Relationships between vegetation indices and surface reflectance: Implications for detecting and monitoring sandification in arid regions
Terminal lakes in arid regions are increasingly vulnerable to sandification under water scarcity and climate stress. Taking the Qingtu Lake region in China’s Shiyang River Basin as a case study, we evaluated different vegetation indices and surface parameter combinations to identify optimal monitori...
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Elsevier
2025-07-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25005709 |
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| author | Yifan Yue Wenzhi Zhao Rentao Liu |
| author_facet | Yifan Yue Wenzhi Zhao Rentao Liu |
| author_sort | Yifan Yue |
| collection | DOAJ |
| description | Terminal lakes in arid regions are increasingly vulnerable to sandification under water scarcity and climate stress. Taking the Qingtu Lake region in China’s Shiyang River Basin as a case study, we evaluated different vegetation indices and surface parameter combinations to identify optimal monitoring model, analyzing the spatiotemporal dynamics of sandification and primary driving factors using long-term remote sensing data (2000–2023). The NDVI–albedo combination outperformed other index–parameter combinations in the feature space models (FSMs), achieving an overall classification accuracy of 88.55 %. This superior combination’s temporal trends exhibited strong inverse relationships, with 70 % of pixels having significant negative correlations between NDVI and albedo. The model effectively captured fine-scale spatial details of sandification levels with high ground truth consistency compared to other tested models. The regional sandification patterning revealed a distinct “transformation-differentiation” dimension in 2000–2023. Temporally, sandification intensity has greatly declined, with the area of extremely severe sandification shrinking from 2282 to 377 km2; spatially, sandification has occurred along a pronounced northeast–southwest gradient. Climate factors persistently imposed significant negative effects on sandification dynamics over past the two decades, whereas the direct influence of human activities showed a marked increase from 0.18 to 0.38. Soil factors functioned as key mediating variables by integrating climate and human influences, while geographical factors exhibited minimal contribution to the overall model (direct effects < 0.1). In conclusion, this study provided a reliable technical framework to better quantitatively assess wetlands’ sandification, thus bolstering essential information for developing targeted prevention and control strategies in arid regions. |
| format | Article |
| id | doaj-art-c8e446a44aea4ce48b6e4bfb9a22ba84 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
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| series | Ecological Indicators |
| spelling | doaj-art-c8e446a44aea4ce48b6e4bfb9a22ba842025-08-20T03:30:38ZengElsevierEcological Indicators1470-160X2025-07-0117611364010.1016/j.ecolind.2025.113640Relationships between vegetation indices and surface reflectance: Implications for detecting and monitoring sandification in arid regionsYifan Yue0Wenzhi Zhao1Rentao Liu2College of Forestry and Prataculture, Ningxia University, Yinchuan 750021, ChinaNorthwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; Corresponding authors at: Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China (W. Zhao). Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration of Northwestern China, Yinchuan 750021, China (R. Liu).Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration of Northwestern China, Yinchuan 750021, China; College of Ecology and Environment, Ningxia University, Yinchuan 750021, China; Corresponding authors at: Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China (W. Zhao). Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration of Northwestern China, Yinchuan 750021, China (R. Liu).Terminal lakes in arid regions are increasingly vulnerable to sandification under water scarcity and climate stress. Taking the Qingtu Lake region in China’s Shiyang River Basin as a case study, we evaluated different vegetation indices and surface parameter combinations to identify optimal monitoring model, analyzing the spatiotemporal dynamics of sandification and primary driving factors using long-term remote sensing data (2000–2023). The NDVI–albedo combination outperformed other index–parameter combinations in the feature space models (FSMs), achieving an overall classification accuracy of 88.55 %. This superior combination’s temporal trends exhibited strong inverse relationships, with 70 % of pixels having significant negative correlations between NDVI and albedo. The model effectively captured fine-scale spatial details of sandification levels with high ground truth consistency compared to other tested models. The regional sandification patterning revealed a distinct “transformation-differentiation” dimension in 2000–2023. Temporally, sandification intensity has greatly declined, with the area of extremely severe sandification shrinking from 2282 to 377 km2; spatially, sandification has occurred along a pronounced northeast–southwest gradient. Climate factors persistently imposed significant negative effects on sandification dynamics over past the two decades, whereas the direct influence of human activities showed a marked increase from 0.18 to 0.38. Soil factors functioned as key mediating variables by integrating climate and human influences, while geographical factors exhibited minimal contribution to the overall model (direct effects < 0.1). In conclusion, this study provided a reliable technical framework to better quantitatively assess wetlands’ sandification, thus bolstering essential information for developing targeted prevention and control strategies in arid regions.http://www.sciencedirect.com/science/article/pii/S1470160X25005709Terminal lakesLand sandificationFeature space modelSpatiotemporal dynamicsDriving factors |
| spellingShingle | Yifan Yue Wenzhi Zhao Rentao Liu Relationships between vegetation indices and surface reflectance: Implications for detecting and monitoring sandification in arid regions Ecological Indicators Terminal lakes Land sandification Feature space model Spatiotemporal dynamics Driving factors |
| title | Relationships between vegetation indices and surface reflectance: Implications for detecting and monitoring sandification in arid regions |
| title_full | Relationships between vegetation indices and surface reflectance: Implications for detecting and monitoring sandification in arid regions |
| title_fullStr | Relationships between vegetation indices and surface reflectance: Implications for detecting and monitoring sandification in arid regions |
| title_full_unstemmed | Relationships between vegetation indices and surface reflectance: Implications for detecting and monitoring sandification in arid regions |
| title_short | Relationships between vegetation indices and surface reflectance: Implications for detecting and monitoring sandification in arid regions |
| title_sort | relationships between vegetation indices and surface reflectance implications for detecting and monitoring sandification in arid regions |
| topic | Terminal lakes Land sandification Feature space model Spatiotemporal dynamics Driving factors |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25005709 |
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