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The systematic decline of land quality due to the mismatch between land quality and land use is called land degradation. It is the consequences of different physical factors but accelerated by human activities. The processes and factors of land degradation are different in different morpho-climatic regions of the world. Present study is primarily concerned in identifying the factors and status of land degradation in a tropical plateau region. Tropical plateaus are one of the most water erosion related land degraded areas of world. Chotanagpur plateau in tropical eastern India is one of the vulnerable soil degraded areas of the tropical environment. The Precambrian geological formation, undulating topography, immature soil, increasing aridity sets natural settings for land degradation in tropical plateau areas. However, induced degradation results from inappropriate land use, clearance of forest land and heavy land pressure related anthropogenic factors accelerates the processes of land degradation. Kangsabati River basin of eastern Chotanagpur plateau bears all of the above characteristics has been taken for present study as a degraded tropical plateau region. Water erosion and vegetal degradation are the major factors of land degradation in Kangsabati basin. The physical factors responsible for land degradation in the studied area includes physiographic formations, edaphic maturity, hydrologic characteristics, and vegetation cover dynamics etc. This land degradation processes is accelerated by different anthropogenic factors like, land use land cover dynamics, deforestation, agricultural pressure. Multispectral remote sensing data, digital elevation data, thematic maps, recorded climatological data, secondary data and field observation have been utilised to understand the different factors of land degradation in a typical tropical plateau region. The less developed soil profile over Precambrian geology with undulated landforms causes high soil erosion in upper plateau proper regions of the basin. High agricultural pressure, forest and groundwater decline with associated land use land cover dynamics creates decline of land quality in lower basin regions of tropical plateau. The varying intensities of land degraded areas for the basin has been identified through the multiplication of different physical and anthropogenic factor overlaying rasters in GIS environment. Before multiplying, each of these input factor rasters has assigned with weightage after considerable importance of land degradation. The lower basin area with accelerated human interference is the most vulnerable land degraded area for the Kangsabati River basin. [ABSTRACT FROM AUTHOR]

The Zagros forests in Western Iran are valuable ecosystems that have been seriously damaged by human interference (harvesting the wood and forest sub-products, converting the forests to the agricultural lands, and grazing) and natural events (drought events and fire). In this study, we generated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests of Shirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field data for protective management. First, we assessed the accuracy of Google Earth data using 300 random field plots in 10 different land cover types. For land cover mapping, we evaluated the performance of four supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD), neural network (NN), and support vector machine (SVM)). The accuracy of the land cover maps was assessed using a set of 150 stratified random plots in Google Earth. We mapped the forest canopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. We calculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots). The linear regression between the NDVI values and the TCC% was used to obtain the predictive model of TCC% based on the NDVI. The results showed that Google Earth data yielded an overall accuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopy cover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, which is highly significant. The results also showed that the linear regression model is a good predictive model for TCC% estimation based on the NDVI (r2 = 0.864). The results can be used as a baseline for decision-makers to monitor land cover change in the region, whether produced by human activities or natural events and to establish measures for protective management of forests. [ABSTRACT FROM AUTHOR]

Land cover changes, especially excessive economic forest plantations, have significantly threatened the ecological security of West Dongting Lake wetland in China. This work aimed to investigate the spatiotemporal dynamics of forests in the West Dongting Lake region from 2000 to 2018 using a reconstructed monthly Landsat NDVI time series. The multi-type forest changes, including conversion from forest to another land cover category, conversion from another land cover category to forest, and conversion from forest to forest (such as flooding and replantation post-deforestation), and land cover categories before and after change were effectively detected by integrating Breaks For Additive Seasonal and Trend (BFAST) and random forest algorithms with the monthly NDVI time series, with an overall accuracy of 87.8%. On the basis of focusing on all the forest regions extracted through creating a forest mask for each image in time series and merging these to produce an 'anytime' forest mask, the spatiotemporal dynamics of forest were analyzed on the basis of the acquired information of multi-type forest changes and classification. The forests are principally distributed in the core zone of West Donting Lake surrounding the water body and the southwestern mountains. The forest changes in the core zone and low elevation region are prevalent and frequent. The variation of forest areas in West Dongting Lake experienced three steps: rapid expansion of forest plantation from 2000 to 2005, relatively steady from 2006 to 2011, and continuous decline since 2011, mainly caused by anthropogenic factors, such as government policies and economic profits. This study demonstrated the applicability of the integrated BFAST method to detect multi-type forest changes by using dense Landsat time series in the subtropical wetland ecosystem with low data availability. [ABSTRACT FROM AUTHOR]