Despite decades of concerted global conservation efforts, biodiversity loss continues unabated, making it important to assess the effectiveness of conservation approaches. Using forest cover as a proxy for conservation effectiveness, we analysed land-use and land-cover changes across a community and a state forest of Jaintia Hills, Meghalaya, India. Forest losses in the community lands (77.94 sq. km) were higher compared to the state forest (11.48 sq. km) between 1994 and 2014, and were driven by mining, industry, plantations and agriculture. We examined the role of policies and institutional arrangements as larger drivers of forest change within the context of conservation effectiveness.

Desertification is one of the major threats to the environment and the global community. Changing climate, deforestation, changing agriculture methods and increasing demand for resources are important drivers of desertification. Due to dependency of human life on land, monitoring and mitigating the desertification effect is getting more attention over the years. The main objective of the study was to assess the impact of natural factors on desertification process on a regional level by using the remotely sensed parameters like TRMM -precipitation, MODIS -Evapotranspiration (PET), MODIS -Net Primary Productivity (NPP), and OPEN ACCESSASTER-DEM with the aid of AHP -GIS model. From the above mentioned sensor parameters, the indices like aridity, precipitation, Rain Use Efficiency (RUE), NPP and slope are prepared for 2000-2012. For analytical purpose, the TRMM data were downscaled to 1km spatial resolution to match the spatial scale of the other parameters. All the parameters were classified using the frequency distribution. Based on the classes, ranks were assigned. The weight of the parameters were assigned based upon the expert's opinions gathered through a questionnaire-based survey. We converted each data into an annual scale for time series analysis. Results showed that 18.17% of the state is characterized by highly sensitive to desertification which is the southeast part of the state. 9.26% comes under the very high sensitive area. Most of the area comes under the moderate sensitive area (63.93%) and 3.84% comes under the low sensitive area. The very low sensitive area exhibits only 4.8 %, which is the hilly region of the study area. We discuss the impact of climatic, vegetation and topography parameters on desertification in the study area.

Urban Tree Canopy (UTC) is an important asset in the urban ecological system by reducing the heat, runoff and improving air quality. Estimating the available urban tree canopy is important for decision makers for better understanding of urban ecosystems and helps to improve environmental quality and human health in urban areas and plan conservation activities. This paper reports on an object-oriented tree extraction method developed by using spectral and textural information of High-Resolution (HR) aerial imagery and normalised Digital Surface Model (nDSM) information derived from Light Detection and Ranging (LIDAR) data. The image was segmented by edge-based segmentation algorithm and classified using the Support Vector machine (SVM) algorithm. The results showed that the proposed object-oriented method provides better classification with an overall accuracy of 88%.

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