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Journal articles on the topic 'Vegetation mapping – Remote sensing'
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Nagasawa, Ryota, and Yoshiyuki Hioki. "Vegetation Mapping using Remote Sensing and GIS." Landscape Ecology and Management 11, no. 1 (2006): 1–2. http://dx.doi.org/10.5738/jale.11.1.
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Hioki, Yoshiyuki, and Ryota Nagasawa. "Vegetation mapping using remote sensing and GIS." Landscape Ecology and Management 11, no. 2 (2007): 105. http://dx.doi.org/10.5738/jale.11.105.
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Laidler, Gita J., and Paul Treitz. "Biophysical remote sensing of arctic environments." Progress in Physical Geography: Earth and Environment 27, no. 1 (March 2003): 44–68. http://dx.doi.org/10.1191/0309133303pp358ra.
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Various remote sensing studies have been conducted to investigate methods and applications of vegetation mapping and analysis in arctic environments. The general purpose of these studies is to extract information on the spatial and temporal distribution of vegetation as required for tundra ecosystem and climate change studies. Because of the recent emphasis on understanding natural systems at large spatial scales, there has been an increasing interest in deriving biophysical variables from satellite data. Satellite remote sensing offers potential for extrapolating, or ‘scaling up’ biophysical measures derived from local sites, to landscape and even regional scales. The most common investigations include mapping spatial vegetation patterns or assessing biophysical tundra characteristics, using medium resolution satellite data. For instance, Landsat TM data have been shown to be useful for broad vegetation mapping and analysis, but not accurately representative of smaller vegetation communities or local spatial variation. It is anticipated, that high spatial resolution remote sensing data, now available from commercial remote sensing satellites, will provide the necessary sampling scale to link field data to remotely sensed reflectance data. As a result, it is expected that these data will improve the representation of biophysical variables over sparsely vegetated regions of the Arctic.
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Bobbe, Tom, Henry Lachowski, Paul Maus, Jerry Greer, and Chuck Dull. "A primer on mapping vegetation using remote sensing." International Journal of Wildland Fire 10, no. 4 (2001): 277. http://dx.doi.org/10.1071/wf01029.
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This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 The use of information based upon remotely sensed data is a central factor in our 21st Century society. Scientists in land management agencies especially require accurate and current geospatial information to effectively implement ecosystem management. The increasing need to collect data across diverse landscapes, scales, and ownerships has resulted in a wider application of remote sensing, Geographic Information Systems (GIS) and associated geospatial technologies for natural resource applications. This paper summarizes the use of digital remotely sensed data for vegetation mapping. Key steps in preparing vegetation maps are described. These steps include defining project requirements and classification schemes, use of reference data, classification procedures, and assessing accuracy. The role of field personnel and inventory data is described. Case studies and applications of vegetation mapping on national forest land are also included. remote sensing, GIS, mapping, geospatial, project planning.
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Xie, Y., Z. Sha, and M. Yu. "Remote sensing imagery in vegetation mapping: a review." Journal of Plant Ecology 1, no. 1 (March 1, 2008): 9–23. http://dx.doi.org/10.1093/jpe/rtm005.
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Zhang, Liyuan, Huihui Zhang, Yaxiao Niu, and Wenting Han. "Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing." Remote Sensing 11, no. 6 (March 13, 2019): 605. http://dx.doi.org/10.3390/rs11060605.
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Mapping maize water stress status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping water stress status of maize under different levels of deficit irrigation at the late vegetative, reproductive and maturation growth stages. Canopy temperature, field air temperature and relative humidity obtained by a handheld infrared thermometer and a portable air temperature/relative humidity meter were used to establish a crop water stress index (CWSI) empirical model under the weather conditions in Ordos, Inner Mongolia, China. Nine vegetation indices (VIs) related to crop water stress were derived from the UAV multispectral imagery and used to establish CWSI inversion models. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stages with an increase of 2.1 °C, however, the non-transpiring baseline did not change significantly with an increase of 0.1 °C. The ratio of transformed chlorophyll absorption in reflectance index (TCARI) and renormalized difference vegetation index (RDVI), and the TCARI and soil-adjusted vegetation index (SAVI) had the best correlations with CWSI. R2 values were 0.47 and 0.50 for TCARI/RDVI and TCARI/SAVI at the reproductive and maturation stages, respectively; and 0.81 and 0.80 for TCARI/RDVI and TCARI/SAVI at the late reproductive and maturation stages, respectively. Compared to CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study had more abilities to assess the field variability of crop and soil. This study demonstrates the potentiality of using high-resolution UAV multispectral imagery to map maize water stress.
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Pascucci, Simone, Stefano Pignatti, Raffaele Casa, Roshanak Darvishzadeh, and Wenjiang Huang. "Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation”." Remote Sensing 12, no. 21 (November 9, 2020): 3665. http://dx.doi.org/10.3390/rs12213665.
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The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies.
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Mohd Salleh, M. R., N. I. Ishak, K. A. Razak, M. Z. Abd Rahman, M. A. Asmadi, Z. Ismail, and M. F. Abdul Khanan. "GEOSPATIAL APPROACH FOR LANDSLIDE ACTIVITY ASSESSMENT AND MAPPING BASED ON VEGETATION ANOMALIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W9 (October 30, 2018): 201–15. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w9-201-2018.
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<p><strong>Abstract.</strong> Remote sensing has been widely used for landslide inventory mapping and monitoring. Landslide activity is one of the important parameters for landslide inventory and it can be strongly related to vegetation anomalies. Previous studies have shown that remotely sensed data can be used to obtain detailed vegetation characteristics at various scales and condition. However, only few studies of utilizing vegetation characteristics anomalies as a bio-indicator for landslide activity in tropical area. This study introduces a method that utilizes vegetation anomalies extracted using remote sensing data as a bio-indicator for landslide activity analysis and mapping. A high-density airborne LiDAR, aerial photo and satellite imagery were captured over the landslide prone area along Mesilau River in Kundasang, Sabah. Remote sensing data used in characterizing vegetation into several classes of height, density, types and structure in a tectonically active region along with vegetation indices. About 13 vegetation anomalies were derived from remotely sensed data. There were about 14 scenarios were modeled by focusing in 2 landslide depth, 3 main landslide types with 3 landslide activities by using statistical approach. All scenarios show that more than 65% of the landslides are captured within 70% of the probability model indicating high model efficiency. The predictive model rate curve also shows that more than 45% of the independent landslides can be predicted within 30% of the probability model. This study provides a better understanding of remote sensing data in extracting and characterizing vegetation anomalies induced by hillslope geomorphology processes in a tectonically active region in Malaysia.</p>
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BELLUCO, E., M. CAMUFFO, S. FERRARI, L. MODENESE, S. SILVESTRI, A. MARANI, and M. MARANI. "Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing." Remote Sensing of Environment 105, no. 1 (November 15, 2006): 54–67. http://dx.doi.org/10.1016/j.rse.2006.06.006.
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Rao, Mahesh, Zachary Silber-Coats, Sharon Powers, Lawrence Fox III, and Abduwasit Ghulam. "Mapping drought-impacted vegetation stress in California using remote sensing." GIScience & Remote Sensing 54, no. 2 (March 4, 2017): 185–201. http://dx.doi.org/10.1080/15481603.2017.1287397.
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White, JD, KC Ryan, CC Key, and SW Running. "Remote Sensing of Forest Fire Severity and Vegetation Recovery." International Journal of Wildland Fire 6, no. 3 (1996): 125. http://dx.doi.org/10.1071/wf9960125.
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Burned forested areas have patterns of varying burn severity as a consequence of various topographic, vegetation, and meteorological factors. These patterns are detected and mapped using satellite data. Other ecological information can be abstracted from satellite data regarding rates of recovery of vegetation foliage and variation of burn severity on different vegetation types. Middle infrared wavelengths are useful for burn severity mapping because the land cover changes associated with burning increase reflectance in this part of the electromagnetic spectrum. Simple stratification of Landsat Thematic Mapper data define varying classes of burn severity because of changes in canopy cover, biomass removal, and soil chemical composition. Reasonable maps of burn severity are produced when the class limits of burn severity reflectance are applied to the entire satellite data. Changes in satellite reflectance over multiple years reveal the dynamics of vegetation and fire severity as low burn areas have lower changes in reflectance relative to high burn areas. This results as a consequence of how much the site was altered due to the burn and how much space is available for vegetation recovery. Analysis of change in reflectance across steppe, riparian, and forested vegetation types indicate that fires potentially increase biomass in steppe areas, while riparian and forested areas are slower to regrow to pre-fire conditions. This satellite-based technology is useful for mapping severely burned areas by exploring the ecological manifestations before and after fire.
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Song, Conghe, Matthew P. Dannenberg, and Taehee Hwang. "Optical remote sensing of terrestrial ecosystem primary productivity." Progress in Physical Geography: Earth and Environment 37, no. 6 (November 8, 2013): 834–54. http://dx.doi.org/10.1177/0309133313507944.
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Terrestrial ecosystem primary productivity is a key indicator of ecosystem functions, including, but not limited to, carbon storage, provision of food and fiber, and sustaining biodiversity. However, measuring terrestrial ecosystem primary productivity in the field is extremely laborious and expensive. Optical remote sensing has revolutionized our ability to map terrestrial ecosystem primary productivity over large areas ranging from regions to the entire globe in a repeated, cost-efficient manner. This progress report reviews the theory and practice of mapping terrestrial primary productivity using optical remotely sensed data. Terrestrial ecosystem primary productivity is generally estimated with optical remote sensing via one of the following approaches: (1) empirical estimation from spectral vegetation indices; (2) models that are based on light-use-efficiency (LUE) theory; (3) models that are not based on LUE theory, but the biophysical processes of plant photosynthesis. Among these three, models based on LUE are the primary approach because there is a solid physical basis for the linkage between fraction of absorbed photosynthetically active radiation (fAPAR) and remotely sensed spectral signatures of vegetation. There has been much inconsistency in the literature with regard to the appropriate value for LUE. This issue should be resolved with the ongoing efforts aimed at direct mapping of LUE from remote sensing. At the same time, major efforts have been dedicated to mapping vegetation canopy biochemical composition via imaging spectroscopy for use in process-based models to estimating primary productivity. In so doing, optical remote sensing will continue to play a vital role in global carbon cycle science research.
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Lukáš Brodský and Luboš, Borůvka. "Object-oriented Fuzzy Analysis of Remote Sensing Data for Bare Soil Brightness Mapping." Soil and Water Research 1, No. 3 (January 7, 2013): 79–84. http://dx.doi.org/10.17221/6509-swr.
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Remote sensing data have an important advantage; the data provide spatially exhaustive sampling of the area of interest instead of having samples of tiny fractions. Vegetation cover is, however, one of the application constraints in soil science. Areas of bare soil can be mapped. These spatially dense data require proper techniques to map identified patterns. The objective of this study was mapping of spatial patterns of bare soil colour brightness in a Landsat 7 satellite image in the study area of Central Bohemia using object-oriented fuzzy analysis. A soil map (1:200 000) was used to associate soil types with the soil brightness in the image. Several approaches to determine membership functions (MF) of the fuzzy rule base were tested. These included a simple manual approach, k-means clustering, a method based on the sample histogram, and one using the probability density function. The method that generally provided the best results for mapping the soil brightness was based on the probability density function with KIA = 0.813. The resulting classification map was finally compared with an existing soil map showing 72.0% agreement of the mapped area. The disagreement of 28.0% was mainly in the areas of Chernozems (69.3%).
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Unni, N. V. M. "Small-scale vegetation mapping of large territories: perspectives of using modern remote sensing and GIS technologies, the Indian experiences." Geobotanical mapping, no. 1994-1995 (1996): 51–54. http://dx.doi.org/10.31111/geobotmap/1994-1995.51.
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The recognition of versatile importance of vegetation for the human life resulted in the emergence of vegetation science and many its applications in the modern world. Hence a vegetation map should be versatile enough to provide the basis for these applications. Thus, a vegetation map should contain not only information on vegetation types and their derivatives but also the geospheric and climatic background. While the geospheric information could be obtained, mapped and generalized directly using satellite remote sensing, a computerized Geographic Information System can integrate it with meaningful vegetation information classes for large areas. Such aft approach was developed with respect to mapping forest vegetation in India at. 1 : 100 000 (1983) and is in progress now (forest cover mapping at 1 : 250 000). Several review works reporting the experimental and operational use of satellite remote sensing data in India were published in the last years (Unni, 1991, 1992, 1994).
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Lira, Daniel Rodrigues, Maria do Socorro Bezerra de Araújo, Everardo Valadares De Sá Barretto Sampaio, and Hewerton Alves da Silva. "Mapeamento e Quantificação da Cobertura Vegetal no Agreste Central de Pernambuco Utilizando Técnicas de Empilhamento e o NDVI (Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique)." Revista Brasileira de Geografia Física 3, no. 3 (January 3, 2011): 157. http://dx.doi.org/10.26848/rbgf.v3i3.232664.
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O mapeamento e monitoramento da cobertura vegetal receberam consideráveis impulsos nas últimas décadas, com o advento do sensoriamento remoto, processamento digital de imagens e políticas de combate ao desmatamento, além dos avanços nas pesquisas e gerações de novos sensores orbitais e sua distribuição de forma mais acessível aos usuários, tornam as imagens de satélite um dos produtos do sensoriamento remoto mais utilizado para análises da cobertura vegetal das terras. Os índices de cobertura vegetal deste trabalho foram obtidos usando o NDVI - Normalized Difference Vegetation Index para o Agreste central de Pernambuco indicou 39,7% de vegetação densa, 13,6% de vegetação esparsa, 14,3% de vegetação rala e 10,5% de solo exposto. O NDVI apresentou uma caracterização satisfatória para a classificação do estado da vegetação do ano de 2007 para o Agreste Central pernambucano, porém ocorreu uma confusão com os índices de nuvens, sombras e solos exposto, necessitando de uma adaptação na técnica para um melhor aprimoramento da diferenciação desses elementos, constituindo numa recombinação de bandas após a elaboração e calculo do NDVI.Palavras-chave: Geoprocessamento; sensoriamento remoto; índice de vegetação. Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique ABSTRACTIn recent decades, advanced techniques for mapping and monitoring vegetation cover have been developed with the advent of remote sensing. New tools for digital processing, the generation of new sensors and their orbital distribution more accessible have facilitated the acquisition and use of satellite images, making them one of the products of remote sensing more used for analysis of the vegetation cover. The aim of this study was to assess the vegetation cover from Central Agreste region of Pernambuco State, using satellite images TM / LANDSAT-5. The images were processed using the NDVI (Normalized Difference Vegetation Index) technique, generating indexes used for classification of vegetation in dense, sparse and scattered. There was a proportion of 39.7% of dense vegetation, 13.6% of sparse vegetation, 14.3% of scattered vegetation and 10.5% of exposed soil. NDVI technique has been used as a useful tool in the classification of vegetation on a regional scale, however, needs improvement to a more precise differentiation among levels of clouds, shadow, exposed soils and vegetation. Keywords: Geoprocessing, remote sensing, vegetation index
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Nolin, Anne W. "Recent advances in remote sensing of seasonal snow." Journal of Glaciology 56, no. 200 (2010): 1141–50. http://dx.doi.org/10.3189/002214311796406077.
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AbstractRemote sensing offers local, regional and global observations of seasonal snow, providing key information on snowpack processes. This brief review highlights advancements in instrumentation and analysis techniques that have been developed over the past decade. Areas of advancement include improved algorithms for mapping snow-cover extent, snow albedo, snow grain size, snow water equivalent, melt detection and snow depth, as well as new uses of instruments such as multiangular spectroradiometers, scatterometry and lidar. Limitations and synergies of the instruments and techniques are discussed, and remaining challenges such as multisensor mapping, scaling issues, vegetation correction and data assimilation are identified.
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Johnston, RM, and MM Barson. "Remote sensing of Australian wetlands: An evaluation of Landsat TM data for inventory and classification." Marine and Freshwater Research 44, no. 2 (1993): 235. http://dx.doi.org/10.1071/mf9930235.
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This study aimed to develop simple remote-sensing techniques suitable for mapping and monitoring wetlands, using Landsat TM imagery of inland wetland sites in Victoria and New South Wales. A range of classification methods was examined in attempts to map the location and extent of wetlands and their vegetation types. Multi-temporal imagery (winter/spring and summer) was used to display seasonal variability in water regime and vegetation status. Simple density slicing of the mid-infrared band (TM5) from imagery taken during wet conditions was useful for mapping the location and extent of inundated areas. None of the classification methods tested reproduced field maps of dominant vegetation species; however, density slicing of multi-temporal imagery produced classes based on seasonal variation in water regime and vegetation status that are useful for reconnaissance mapping and for examining variability in previously mapped units. Satellite imagery is unlikely to replace aerial photography for detailed mapping of wetland vegetation types, particularly where ecological gradients are steep, as in many riverine systems. However, it has much to offer in monitoring changes in water regime and in reconnaissance mapping at regional scales.
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Golubeva, Elena, Yurate Plyushkyavichyute, Gareth Rees, and Olga Tutubalina. "REMOTE SENSING METHODS FOR PHYTOMASS ESTIMATION AND MAPPING OF TUNDRA VEGETATION." GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY 3, no. 3 (January 1, 2010): 4–13. http://dx.doi.org/10.24057/2071-9388-2010-3-3-4-13.
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Golubeva, Elena, Yurate Plyushkyavichyute, Gareth Rees, and Olga Tutubalina. "REMOTE SENSING METHODS FOR PHYTOMASS ESTIMATION AND MAPPING OF TUNDRA VEGETATION." GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY 3, no. 3 (October 21, 2010): 4–13. http://dx.doi.org/10.15356/2071-9388_03v03_2010_01.
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Zhuang, Dafang, Yangrong Ling, and Awaya Yoshio. "Integrated vegetation classification and mapping using remote sensing and GIS techniques." Chinese Geographical Science 9, no. 1 (March 1999): 49–56. http://dx.doi.org/10.1007/s11769-999-0020-5.
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Roff, Adam, Anthea Mitchell, Michael Day, and Geoffrey Taylor. "Community scale vegetation mapping and condition assessment using hyperspectral remote sensing." Ecological Management and Restoration 7, s1 (June 2006): S78—S79. http://dx.doi.org/10.1111/j.1442-8903.2006.298_3.x.
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Vannier, Clémence, and Laurence Hubert-Moy. "Multiscale comparison of remote-sensing data for linear woody vegetation mapping." International Journal of Remote Sensing 35, no. 21 (November 2, 2014): 7376–99. http://dx.doi.org/10.1080/01431161.2014.968683.
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Fraser, Robert H., Ian Olthof, Trevor C. Lantz, and Carla Schmitt. "UAV photogrammetry for mapping vegetation in the low-Arctic." Arctic Science 2, no. 3 (September 2016): 79–102. http://dx.doi.org/10.1139/as-2016-0008.
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Plot-scale field measurements are necessary to monitor changes to tundra vegetation, which has a small stature and high spatial heterogeneity, while satellite remote sensing can be used to track coarser changes over larger regions. In this study, we explored the potential of unmanned aerial vehicle (UAV) photographic surveys to map low-Arctic vegetation at an intermediate scale. A multicopter was used to capture highly overlapping, subcentimetre photographs over a 2 ha site near Tuktoyaktuk, Northwest Territories. Images were processed into ultradense 3D point clouds and 1 cm resolution orthomosaics and vegetation height models using Structure-from-Motion (SfM) methods. Shrub vegetation heights measured on the ground were accurately represented using SfM point cloud data (r2 = 0.96, SE = 8 cm, n = 31) and a combination of spectral and height predictor variables yielded an 11-class classification with 82% overall accuracy. Differencing repeat UAV surveys before and after manually trimming shrub patches showed that vegetation height decreases in trimmed areas (− 6.5 cm, SD = 21 cm). Based on these findings, we conclude that UAV photogrammetry provides a promising, cost-efficient method for high-resolution mapping and monitoring of tundra vegetation that can be used to bridge the gap between plot and satellite remote sensing measurements.
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Anggraini, Nanin, and Atriyon Julzarika. "Deteksi Tinggi Vegetasi di Delta Mahakam dengan Penginderaan Jauh." Oseanologi dan Limnologi di Indonesia 4, no. 3 (December 31, 2019): 175. http://dx.doi.org/10.14203/oldi.2019.v4i3.212.
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<strong>Detection of Vegetation Height in Mahakam Delta Using Remote Sensing. </strong>The vegetation height is a vertical distance between top of the vegetation to ground surface. Vegetation height is one of the parameters for vegetation growth. There are various methods to measure vegetation height; one of them is the use of remote sensing technology. This study aims to map vegetation height in Mahakam Delta by using height models derived from remote sensing data. Such models are Digital Surface Model (DSM) and Digital Terrain Model (DTM). DSM was generated using a combination of interferometric processing of ALOS PALSAR interferometry, X-SAR, Shuttle Radar Topography Mission (SRTM), and geodetic height of Icesat/GLAS satellite imagery. This integration technique incorporated the Digital Elevation Model (DEM) method. The geoid model used in this study was EGM 2008. The following step was the correction of height errors of DSM. Terrain correction was undertaken to convert DSM into DTM, while vegetation heights were obtained from subtraction of DSM and DTM. Vertical accuracy verification refers to a tolerance of 1.96σ (95%) or ~80 cm. In DSM, a vertical accuracy value of 60.4 cm was obtained so that the DSM is feasible for mapping with scale of 1: 10,000, while the DTM was 37 cm so it is also applicable for mapping with such scale. Based on the subtraction of DSM and DTM, the vegetation heights in Mahakam Delta varied between 0 and 64 m.
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Mao, Lijun, Mingshi Li, and Wenjuan Shen. "Remote Sensing Applications for Monitoring Terrestrial Protected Areas: Progress in the Last Decade." Sustainability 12, no. 12 (June 19, 2020): 5016. http://dx.doi.org/10.3390/su12125016.
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Terrestrial protected areas (PAs) play an essential role in maintaining biodiversity and ecological processes worldwide, and the monitoring of PAs is a useful tool in assessing the effectiveness of PA management. Advanced remote sensing technologies have been increasingly used for mapping and monitoring the dynamics of PAs. We review the advances in remote sensing-based approaches for monitoring terrestrial PAs in the last decade and identify four types of studies in this field: land use & land cover and vegetation community classification, vegetation structure quantification, natural disturbance monitoring, and land use & land cover and vegetation dynamic analysis. We systematically discuss the satellite data and methods used for monitoring PAs for the four research objectives. Moreover, we summarize the approaches used in the different types of studies. The following suggestions are provided for future studies: (1) development of remote sensing frameworks for local PA monitoring worldwide; (2) comprehensive utilization of multisource remote sensing data; (3) improving methods to investigate the details of PA dynamics; (4) discovering the driving forces and providing measures for PA management. Overall, the integration of remote sensing data and advanced processing methods can support PA management and decision-making procedures.
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Novo, A., H. González-Jorge, J. Martínez-Sánchez, and H. Lorenzo. "REMOTE SENSING APPROACH TO EVALUATE POST-FIRE VEGETATION STRUCTURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 1031–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1031-2020.
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Abstract. Spain is included in the top five European countries with the highest number of wildfires. Forest fire can produce significant impacts on the structure and functioning of natural ecosystems. After a forest fire, the evaluation of the damage severity and spatial patterns are important for forest recovery planning, which plays a critical role in the sustainability of the forest ecosystem. The process of forest recovery and the ecological and physiological functions of the burned forest area should be continuously monitored. Remote sensing technologies and in special LiDAR are useful to describe the structure of vegetation. The vegetation modelling and the initial changes of forest plant composition are studied in the forest after mapping the burned areas using Landsat-7 images and Sentinel-2 images. Normalized Burn Ratio (NBR) index and Normalized Difference Vegetation Index (NVVI) is calculated as well as the difference before and after fire. The evaluation of temporal changes of vegetation are analysed by statistical variables of the point cloud, average height, standard deviation and variance. Fraction Canopy Cover (FCC) also is calculated and the point cloud is classified following the fuel model by Prometheus. An analysis method based on satellite images was completed in order to analyse the evolution of vegetation in areas that suffer forest fire.
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Arieira, J., D. Karssenberg, S. M. de Jong, E. A. Addink, E. G. Couto, C. Nunes da Cunha, and J. O. Skøien. "Integrating field sampling, geostatistics and remote sensing to map wetland vegetation in the Pantanal, Brazil." Biogeosciences 8, no. 3 (March 17, 2011): 667–86. http://dx.doi.org/10.5194/bg-8-667-2011.
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Abstract. Development of efficient methodologies for mapping wetland vegetation is of key importance to wetland conservation. Here we propose the integration of a number of statistical techniques, in particular cluster analysis, universal kriging and error propagation modelling, to integrate observations from remote sensing and field sampling for mapping vegetation communities and estimating uncertainty. The approach results in seven vegetation communities with a known floral composition that can be mapped over large areas using remotely sensed data. The relationship between remotely sensed data and vegetation patterns, captured in four factorial axes, were described using multiple linear regression models. There were then used in a universal kriging procedure to reduce the mapping uncertainty. Cross-validation procedures and Monte Carlo simulations were used to quantify the uncertainty in the resulting map. Cross-validation showed that accuracy in classification varies according with the community type, as a result of sampling density and configuration. A map of uncertainty derived from Monte Carlo simulations revealed significant spatial variation in classification, but this had little impact on the proportion and arrangement of the communities observed. These results suggested that mapping improvement could be achieved by increasing the number of field observations of those communities with a scattered and small patch size distribution; or by including a larger number of digital images as explanatory variables in the model. Comparison of the resulting plant community map with a flood duration map, revealed that flooding duration is an important driver of vegetation zonation. This mapping approach is able to integrate field point data and high-resolution remote-sensing images, providing a new basis to map wetland vegetation and allow its future application in habitat management, conservation assessment and long-term ecological monitoring in wetland landscapes.
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Azabdaftari, A., and F. Sunar. "SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 3–9. http://dx.doi.org/10.5194/isprsarchives-xli-b7-3-2016.
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Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.
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Azabdaftari, A., and F. Sunar. "SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 3–9. http://dx.doi.org/10.5194/isprs-archives-xli-b7-3-2016.
Full textAbstract:
Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.
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Korets, M. A., V. A. Ryzhkova, I. V. Danilova, and A. S. Prokushkin. "VEGETATION COVER MAPPING BASED ON REMOTE SENSING AND DIGITAL ELEVATION MODEL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 699–704. http://dx.doi.org/10.5194/isprs-archives-xli-b8-699-2016.
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An algorithm of forest cover mapping based on combined GIS-based analysis of multi-band satellite imagery, digital elevation model, and ground truth data was developed. Using the classification principles and an approach of Russian forest scientist Kolesnikov, maps of forest types and forest growing conditions (FGC) were build. The first map is based on RS-composite classification, while the second map is constructed on the basis of DEM-composite classification. The spatial combination of this two layers were also used for extrapolation and mapping of ecosystem carbon stock values (kgC/m<sup>2</sup>). The proposed approach was applied for the test site area (~3600 km<sup>2</sup>), located in the Northern Siberia boreal forests of Evenkia near Tura settlement.
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Korets, M. A., V. A. Ryzhkova, I. V. Danilova, and A. S. Prokushkin. "VEGETATION COVER MAPPING BASED ON REMOTE SENSING AND DIGITAL ELEVATION MODEL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 699–704. http://dx.doi.org/10.5194/isprsarchives-xli-b8-699-2016.
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An algorithm of forest cover mapping based on combined GIS-based analysis of multi-band satellite imagery, digital elevation model, and ground truth data was developed. Using the classification principles and an approach of Russian forest scientist Kolesnikov, maps of forest types and forest growing conditions (FGC) were build. The first map is based on RS-composite classification, while the second map is constructed on the basis of DEM-composite classification. The spatial combination of this two layers were also used for extrapolation and mapping of ecosystem carbon stock values (kgC/m<sup>2</sup>). The proposed approach was applied for the test site area (~3600 km<sup>2</sup>), located in the Northern Siberia boreal forests of Evenkia near Tura settlement.
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Thierion, Vincent, Samuel Alleaume, Christine Jacqueminet, Christelle Vigneau, Kristell Michel, and Sandra Luque. "The potential of Pléiades imagery for vegetation mapping: an example of grasslands and pastoral environments." Revue Française de Photogrammétrie et de Télédétection, no. 208 (October 23, 2014): 105–10. http://dx.doi.org/10.52638/rfpt.2014.124.
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Nowadays the use of remote sensing for vegetation mapping over large areas is becoming progressively common, with the increase of satellites providing a good trade-off between metric spatial resolution and large swath (e.g. Spot 5, RapidEye). In France, the government launched an ambitious project to map all terrestrial habitats of the national territory. — Thus, CarHAB project uses remote sensing technology to support field work and ground observations for vegetation mapping in support to the 11 National Botanical Conservatories working on the whole of French territory. For this purpose, a physiognomic typology has been produced. This typology captures the intrinsic structure of vegetation and potentially its land use. In order to improve semantic and geometric accuracy of the vegetation cover, the use of infra-metric imagery, such as the ones provided by Pléiades constellation offer valuable insights. This imagery offers visual and geometric potentialities closed to aerial photos but with the advantage of better spectral information. Results presented in this research focus on physiognomic mapping of natural and semi-natural vegetation of pasture, grasslands and farmland areas in Isere Department in France. The potentialities of Pléiades imagery are demonstrated by evaluating separability capabilities of textural analysis of woody and herbaceous habitats and vegetation associated to screes.
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Räsänen, Aleksi, Sari Juutinen, Eeva‐Stiina Tuittila, Mika Aurela, and Tarmo Virtanen. "Comparing ultra‐high spatial resolution remote‐sensing methods in mapping peatland vegetation." Journal of Vegetation Science 30, no. 5 (July 19, 2019): 1016–26. http://dx.doi.org/10.1111/jvs.12769.
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Yuan, Lin, and Li-Quan Zhang. "Mapping large-scale distribution of submerged aquatic vegetation coverage using remote sensing." Ecological Informatics 3, no. 3 (July 2008): 245–51. http://dx.doi.org/10.1016/j.ecoinf.2008.01.004.
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Ghasemian Sorboni, N., P. Pahlavani, and B. Bigdeli. "VEGETATION MAPPING OF SENTINEL-1 AND 2 SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND RANDOM FOREST WITH THE AID OF DUAL-POLARIZED AND OPTICAL VEGETATION INDEXES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 435–40. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-435-2019.
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Abstract. Vegetation mapping is one of the most critical challenges of remote sensing society in forestry applications. Sentinel-1 dataset has the potential of vegetation mapping, but because of its limited number of polarizations, full polarized vegetation indexes are not accessible. The Sentinel-2 dataset is more suitable for vegetation mapping because a wide variety of vegetation indexes can be extracted from them. Handling this large number of vegetation indexes needs a robust feature extractor. Convolutional Neural Networks (CNN) extract relevant features through their deep feature layers structure and throw out disturbances from small to large scales. Hence, they can be far useful for classifying remote sensing data when the number of input bands is considerable. After pre-processing Sentinel-1 and 2 datasets and extracting the dual-polarized and optical vegetation indexes, we fed the sentinel-1 vegetation indexes alongside the VV and VH sigma Nought bands to a Random Forest (RF) and 1D CNN classifier. Also, 13 spectral features of the Sentinel-2 and the extracted indexes like Blue Ratio (BR), Vegetation index based on Red Edge (VIRE) and Normalized Near Infrared (NNIR) were imported to a RF and 1D CNN. The classification result of Sentinel-1 data showed that Dual Polarized Soil Vegetation Index (DPSVI) is a good indicator for discriminating vegetation pixels. Also, the experiment on the Sentinel-2 dataset using 1D CNN resulted in True Positive Rate (TPR) and False Positive Rate of 0.839 and 0.034, respectively.
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Langford, Zachary L., Jitendra Kumar, Forrest M. Hoffman, Amy L. Breen, and Colleen M. Iversen. "Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks." Remote Sensing 11, no. 1 (January 2, 2019): 69. http://dx.doi.org/10.3390/rs11010069.
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Land cover datasets are essential for modeling and analysis of Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, often limited due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The fusion of hyperspectral, multispectral, and terrain datasets was performed using unsupervised and supervised classification techniques over a ∼343 km2 area, and a high-resolution (5 m) vegetation classification map was generated. An unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters. We employed a quantitative method to add supervision to the unlabeled clusters, producing a fully labeled vegetation map. We then developed convolutional neural networks (CNNs) using the multi-sensor fusion datasets to map vegetation distributions using the original classes and the classes produced by the unsupervised classification method. To validate the resulting CNN maps, vegetation observations were collected at 30 field plots during the summer of 2016, and the resulting vegetation products developed were evaluated against them for accuracy. Our analysis indicates the CNN models based on the labels produced by the unsupervised classification method provided the most accurate mapping of vegetation types, increasing the validation score (i.e., precision) from 0.53 to 0.83 when evaluated against field vegetation observations.
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Essien, Etido, and Samimi Cyrus. "Detection of Urban Development in Uyo (Nigeria) Using Remote Sensing." Land 8, no. 6 (June 25, 2019): 102. http://dx.doi.org/10.3390/land8060102.
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Uyo is one of the fastest-growing cities in Nigeria. In recent years, there has been a widespread change in land use, yet to date, there is no thorough mapping of vegetation change across the area. This study focuses on land use change, urban development, and the driving forces behind natural vegetation loss in Uyo. Based on time series Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) image data, the relationships between urban land development and its influencing factors from 1985 to 2018 were analyzed using remote sensing (RS) and time series data. The results show eight land use cover classes. Three of these (forest, swamp vegetation, and mixed vegetation) are related to natural vegetation, and three (sparse built-up, dense built-up, and borrow pit) are direct consequences of urban infrastructure development changes to the landscape. Swamp vegetation, mixed vegetation, and forest are the most affected land use classes. Thus, the rapid growth of infrastructure and industrial centers and the rural and urban mobility of labor have resulted in an increased growth of built-up land. Additionally, the growth pattern of built-up land in Uyo corresponds with socioeconomic interviews conducted in the area. Land use changes in Uyo could be attributed to changes in economic structure, urbanization through infrastructure development, and population growth. Normalized difference vegetation index (NDVI) analysis shows a trend of decreasing vegetation in Uyo, which suggests that changes in economic structure represent a key driver of vegetation loss. Furthermore, the implementation of scientific and national policies by government agencies directed at reducing the effects of urbanization growth should be strengthened, in order to calm the disagreement between urban developers and environmental managers and promote sustainable land use.
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Elhag, Mohamed, Silevna Boteva, and Nassir Al-Amri. "Forest cover assessment using remote-sensing techniques in Crete Island, Greece." Open Geosciences 13, no. 1 (January 1, 2021): 345–58. http://dx.doi.org/10.1515/geo-2020-0235.
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Abstract Remote-sensing satellite images provided rapid and continuous spectral and spatial information of the land surface in the Sougia River catchment by identifying the major changes that have taken place over 20 years (1995–2015). Vegetation indices (VIs) of normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index were derived for monitoring and mapping variations in vegetation cover. The quantified decrease in NDVI was found to be 4% between 1995 and 2005, and further decreased by 77.1% between 2005 and 2015; it declined back to almost the initial status of 1995. EVI results were inconsistent suggesting that seasonal crops influence the temporal distribution of vegetation cover. The temporal variations in the VIs were important input parameters for the modelling and management of the catchment’s hydrological behaviour. Image classification found that the 4- and the 6-class classifications between 1995 and 2005 were unstable and produced, respectively, a 13.8% and 16.2% total change between classes. Meanwhile, the 8-, 10- and the 12-class showed an almost horizontal line with a minor fluctuation of less than 0.05%. The results of the post-classification change detection analysis indicated a land degradation in terms of natural vegetation losses with sparser or even with no natural vegetation cover.
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Rocha, Andre Medeiros, Marcos Esdras Leite, and Mário Marcos do Espírito-Santo. "MONITORING OF BRAZILIAN DECIDUOUS SEASONAL FOREST BY REMOTE SENSING." Mercator 19, no. 2020 (December 15, 2020): 1–20. http://dx.doi.org/10.4215/rm2020.e19022.
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Among the many characteristics that the Brazilian territory possesses, one precisely excel: the mentioned country hosts the second biggest forest resource of the planet, corresponding for approximately 10% of the total amount of global forest resources. In that scenario, the Seasonally Dry Tropical Forests (SDTF) perform the second less expressive forest type in Brazil, being situated mostly in non-forested biomes, such as Savannas and Scrublands. Thus, its conservation must rely on its correct identification, which becomes difficult because the SDTF areas are generally classified as other vegetation types. Therefore, the present study aimed to perform the land cover-land use monitoring for the years of 2007 and 2016 of the continuous area North of Minas Gerais - South Piauí, with the purpose of evaluating the current situation of Brazilian SDTFs and assessing the main drivers that affect its deforestation and natural regeneration. As a result, the study verified that the significant increase in crop areas and spatial mobility of parturelands contributed decisively for the changes presented by vegetation formations. HOWEVER, such drivers played differentiated roles in losses/gains. Especially, it was concluded that the changes in which deciduous forests have undergone were explained particularly by pasture. The other types of vegetation were also impacted by this class, but with a more incisive participation of the crops. Key-words: Mapping, Deciduous Forests, Remote Sensing, GIS.
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Hrabalikova and Finger. "Monitoring of Carbon Sequestration in Iceland Using Remote Sensing Technology: An Overview of the LanDeg Project." Proceedings 30, no. 1 (December 24, 2019): 39. http://dx.doi.org/10.3390/proceedings2019030039.
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The monitoring of restoration and forestation is essential to reduce future drought and flood risk as well as ongoing carbon sequestration projects in Iceland. This is especially relevant for Iceland’s efforts to become carbon neutral by 2040. Such a monitoring can be done by using the state-of-art remote sensing technology, using remotely sensed data and digital mapping approaches. The LanDeg project will use free Geographic Information System (GIS) and Remote Sensing (RS) data to map soil degradation, restoration and ongoing forestation efforts to assess carbon sequestration. For this purpose, we will validate GIS and RS data analysis with field mapping of vegetation and soil cover in a restored area in southern Iceland. The validated GIS and RS analysis will be used to assess restoration efforts and trends in vegetation cover in the area. Subsequently, the changes in the vegetation cover will be used to assess the carbon sequestration rate. Based on these results we will identify best-restoration and carbon sequestration practices.
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White, Lori, Robert A. Ryerson, Jon Pasher, and Jason Duffe. "State of Science Assessment of Remote Sensing of Great Lakes Coastal Wetlands: Responding to an Operational Requirement." Remote Sensing 12, no. 18 (September 16, 2020): 3024. http://dx.doi.org/10.3390/rs12183024.
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The purpose of this research was to develop a state of science synthesis of remote sensing technologies that could be used to track changes in Great Lakes coastal vegetation for the Great Lakes-St. Lawrence River Adaptive Management (GLAM) Committee. The mapping requirements included a minimum mapping unit (MMU) of either 2 × 2 m or 4 × 4 m, a digital elevation model (DEM) accuracy in x and y of 2 m, a “z” value or vertical accuracy of 1–5 cm, and an accuracy of 90% for the classes of interest. To determine the appropriate remote sensing sensors, we conducted an extensive literature review. The required high degree of accuracy resulted in the elimination of many of the remote sensing sensors used in other wetland mapping applications including synthetic aperture radar (SAR) and optical imagery with a resolution >1 m. Our research showed that remote sensing sensors that could at least partially detect the different types of wetland vegetation in this study were the following types: (1) advanced airborne “coastal” Airborne Light Detection and Ranging (LiDAR) with either a multispectral or a hyperspectral sensor, (2) colour-infrared aerial photography (airplane) with (optimum) 8 cm resolution, (3) colour-infrared unmanned aerial vehicle (UAV) photography with vertical accuracy determination rated at 10 cm, (4) colour-infrared UAV photography with high vertical accuracy determination rated at 3–5 cm, (5) airborne hyperspectral imagery, and (6) very high-resolution optical satellite data with better than 1 m resolution.
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Launeau, Patrick, Christophe Sotin, and Jacques Girardeau. "Cartography of the Ronda peridotite (Spain) by hyperspectral remote sensing." Bulletin de la Société Géologique de France 173, no. 6 (November 1, 2002): 491–508. http://dx.doi.org/10.2113/173.6.491.
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Abstract The Ronda Peridotite, south of Andalusia (Spain), was imaged by AVIRIS in 1991 and partially sampled by us in the field with a GER 3700 spectrometer in 1997 in order to get experience in processing hyperpectral images of planetary surfaces with probes such as ISM Phobos (1989), OMEGA Mars Express (2003) and VIMS Cassini (2004). The high spectral resolution of the images (224 channels from 400 to 2455 nm) is necessary to conduct geological analysis with remote petrological determinations of rock types. On Earth, it is also necessary to determine species of vegetation because of their strong influence in mapping lithology, even in dry areas like the Ronda peridotite. The Ronda AVIRIS image was first processed to infer geological features using photo-interpretation of colour composite images extracted from 150 useful channels compared to geological maps and checked on the field during the campaign of July 97. This allows us to distinguish easily the peridotite massif from its surrounding rocks and its own serpentine zoning. Since this work followed the work of Chabrillat et al. [2000] we chose to explore the AVIRIS data with other techniques. We chose to remove the contribution of the atmosphere with spectra collected in the field on a white target at various altitudes and to remove the main vegetation with spectra of the most characteristic vegetation of the peridotite. In both cases we first estimated the amount of atmosphere and vegetation with band ratios and remove them with two similar empiric corrections of the reflectance. From the spectroscopy data, after removal of the atmosphere and some vegetation signal, we were able to clearly distinguish the crustal rocks from the mantle ones, as well as compositional variations due to pyroxene and mostly serpentine abundance within the peridotites. Hyperspectral infrared spectrometry will provide good geological mapping of the main rocks on planetary surfaces, if images can also be calibrated with in situ field measurements which will not miss any unexpected component. However, some ambiguities remain between certain types of rock which have close mineralogical composition (e.g. harzburgite compared to lherzolite) or which have resulting spectra very similar to each other (plagioclase and lizardite in peridotites). Some other ambiguities between spectra are also introduced by techniques of analysis based on relative reflectance. By not taking into account absolute intensity of the reflectance, because of roughness and topographic shading effects, small mineral variations are not always visible.
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Bernier, P. Y. "Microwave Remote Sensing of Snowpack Properties: Potential and Limitations." Hydrology Research 18, no. 1 (February 1, 1987): 1–20. http://dx.doi.org/10.2166/nh.1987.0001.
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This review explores from a user's viewpoint the possibilities and limitations of microwave-based techniques for the remote sensing of snowpack properties. Mapping of dry snowpacks and detection of melt onset can be achieved with combinations of readings taken at different frequencies with passive microwave sensors. A combination of readings from both passive and active sensors coupled with ground truth data will be required to estimate snow water equivalent under most snow conditions. Snowpack structure and overlying vegetation still present major problems in the estimation of snowpack water equivalent from microwave remote sensing devices.
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Franklin, Janet. "Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients." Progress in Physical Geography: Earth and Environment 19, no. 4 (December 1995): 474–99. http://dx.doi.org/10.1177/030913339501900403.
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Predictive vegetation mapping can be defined as predicting the geographic distribution of the vegetation composition across a landscape from mapped environmental variables. Comput erized predictive vegetation mapping is made possible by the availability of digital maps of topography and other environmental variables such as soils, geology and climate variables, and geographic information system software for manipulating these data. Especially important to predictive vegetation mapping are interpolated climatic variables related to physiological tolerances, and topographic variables, derived from digital elevation grids, related to site energy and moisture balance. Predictive vegetation mapping is founded in ecological niche theory and gradient analysis, and driven by the need to map vegetation patterns over large areas for resource conservation planning, and to predict the effects of environmental change on vegetation distributions. Predictive vegetation mapping has advanced over the past two decades especially in conjunction with the development of remote sensing-based vegetation mapping and digital geographic information analysis. A number of statistical and, more recently, machine-learning methods have been used to develop and implement predictive vegetation models.
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Shoshany, Maxim. "Satellite remote sensing of natural Mediterranean vegetation: a review within an ecological context." Progress in Physical Geography: Earth and Environment 24, no. 2 (June 2000): 153–78. http://dx.doi.org/10.1177/030913330002400201.
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Mediterrranean regions are characterized by high spatiotemporal heterogeneity of vegetation patterns. Understanding the dynamic nature of these environments requires detailed data for wide regions regarding changes in their phyto-ecology, biomass and productivity. This article assesses the current status of satellite remote sensing in this field of application. Mapping the five main life-forms (physiognomic classes) in Mediterranean regions (forests, woodlands, scrub, dwarf shrubs and herbaceous growth) has attracted major attention in recent years. Methodologies developed for this purpose are based on the spectral, temporal and spatial (textural) information domains provided by satellite data. Wide regional vegetation mapping was achieved using phenological classification of vegetation indices derived mainly from NOAA AVHRR images. More detailed mapping was conducted with multispectral techniques in local areas using mainly Landsat TM images. Assessments of multispectral and multi-temporal categories have shown limitations in their applicability over wide regions due mainly to the heterogeneity of Mediterranean regions. This heterogeneity cannot be regarded as a simple mixing of life-forms over large areas but, rather, the formation of transitional zones of varying mixtures resulting from disturbance and recovery cycles. Productivity and biomass monitoring has been found to be an active methodological development due to the introduction of new off-nadir viewing sensors in the visible and infrared spectral bands, and because of the development of methodologies for the retrieval of biophysical information from Synthetic Aperature Radar (SAR) data. Studies of ecosystem evolution using satellite data were conducted mainly in the fields of fire disturbance and desertification. Further progress in the remote sensing of Mediterranean vegetation ecology requires a better synergy of sensors, methods and ancillary data.
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Symeonakis, E., A. Korkofigkas, G. Vamvoukakis, G. Stamou, and E. Arnau-Rosalén. "DEEP LEARNING MONITORING OF WOODY VEGETATION DENSITY IN A SOUTH AFRICAN SAVANNAH REGION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1645–49. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1645-2020.
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Abstract. Bush encroachment in African savannahs has been identified as a land degradation process, mainly due to the detrimental effect it has on small pastoralist communities. Mapping and monitoring the extent covered by the woody component in savannahs has therefore become the focus of recent remote sensing-based studies. This is mainly due to the large spatial scale that the process of woody vegetation encroachment is related with and the fact that appropriate remote sensing data are now available free of charge. However, due to the nature of savannahs and the mixture of land cover types that commonly make up the signal of a single pixel, simply mapping the presence/absence of woody vegetation is somewhat limiting: it is more important to know whether an area is undergoing an increase in woody cover, ever if it is not the dominant cover type. More recent efforts have, therefore, focused in mapping the fraction of woody vegetation, which, clearly, is much more challenging. This paper proposes a methodological framework for mapping savannah woody vegetation and monitoring its evolution though time, based on very high-resolution data and multi-temporal medium-scale satellite imagery. We tested our approach in a South African savannah region, the Northwest Province (> 104,000 km2), 0.5m-pixel aerial photographs for sampling and validation and Landsat data.
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Upadhyay, P., D. Uniyal, and M. P. S. Bisht. "HYPERSPECTRAL REMOTE SENSING FOR TEMPERATE HORTICULTURE FRUIT CROPS IN NORTHERN-WESTERN HIMALAYAN REGION: A REVIEW." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 333–38. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-333-2019.
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<p><strong>Abstract.</strong> The North-Western Indian States and the North-Eastern Indian States of Indian Himalayan Region (IHR) are rich of various temperate horticulture fruits such as the Apple, Pear, Peach, Plum, Apricot, Sweet Cherry and Sour Cherry. These horticulture fruits are majorly grown in North-western region comprising of Jammu and Kashmir (J&amp;K), Himachal Pradesh (H.P.) and Uttarakhand (U.K.). These states of IHR share the same type of geographical and climatic condition and having nearly common flora and fauna. Out of the various horticulture temperate fruit crops apple and apricot have the potential to make a positive impact on economy of these states. Hyper-spectral remote sensing due to its capability of identifying the small variations within a particular feature (or land cover) is an important tool for discriminating or mapping the specific land cover among the various existing classes. Contrary to multispectral remote sensing, it is not only capable of mapping the vegetation class among the various classes in the land but also has the potential to discriminate within the different classes of vegetation as well as diseases identification within a class. This specific class level discrimination of vegetation is an important tool for mapping. In hyper-spectral remote sensing this variation is observed through the possible discrimination of spectral signatures of various vegetation classes. Thus, due to its fine spectral bands this type of remote sensing data has the potential to map the horticulture crops. However, the processing of hyper-spectral data always require the in-situ measurements or existing spectral library. Such a type of spectral library is never generated for the horticulture crops of IHR. This can be further useful for identifying the disease affected crops and input for developing model for estimation of biophysical and biochemical parameters. Therefore, in this study, a need for the development of spectral library for temperate horticulture crop has been highlighted. Further, a methodology for the processing of hyperspectral data has also be proposed.</p>
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Hongkang, Chi. "Vegetation mapping in China." Geobotanical mapping, no. 1994-1995 (1996): 55–58. http://dx.doi.org/10.31111/geobotmap/1994-1995.55.
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The article gives the brief outline of the history of vegetation mapping in China. Three periods in the development of Chinese vegetation cartography are distinguished. 1. The primary period (before 1957) is characterized by schematic, mostly regionalization, small-scale maps based on a physiognomic approach with few divisions of the legend. An example is the Vegetation Map of China at 1 : 18 000 000, published in 1957 which showed basic reguliarities of the geographic distribution of vegetation in China, its legend having included 13 numbers. 2. The period of maturation (from 1958 up to 1979). The extensive field investigations and rapid development of theory and methodology promoted the creation of some important cartographic works. The most significant of them are: Vegetation Map of China at 1 : 10 000 000 (Hou Hsiohyu et al., 1965), Vegetation Regionalization Map of China (Hou Hsiohyu, 1965), Vegetation Map of People s Republic of China at 1 : 4 000 000 (Hou Hsiohyu at al., 1980). These maps are notable for a great deal of various data involved, the hierarchic complex legends of almost two hundred divisions, the application of some new scientific approaches: showing the latitudinal, longitudinal and altitudinal differentiation, the edaphic variation of and the dynamic phenomena in vegetation. In this period some regional maps were published as well: vegetation maps of North-East China at 1 : 500 000 and of Zun Ge Er at 1 : 2 000 000. 3. The period of rapid and intensive development (after 1980). Owing to remote sensing technique numerous small-scale maps were prepared and published. The basic one is the Vegetation Map of China at 1 : 1 000 000. For its preparing a great deal of field materials, satellite images and literature data were involved. About 300 researches took part in this work.
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Nouri, Hamideh, Sattar Chavoshi Borujeni, Sina Alaghmand, Sharolyn Anderson, Paul Sutton, Somayeh Parvazian, and Simon Beecham. "Soil Salinity Mapping of Urban Greenery Using Remote Sensing and Proximal Sensing Techniques; The Case of Veale Gardens within the Adelaide Parklands." Sustainability 10, no. 8 (August 9, 2018): 2826. http://dx.doi.org/10.3390/su10082826.
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More well-maintained green spaces leading toward sustainable, smart green cities mean that alternative water resources (e.g., wastewater) are needed to fulfill the water demand of urban greenery. These alternative resources may introduce some environmental hazards, such as salt leaching through wastewater irrigation. Despite the necessity of salinity monitoring and management in urban green spaces, most attention has been on agricultural fields. This study was defined to investigate the capability and feasibility of monitoring and predicting soil salinity using proximal sensing and remote sensing approaches. The innovation of the study lies in the fact that it is one of the first research studies to investigate soil salinity in heterogeneous urban vegetation with two approaches: proximal sensing salinity mapping using Electromagnetic-induction Meter (EM38) surveys and remote sensing using the high-resolution multispectral image of WorldView3. The possible spectral band combinations that form spectral indices were calculated using remote sensing techniques. The results from the EM38 survey were validated by testing soil samples in the laboratory. These findings were compared to remote sensing-based soil salinity indicators to examine their competence on mapping and predicting spatial variation of soil salinity in urban greenery. Several regression models were fitted; the mixed effect modeling was selected as the most appropriate to analyze data, as it takes into account the systematic observation-specific unobserved heterogeneity. Our results showed that Soil Adjusted Vegetation Index (SAVI) was the only salinity index that could be considered for predicting soil salinity in urban greenery using high-resolution images, yet further investigation is recommended.
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Rapinel, Sébastien, and Laurence Hubert-Moy. "One-Class Classification of Natural Vegetation Using Remote Sensing: A Review." Remote Sensing 13, no. 10 (May 12, 2021): 1892. http://dx.doi.org/10.3390/rs13101892.
Full textAbstract:
Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities.