capability of common red, blue, green and
near-infrared bands in delineating forest canopy gaps. The maximum likelihood classifier
(MLC) in pixel-based classification yielded the overall classification accuracy of 86.90% on
an 8 band WorldView-2 image, while the modified plant senescence reflectance index
(mPSRI) in object-based classification yielded 93.69%. The McNemar’s test indicated that
there was a statistical difference between the MLC and the mPSRI. The mPSRI is a
vegetation index that incorporates the use of the red edge band, which solves a saturation
problem common in sensors such as Landsat and SPOT.
An integrated model (with both WorldView-2 data and environmental data) used to predict
the occurrence of Chromolaena odorata in forest gaps yielded a deviance of about 42% (D2 =
0.42), compared to the model derived from environmental data only (D2 = 0.12) and
WorldView-2 data only (D2 = 0.20). A D2 of 0.42 means that a model can explain about 42%
of the variability of the presence/absence of Chromolaena odorata in forest gaps. The
Distance to Stream and Aspect were the significant environmental variables (ρ < 0.05) which
were positively correlated with presence/absence of Chromolaena in forest gaps.
WorldView-2 bands such as the coastal band (λ425 nm) yellow band (λ605 nm) and the nearinfrared-
1 (λ833 nm) are positively and significantly related to the presence/absence of
invasive species (ρ < 0.05). On the other hand, a significant negative correlation (ρ < 0.05) of
near-infrared-2 band (λ950 nm) and the red edge normalized difference vegetation index
(NDVI725) suggests that the probability of occurrence of invasive Chromolaena increases forest gaps with low vegetation density. This study highlights the importance of WorldView-
2 imagery and its application in subtropical indigenous coastal forest monitoring.<br>Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.