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评估1992年至2023年土地利用变化导致的森林破碎化:基于遥感数据的时空分析

Assessing forest fragmentation due to land use changes from 1992 to 2023: A spatio-temporal analysis using remote sensing data.

作者信息

Hussain Khadim, Mehmood Kaleem, Anees Shoaib Ahmad, Ding Zhidan, Muhammad Sultan, Badshah Tariq, Shahzad Fahad, Haidar Ijlal, Wahab Abdul, Ali Jamshid, Ansari Mohammad Javed, Salmen Saleh H, Yujun Sun, Khan Waseem Razzaq

机构信息

State Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing, 100083, China.

Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing, 100083, China.

出版信息

Heliyon. 2024 Jul 16;10(14):e34710. doi: 10.1016/j.heliyon.2024.e34710. eCollection 2024 Jul 30.

Abstract

The increasing pressures of urban development and agricultural expansion have significant implications for land use and land cover (LULC) dynamics, particularly in ecologically sensitive regions like the Murree and Kotli Sattian tehsils of the Rawalpindi district in Pakistan. This study's primary objective is to assess spatial variations within each LULC category over three decades (1992-2023) using cross-tabulation in ArcGIS to identify changes in LULC and investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0) to classify forest into several classes such as patch, edge, perforated, small core, medium core, and large core. Utilizing remote sensing data from Landsat 5 and Landsat 9 satellites, the research focuses on the temporal dynamics in various land classes including Coniferous Forest (CF), Evergreen Forest (EF), Arable Land (AR), Buildup Area (BU), Barren Land (BA), Water (WA), and Grassland (GL). The Support Vector Machine (SVM) classifier and ArcGIS software were employed for image processing and classification, ensuring accuracy in categorizing different land types. Our results indicate a notable reduction in forested areas, with Coniferous Forest (CF) decreasing from 363.9 km, constituting 45.0 % of the area in 1992, to 291.5 km (36.0 %) in 2023, representing a total decrease of 72.4 km. Similarly, Evergreen Forests have also seen a significant reduction, from 177.9 km (22.0 %) in 1992 to 99.8 km (12.3 %) in 2023, a decrease of 78.1 km. The study investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0), revealing an increase in fragmentation and a decrease in large core forests from 20.3 % of the total area in 1992 to 7.2 % in 2023. Additionally, the patch forest area increased from 2.4 % in 1992 to 5.9 % in 2023, indicating significant fragmentation. Transition matrices and a Sankey diagram illustrate the transitions between different LULC classes, providing a comprehensive view of the dynamics of land-use changes and their implications for ecosystem services. These findings highlight the critical need for robust conservation strategies and effective land management practices. The study contributes to the understanding of LULC dynamics and forest fragmentation in the Himalayan region of Pakistan, offering insights essential for future land management and policymaking in the face of rapid environmental changes.

摘要

城市发展和农业扩张带来的压力与日俱增,对土地利用和土地覆盖(LULC)动态变化产生了重大影响,尤其是在巴基斯坦拉瓦尔品第区穆里和科特利萨蒂安两个乡这样的生态敏感地区。本研究的主要目标是利用ArcGIS中的交叉制表法评估三个十年(1992 - 2023年)内每个LULC类别中的空间变化,以识别LULC的变化,并使用景观破碎化工具(LFTv2.0)进行森林破碎化分析,将森林分为斑块、边缘、穿孔、小核心、中核心和大核心等几类。该研究利用Landsat 5和Landsat 9卫星的遥感数据,聚焦于包括针叶林(CF)、常绿林(EF)、耕地(AR)、建成区(BU)、裸地(BA)、水体(WA)和草地(GL)在内的各种土地类型的时间动态变化。采用支持向量机(SVM)分类器和ArcGIS软件进行图像处理和分类,确保对不同土地类型分类的准确性。我们的结果表明,森林面积显著减少,针叶林(CF)从1992年的363.9平方千米(占该地区面积的45.0%)减少到2023年的291.5平方千米(36.0%),总共减少了72.4平方千米。同样,常绿林也大幅减少,从1992年的177.9平方千米(22.0%)降至2023年的99.8平方千米(12.3%),减少了78.1平方千米。该研究使用景观破碎化工具(LFTv2.0)进行森林破碎化分析,结果显示破碎化加剧,大核心森林占总面积的比例从1992年的20.3%降至2023年的7.2%。此外,斑块森林面积从1992年的2.4%增加到2023年的5.9%,表明破碎化显著。转移矩阵和桑基图展示了不同LULC类别之间的转变,全面呈现了土地利用变化的动态及其对生态系统服务的影响。这些发现凸显了制定强有力的保护策略和有效的土地管理措施的迫切需求。该研究有助于理解巴基斯坦喜马拉雅地区的LULC动态变化和森林破碎化情况,为面对快速环境变化时的未来土地管理和政策制定提供了至关重要的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3de7/11325051/a43a2fc33a3d/gr1.jpg

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