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Bio-D – Solvency – Deforestation
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- Bio-D – Solvency – Land Management
Bio-D – Solvency – DeforestationLandsat key to monitoring deforestation Joseph et al 9 (S. Joseph · B. Gharai · S. Sudhakar · M. S. R. Murthy Forestry and Ecology Division, National Remote Sensing Agency, S. Joseph, GIS Centre, Department of Physical Geography and Ecosystem Analysis, Lund University, G. A. Blackburn Department of Geography, Lancaster University, S. Joseph · A. P. Thomas School of Environmental Sciences, Mahatma Gandhi University, Environ Monit Assess 158:169–179, http://www.lancs.ac.uk/staff/blackbga/Joseph%20et%20al%20EMAS%202009%20printed%20version.pdf, accessed 7-6-11, JMB) Land cover change is one of the most critical dynamic elements of ecosystems. Tropical forests, which play critical roles as repositories of biological diversity and regulators of global biogeochemical and hydrological cycles (Houghton 1999; Cairns et al. 2000; Myers et al. 2000) have undergone rapid land cover changes especially in the last few decades. (Bockstael et al. 1995; Pijanowski et al. 2000). Global estimates show that deforestation in the tropics during 1990–2000 was 14.2 million ha per year while reforestation was 1.9 million ha, which resulted in a net loss of 12.3 million ha of forest per year (FAO 2001). South Asia experienced a negative rate (0.13% per annum) of forest cover change, which was approximately half the negative rate of change in the world (0.22% per annum) and double the negative rate of change for the whole Asian region (0.07% per annum). These trends point out the prevalence of complex and multidirectional changes in forest cover dynamics which could be attributed to local level management measures. Remote sensing offers an important means of detecting and analyzing temporal changes and since the early 1970s satellite data have been commonly used for change detection studies (Jensen et al. 1993). The use of remotely sensed data for monitoring tropical deforestation and assessing the drivers of deforestation has been operationalised by a range of programs. Noteworthy programs include NASA’s (National Aeronautics and Space Administration) Landsat Pathfinder Project on Deforestation in the Humid Tropics (Townshend et al. 1995; Kalluri et al. 2001) and the TREES (Tropical Ecosystem Environment Observations by Satellite) project (Stibig and Achard 2003). Such work has demonstrated that satellite remote sensing can provide satisfactory results for regional forest cover mapping and for obtaining up-to-date and uniform estimates of the total forest area in a region. Furthermore, a range of change detection techniques have been developed for monitoring land cover dynamics from remotely sensed imagery (see reviews by Coppin et al. 2004; Lu et al. 2004). Such techniques have been used to explore the relationships between shifts in vegetation patterns and factors such as human activities, natural disturbances and topography (Turner et al. 1996; Cohen et al. 2002). Moreover, remote monitoring of deforestation as well as successional regrowth has yielded valuable insights into processes such as the dynamism of ecotones, rates of succession, and invasion of weeds, which, in turn has provided substantial evidence concerning the drivers of land cover change (Nelson and Holben 1986; Sader et al. 1990; Mausel et al. 1993). Bio-D – Solvency – Land ManagementRemote-sensing key to improving land management strategies Joseph et al 9 (S. Joseph · B. Gharai · S. Sudhakar · M. S. R. Murthy Forestry and Ecology Division, National Remote Sensing Agency, S. Joseph, GIS Centre, Department of Physical Geography and Ecosystem Analysis, Lund University, G. A. Blackburn Department of Geography, Lancaster University, S. Joseph · A. P. Thomas School of Environmental Sciences, Mahatma Gandhi University, Environ Monit Assess 158:169–179, http://www.lancs.ac.uk/staff/blackbga/Joseph%20et%20al%20EMAS%202009%20printed%20version.pdf, accessed 7-6-11, JMB) This study has demonstrated that remotely-sensed based assessments of land cover dynamics can have an important contribution to monitoring the consequences of land management strategies and deepening our understanding of the processes that underpin land use changes. The vegetation type map of the Indira Gandhi Wildlife Sanctuary derived from IRS P6 LISS III data showed that the area is currently dominated by deciduous and evergreen forests. Land cover change assessment for a period of 33 years helped to identify the rates and characteristics of land cover transformations. Two major and divergent trends, degradational and successional, were observed in the study. The degradational trend was indicated by the transformation of undisturbed forest to disturbed forest and other non-forest categories. These changes can be attributed to a number of causes, principally livelihood dependence, agricultural expansion and infrastructure development resulting from population growth in and around the area and uncoordinated policies of the different government agencies. The positive successional changes resulting from protection of the area showed the resilience of the system even after prolonged disturbances on vegetation cover. The observed degradational transitions exceed the rates of successional changes. Hence, the sanctuary appears susceptible to continuing disturbances under the current management regime, however, the impacts of such processes are substantially lower than in surrounding unprotected areas. Remote sensing is key to land management Glenn, Nagler and Huete 10 (E.P., Univ. AZ, P.L., USGS, A.R., Dept. Ag, AZ, http://www.springerlink.com/content/w17411820146j015/, accessed 7/7/11) CJQ Estimates of terrestrial evapotranspiration (ET) are needed for land management tasks at local, regional and continental scales of measurement, and to project potential changes in the global hydrological cycle under different climate change scenarios (e.g., Allen 2005; Teuling et al. 2009). Remote sensing is perhaps the only feasible means of estimating ET over wide areas of mixed landscape types, typical of applications for which regional water budgets are required. Some of the specific tasks for which remote sensing estimates of ET are used are: determining consumptive water use by crops in irrigation districts, to construct district-wide water budgets (e.g., United States Bureau of Reclamation 2009); refining crop coefficients for individual crops to match local conditions (e.g., Hunsaker et al. 2007); characterizing water use patterns of plants in natural ecosystems in ecological studies (e.g., Groeneveld et al. 2007; Dennison et al. 2009); developing wide-area estimates of ET to construct catchment water budget models (e.g., Guerschman et al. 2009); and scaling flux tower measurements of ET and carbon exchange over biomes and continents, to be used in climate change studies (e.g., Leuning et al. 2008; Fisher et al. 2008). In most of these applications, time-series satellite imagery is used to project ET over spatial and temporal scales that cannot be achieved by point estimates of ET measured on the ground. 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