Academic literature on the topic 'Deforestation Forests and forestry Remote sensing'

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.

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.

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.

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.

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.