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Modelling Spatial and Temporal Forest Cover Change Patterns (1973-2020): A Case Study from South Western Ghats (India)

机译:时空森林覆盖变化模式建模(1973-2020年):来自西南高止山脉的案例研究(印度)

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摘要

This study used time series remote sensing data from 1973, 1990 and 2004 to assess spatial forest cover change patterns in the Kalakad-Mundanthurai Tiger Reserve (KMTR), South Western Ghats (India). Analysis of forest cover changes and its causes are the most challenging areas of landscape ecology, especially due to the absence of temporal ground data and comparable space platform based data. Comparing remotely sensed data from three different sources with sensors having different spatial and spectral resolution presented a technical challenge. Quantitative change analysis over a long period provided a valuable insight into forest cover dynamics in this area. Time-series maps were combined within a geographical information system (GIS) with biotic and abiotic factors for modelling its future change. The land-cover change has been modelled using GEOMOD and predicted for year 2020 using the current disturbance scenario. Comparison of the forest change maps over the 31-year period shows that evergreen forest being degraded (16%) primarily in the form of selective logging and clear felling to raise plantations of coffee, tea and cardamom. The natural disturbances such as forest fire, wildlife grazing, invasions after clearance and soil erosion induced by anthropogenic pressure over the decades are the reasons of forest cover change in KMTR. The study demonstrates the role of remote sensing and GIS in monitoring of large-coverage of forest area continuously for a given region over time more precisely and in cost-effective manner which will be ideal for conservation planning and prioritization.
机译:这项研究使用了1973年,1990年和2004年的时间序列遥感数据评估了西南高止山脉(印度)的Kalakad-Mundanthurai老虎保护区(KMTR)的空间森林覆盖变化模式。对森林覆盖率变化及其成因的分析是景观生态学中最具挑战性的领域,尤其是由于缺乏时空地面数据和可比较的基于空间平台的数据。将来自三个不同来源的遥感数据与具有不同空间和光谱分辨率的传感器进行比较提出了一项技术挑战。长期的定量变化分析为该地区的森林覆盖动态提供了宝贵的见解。将时间序列图与具有生物和非生物因素的地理信息系统(GIS)组合在一起,以建模其未来变化。利用GEOMOD对土地覆盖变化进行了建模,并使用当前的干扰情景对2020年进行了预测。通过对31年间森林变化图的比较,可以看出,常绿森林的退化(16%)主要是通过选择性伐木和砍伐森林来种植咖啡,茶和小豆蔻的方式。几十年来,诸如森林火灾,野生动植物放牧,清除后的入侵和人为压力引起的水土流失等自然干扰是KMTR森林覆盖率发生变化的原因。该研究表明,随着时间的推移,遥感和地理信息系统在给定区域连续更精确地以成本有效的方式连续监测大面积森林面积的作用,这对于进行保护规划和确定优先次序是理想的。

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