Zhiqiang Lin , Shuangyun Peng , Yuanyuan Yin , Dongling Ma , Rong Jin , Jiaying Zhu , Ziyi Zhu , Shuangfu Shi , Yilin Zhu
{"title":"InVEST-GTWR耦合模型揭示了中国山区氮磷出口的规模依赖驱动因素","authors":"Zhiqiang Lin , Shuangyun Peng , Yuanyuan Yin , Dongling Ma , Rong Jin , Jiaying Zhu , Ziyi Zhu , Shuangfu Shi , Yilin Zhu","doi":"10.1016/j.jag.2025.104705","DOIUrl":null,"url":null,"abstract":"<div><div>Nitrogen (N) and phosphorus (P) export from non-point sources significantly threaten water quality in mountainous regions undergoing rapid agricultural intensification and urbanization. However, existing research often neglects multi-scale analyses across administrative levels and provides limited insight into the complex drivers of nutrient export in data-scarce mountainous regions. This study presents a novel integrated modeling framework by coupling the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model with Geographically and Temporally Weighted Regression (GTWR) to analyze N and P export dynamics in Yunnan Province from 2000 to 2019. We reveal a temporal turning point around 2011, with declining nutrient exports before 2011 followed by a rising trend linked to policy changes and intensified land use. Spatially, high N and P export clusters occur in densely populated, agriculturally intensive central and southeastern Yunnan, while forested northwestern areas exhibit low export. Prefecture-scale drivers are dominated by population density, fertilizer application, and industrial activity, whereas county-scale drivers highlight cropland area, precipitation, and terrain factors. Importantly, natural and anthropogenic factors interact to shape nutrient export patterns, underscoring the need for spatially differentiated management. The integration of InVEST-GTWR reflects methodological innovations that capture spatio-temporal non-stationarity and provide actionable insights for targeted nutrient pollution control in mountainous regions, with high model accuracy (R<sup>2</sup> = 0.98 at the prefecture scale and R<sup>2</sup> = 0.95 at the county scale).</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"142 ","pages":"Article 104705"},"PeriodicalIF":8.6000,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Coupled InVEST-GTWR modeling reveals scale-dependent drivers of N and P export in a Chinese mountainous region\",\"authors\":\"Zhiqiang Lin , Shuangyun Peng , Yuanyuan Yin , Dongling Ma , Rong Jin , Jiaying Zhu , Ziyi Zhu , Shuangfu Shi , Yilin Zhu\",\"doi\":\"10.1016/j.jag.2025.104705\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Nitrogen (N) and phosphorus (P) export from non-point sources significantly threaten water quality in mountainous regions undergoing rapid agricultural intensification and urbanization. However, existing research often neglects multi-scale analyses across administrative levels and provides limited insight into the complex drivers of nutrient export in data-scarce mountainous regions. This study presents a novel integrated modeling framework by coupling the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model with Geographically and Temporally Weighted Regression (GTWR) to analyze N and P export dynamics in Yunnan Province from 2000 to 2019. We reveal a temporal turning point around 2011, with declining nutrient exports before 2011 followed by a rising trend linked to policy changes and intensified land use. Spatially, high N and P export clusters occur in densely populated, agriculturally intensive central and southeastern Yunnan, while forested northwestern areas exhibit low export. Prefecture-scale drivers are dominated by population density, fertilizer application, and industrial activity, whereas county-scale drivers highlight cropland area, precipitation, and terrain factors. Importantly, natural and anthropogenic factors interact to shape nutrient export patterns, underscoring the need for spatially differentiated management. The integration of InVEST-GTWR reflects methodological innovations that capture spatio-temporal non-stationarity and provide actionable insights for targeted nutrient pollution control in mountainous regions, with high model accuracy (R<sup>2</sup> = 0.98 at the prefecture scale and R<sup>2</sup> = 0.95 at the county scale).</div></div>\",\"PeriodicalId\":73423,\"journal\":{\"name\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"volume\":\"142 \",\"pages\":\"Article 104705\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2025-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1569843225003528\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of applied earth observation and geoinformation : ITC journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569843225003528","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REMOTE SENSING","Score":null,"Total":0}
Coupled InVEST-GTWR modeling reveals scale-dependent drivers of N and P export in a Chinese mountainous region
Nitrogen (N) and phosphorus (P) export from non-point sources significantly threaten water quality in mountainous regions undergoing rapid agricultural intensification and urbanization. However, existing research often neglects multi-scale analyses across administrative levels and provides limited insight into the complex drivers of nutrient export in data-scarce mountainous regions. This study presents a novel integrated modeling framework by coupling the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model with Geographically and Temporally Weighted Regression (GTWR) to analyze N and P export dynamics in Yunnan Province from 2000 to 2019. We reveal a temporal turning point around 2011, with declining nutrient exports before 2011 followed by a rising trend linked to policy changes and intensified land use. Spatially, high N and P export clusters occur in densely populated, agriculturally intensive central and southeastern Yunnan, while forested northwestern areas exhibit low export. Prefecture-scale drivers are dominated by population density, fertilizer application, and industrial activity, whereas county-scale drivers highlight cropland area, precipitation, and terrain factors. Importantly, natural and anthropogenic factors interact to shape nutrient export patterns, underscoring the need for spatially differentiated management. The integration of InVEST-GTWR reflects methodological innovations that capture spatio-temporal non-stationarity and provide actionable insights for targeted nutrient pollution control in mountainous regions, with high model accuracy (R2 = 0.98 at the prefecture scale and R2 = 0.95 at the county scale).
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.