Microtopography-Guided precision restoration of sandy lands through UAV: A case study in Hunshandake Sandy Land, China

IF 5.4 1区 农林科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Catena Pub Date : 2024-10-24 DOI:10.1016/j.catena.2024.108489
Wenhe Chen , Weicheng Sun , Zhisheng Wu , Yaobin Wang , Yang Wang , Yongfei Bai , Yujin Zhao
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Abstract

Increasing desertification rates have adversely affected biodiversity and ecosystem functioning in sandy lands. Ecological restoration is an effective way to combat desertification. Specific microtopographic characteristics may facilitate vegetation growth, enhancing the success of restoration efforts. However, limited research to date has explored how microtopography may guide the “precision restoration” of sandy lands by supporting spatially continuous patterns of vegetation growth. Here, high-resolution unmanned aerial vehicle (UAV) multispectral data were used to identify individual species and extract microtopographic variables, and the relationships between vegetation growth and microtopography were characterized for the Hunshandake Sandy Land in China. The distribution of three dominant shrub species and grasses was investigated by comparing the performance of five popular machine-learning methods. An auto-marking watershed algorithm was then developed to discriminate individual semi-shrubs (Artemisia desertorum). Finally, a new vegetation growth index (VGI), calculated from the UAV-derived crown area, normalized difference vegetation index (NDVI), and canopy height, was used to characterize the relationships between vegetation growth and several microtopographic variables (aspect, slope, and a topographic wetness index [TWI]). With the highest species classification accuracy (94.36%) and individual discrimination rate (81%), areas with high humidity (TWI), gentle and shady slopes were found to most strongly support vegetation growth; for grasses, VGI was lower in artificially-restored regions than naturally-developed regions with similar microtopographic characteristics. These findings provide valuable guidance on the use of UAV to support diverse ecological restoration solutions in sandy lands with a “precision restoration” strategy, thereby improving the survival of key vegetation for sand restoration, especially in remote areas.
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微地形--无人机引导下的沙地精准修复:中国浑善达克沙地案例研究
荒漠化率的上升对沙地的生物多样性和生态系统功能产生了不利影响。生态恢复是防治荒漠化的有效方法。特定的微地形特征可促进植被生长,提高恢复工作的成功率。然而,迄今为止,有关微地形如何通过支持植被生长的空间连续模式来指导沙地 "精准修复 "的研究还很有限。本文利用高分辨率无人机(UAV)多光谱数据识别单个物种并提取微地形变量,描述了中国浑善达克沙地植被生长与微地形之间的关系。通过比较五种常用机器学习方法的性能,研究了三种优势灌木物种和禾本科植物的分布。然后开发了一种自动标记分水岭算法,用于区分单个半灌木(荒漠蒿)。最后,利用从无人机获取的树冠面积、归一化差异植被指数(NDVI)和树冠高度计算出的新植被生长指数(VGI)来描述植被生长与几个微地形变量(高程、坡度和地形湿润指数[TWI])之间的关系。结果发现,高湿度(TWI)、缓坡和阴坡地区对植被生长的支持力度最大,物种分类准确率(94.36%)和个体识别率(81%)最高;对于禾本科植物而言,人工恢复地区的 VGI 低于具有类似微地形特征的自然发育地区。这些发现为利用无人机支持沙地生态恢复的多样化解决方案提供了宝贵的指导,该方案采用 "精准恢复 "策略,从而提高沙地恢复关键植被的存活率,尤其是在偏远地区。
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来源期刊
Catena
Catena 环境科学-地球科学综合
CiteScore
10.50
自引率
9.70%
发文量
816
审稿时长
54 days
期刊介绍: Catena publishes papers describing original field and laboratory investigations and reviews on geoecology and landscape evolution with emphasis on interdisciplinary aspects of soil science, hydrology and geomorphology. It aims to disseminate new knowledge and foster better understanding of the physical environment, of evolutionary sequences that have resulted in past and current landscapes, and of the natural processes that are likely to determine the fate of our terrestrial environment. Papers within any one of the above topics are welcome provided they are of sufficiently wide interest and relevance.
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