Use of lidar for monitoring vegetation growth dynamics in reclaimed mine lands in Kentucky

IF 3.8 Q2 ENVIRONMENTAL SCIENCES Remote Sensing Applications-Society and Environment Pub Date : 2024-06-20 DOI:10.1016/j.rsase.2024.101277
Kabita Paudel , Buddhi Gyawali , Demetrio P. Zourarakis , Maheteme Gebremedhin , Shawn T. Lucas
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Abstract

Surface coal mining in the Appalachian region has led to a significant forest disturbance over time. Evaluating the effectiveness of current reclamation practices in promoting vegetation growth on reclaimed mine sites is a key to understanding how much vegetation has changed in those sites since reclamation. This study employed statewide airborne lidar data to assess changes in lidar vegetation structural metrics on reclaimed mine lands in the Lower Levisa Watershed of Eastern Kentucky between 2011 and 2019 and compare vegetation growth at various reclaimed sites reclaimed in different decades. Eighteen inactive surface mines were selected for the study and categorized into four groups based on the release of their reclamation bonds in different decades. Lidar point cloud data were processed in ArcGIS Pro using filtering and segmentation algorithms to calculate various vegetation attributes from the point clouds, including maximum vegetation height (Hmax), mean height (Hmean), standard deviation of height (HSD), canopy cover (CC), and height percentiles (10, 50 and 75), which were represented as lidar metrics. The process of generating the lidar metrics involved creating Digital Elevation Models (DEMs) and Digital Surface Models (DSMs), calculating Canopy Height Models (CHMs), creating LAS height metrics and generating point statistics rasters to derive these metrics. Change maps for each metric were visually assessed over time, and circular plots with a radius of 12 m were established within each site for further statistical analysis. Significant changes in lidar vegetation metrics were observed between 2011 and 2019 with significant differences among sites reclaimed at different time periods. There was an overall increase in Hmean from 2011 to 2019, with values ranging from 2.4 to 3.8 m. Sites reclaimed in the 1980s experienced an average decrease in canopy cover of −0.5%, while those from the 1990s, 2000s, and 2010s demonstrated increases of 4.9%, 10.1%, and 18.1%, respectively, suggesting that canopy growth rates are higher in younger sites compared to older ones. Vertical variability of the vegetation also increased over time, as indicated by increasing HSD values. Utilizing statewide airborne lidar data allowed for a comprehensive and detailed assessment of vegetation dynamics on reclaimed mine lands. The findings of this study serve as a foundation for future research endeavors focused on vegetation recovery assessment and success in reclaimed mine lands using lidar data.

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利用激光雷达监测肯塔基州开垦矿区的植被生长动态
随着时间的推移,阿巴拉契亚地区的露天采煤导致了严重的森林干扰。评估当前复垦措施在促进复垦矿区植被生长方面的有效性,是了解这些矿区自复垦以来植被变化程度的关键。本研究采用全州范围的机载激光雷达数据,评估 2011 年至 2019 年期间肯塔基州东部下勒维萨流域复垦矿区激光雷达植被结构指标的变化,并比较不同年代复垦矿区的植被生长情况。研究选取了 18 个不活跃的地表矿山,并根据其在不同年代释放复垦债券的情况将其分为四组。激光雷达点云数据在 ArcGIS Pro 中使用过滤和分割算法进行处理,以计算点云中的各种植被属性,包括最大植被高度(Hmax)、平均高度(Hmean)、高度标准偏差(HSD)、冠层覆盖(CC)和高度百分位数(10、50 和 75),并将其表示为激光雷达度量。生成激光雷达度量的过程包括创建数字高程模型(DEM)和数字表面模型(DSM)、计算树冠高度模型(CHM)、创建 LAS 高度度量以及生成点统计栅格以得出这些度量。对每项指标随时间的变化图进行目测评估,并在每个地点建立半径为 12 米的圆形地块,以进一步进行统计分析。2011 年至 2019 年期间,激光雷达植被指标发生了显著变化,不同时间段开垦的地点之间差异显著。20 世纪 80 年代开垦的地点的冠层覆盖率平均下降了-0.5%,而 20 世纪 90 年代、2000 年代和 2010 年代开垦的地点的冠层覆盖率分别增加了 4.9%、10.1% 和 18.1%,这表明较年轻地点的冠层生长率高于较老的地点。植被的垂直变异性也随着时间的推移而增加,HSD 值的增加就表明了这一点。利用全州范围的机载激光雷达数据,可以对复垦矿区的植被动态进行全面而详细的评估。这项研究的结果为今后利用激光雷达数据开展植被恢复评估和矿区复垦成功与否的研究奠定了基础。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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