Explainable machine learning-based fractional vegetation cover inversion and performance optimization – A case study of an alpine grassland on the Qinghai-Tibet Plateau DOI Creative Commons
Xinhong Li, Jianjun Chen, Zizhen Chen

и другие.

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102768 - 102768

Опубликована: Авг. 10, 2024

Fractional Vegetation Cover (FVC) serves as a crucial indicator in ecological sustainability and climate change monitoring. While machine learning is the primary method for FVC inversion, there are still certain shortcomings feature selection, hyperparameter tuning, underlying surface heterogeneity, explainability. Addressing these challenges, this study leveraged extensive field data from Qinghai-Tibet Plateau. Initially, selection algorithm combining genetic algorithms XGBoost was proposed. This integrated with Optuna tuning method, forming GA-OP combination to optimize learning. Furthermore, comparative analyses of various models inversion alpine grassland were conducted, followed by an investigation into impact heterogeneity on performance using NDVI Coefficient Variation (NDVI-CV). Lastly, SHAP (Shapley Additive exPlanations) employed both global local interpretations optimal model. The results indicated that: (1) exhibited favorable terms computational cost accuracy, demonstrating significant potential tuning. (2) Stacking model achieved among seven (R2 = 0.867, RMSE 0.12, RPD 2.552, BIAS −0.0005, VAR 0.014), ranking follows: > CatBoost LightGBM RFR KNN SVR. (3) NDVI-CV enhanced result reliability excluding highly heterogeneous regions that tended be either overestimated or underestimated. (4) revealed decision-making processes perspectives. allowed deeper exploration causality between features targets. developed high-precision scheme, successfully achieving accurate proposed approach provides valuable references other parameter inversions.

Язык: Английский

Identification of Ecological Sources Using Ecosystem Service Value and Vegetation Productivity Indicators: A Case Study of the Three-River Headwaters Region, Qinghai–Tibetan Plateau, China DOI Creative Commons

Xinyi Feng,

Huiping Huang,

Yingqi Wang

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(7), С. 1258 - 1258

Опубликована: Апрель 2, 2024

As a crucial component of the ecological security pattern, source (ES) plays vital role in providing ecosystem service value (ESV) and conserving biodiversity. Previous studies have mostly considered ES only from either landscape change pattern or function perspectives, ignored their integration spatio-temporal evolutionary modeling. In this study, we proposed multi-perspective framework for characteristics by ESV incorporating aesthetics, carbon sink characteristics, quality, kernel NDVI (kNDVI). By integrating revised normalized difference vegetation index as foundation, employed spatial priority model to identify ES. This improvement aims yield more practical specific result. Applying Three-River Headwaters Region (TRHR), significant sources has been observed 2000 2020. performance provided reference conservation TRHR. The results indicate that identification reliable accuracy efficiency compared with existing NRs method could reveal precise distributions ES, enhancing integrity technical modeling support developing cross-scale planning management strategies nature reserve boundaries. our research serve building networks other ecologically fragile areas.

Язык: Английский

Процитировано

10
Nonlinear effects of agricultural drought on vegetation productivity in the Yellow River Basin, China DOI
Yu‐Jie Ding, Lifeng Zhang, Yi He

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 948, С. 174903 - 174903

Опубликована: Июль 20, 2024

Язык: Английский

Процитировано

10
Comparison between Satellite Derived Solar-Induced Chlorophyll Fluorescence, NDVI and kNDVI in Detecting Water Stress for Dense Vegetation across Southern China DOI Creative Commons
Chunxiao Wang,

Lu Liu,

Yuke Zhou

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(10), С. 1735 - 1735

Опубликована: Май 14, 2024

In the context of global climate change and increase in drought frequency, monitoring accurately assessing impact hydrological process limitations on vegetation growth is paramount importance. Our study undertakes a comprehensive evaluation efficacy satellite remote sensing indices—Normalized Difference Vegetation Index (MODIS NDVI product), kernel (kNDVI), Solar-Induced chlorophyll Fluorescence (GOSIF product) this regard. Initially, we applied LightGBM-Shapley additive explanation framework to assess influencing factors three indices. We found that Vapor Pressure Deficit (VPD) primary factor affecting southern China (18°–30°N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as performance benchmark, evaluated ability these indices reflect GPP changes during conditions. findings indicate SIF serves most effective surrogate for GPP, capturing variability periods with minimal time lag. Additionally, our reveals kNDVI significantly varies depending estimation different parameters. The application time-heuristic method could potentially enhance kNDVI’s capacity capture dynamics more effectively periods. Overall, demonstrates satellite-based data are adept at responses water stress tracking anomalies caused by droughts. These not only provide critical insights into selection optimization product but also offer valuable future research aimed improving understanding status under climatic changes.

Язык: Английский

Процитировано

9
Real-time monitoring of maize phenology with the VI-RGS composite index using time-series UAV remote sensing images and meteorological data DOI

Ziheng Feng,

Zhida Cheng,

Lipeng Ren

и другие.

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 224, С. 109212 - 109212

Опубликована: Июль 2, 2024

Язык: Английский

Процитировано

9
Enhancing the accuracy of monitoring effective tiller counts of wheat using multi-source data and machine learning derived from consumer drones DOI

Ziheng Feng,

Jiaxiang Cai, Ke Wu

и другие.

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 232, С. 110120 - 110120

Опубликована: Фев. 24, 2025

Язык: Английский

Процитировано

2
Spatiotemporal Variations in Compound Extreme Events and Their Cumulative and Lagged Effects on Vegetation in the Northern Permafrost Regions from 1982 to 2022 DOI Creative Commons

Y. Dong,

Guimin Liu, Xiaodong Wu

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(1), С. 169 - 169

Опубликована: Янв. 6, 2025

The northern permafrost regions are increasingly experiencing frequent and intense extreme events, with a rise in the occurrence of compound events. Many climate-related hazards these areas driven by such significantly affecting stability functionality vegetation ecosystems. However, cumulative lagged effects events on remain unclear, which may lead to an underestimation their actual impacts. This study provides comprehensive analysis spatiotemporal variations response from 1982 2022. primary focus this is examining climate Kernel Normalized Difference Vegetation Index (kNDVI) during growing seasons. results indicate that high-latitude regions, frequency high temperature–precipitation temperature–drought have increased 58.0% 67.0% areas, respectively. Conversely, low has decreased 70.6% 57.2% showing fastest increase. temporal kNDVI vary type; they produce more compared single high-temperature fewer precipitation forest grassland Notably, exhibit strongest vegetation, while influence wetland shrubland within same month. underscores importance multivariable perspective understanding dynamics regions.

Язык: Английский

Процитировано

1
Study on the driving mechanism of spatio-temporal non-stationarity of vegetation dynamics in the Taihangshan-Yanshan Region DOI Creative Commons

Jiao Pang,

Meiqing Wang, Huicong Zhang

и другие.

Ecological Indicators, Год журнала: 2025, Номер 170, С. 113084 - 113084

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
kNDVI Spatiotemporal Variations and Climate Lag on Qilian Southern Slope: Sen–Mann–Kendall and Hurst Index Analyses for Ecological Insights DOI Open Access
Qian Zhang,

Guangchao Cao,

Meiliang Zhao

и другие.

Forests, Год журнала: 2025, Номер 16(2), С. 307 - 307

Опубликована: Фев. 10, 2025

In the context of climate change, southern slope Qilian Mountains stands as a pivotal region for China’s ecological security, holding immense significance sustaining sustainable development. This study aims to precisely monitor and predict dynamic changes in vegetation cover within this region, along with their time-lagged effects on thereby providing scientific basis management. By calculating kNDVI from 2001 2020 Google Earth Engine (GEE) platform, integrating Sen’s trend analysis, Hurst exponent, partial correlation we have conducted an in-depth exploration long-term spatiotemporal variations its delayed responses factors. The primary research findings can be summarized follows: exhibits overall positive trend, notable geographical spatial distribution. proportion areas showing improvement is high 84%, while degraded account only 17%. Furthermore, there average lag response 1.6 months precipitation 0.6 temperature region. speed positively correlates coefficient between Notably, more sensitive area Mountains. not fills gap monitoring but also offers support governance green development initiatives Additionally, it showcases innovative application advanced remote sensing technologies statistical analysis methods research, fresh perspectives future management strategies. These hold profound implications promoting conservation area.

Язык: Английский

Процитировано

1
A long-term reconstruction of a global photosynthesis proxy over 1982–2023 DOI Creative Commons
Jianing Fang, Lian Xu, Youngryel Ryu

и другие.

Scientific Data, Год журнала: 2025, Номер 12(1)

Опубликована: Март 3, 2025

Abstract Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to recent decade, impeding their application in detecting long-term dynamics ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982–2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001–2023). Importantly, calibrate orbit-correct AVHRR against MODIS counterparts during overlapping period. Using bias-corrected data MODIS, neural network trained produce Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over 1982–2023 Compared with previous photosynthesis proxies, LCSPP has similar skill but can be advantageously extended Further comparison three widely used vegetation indices (NDVI, kNDVI, NIRv) shows higher or comparable correlation satellite site-level GPP estimates across types, ensuring greater capacity representing activity.

Язык: Английский

Процитировано

1
Impacts of Climate Change and Anthropogenic Activities on Vegetation Dynamics Considering Time Lag and Accumulation Effects: A Case Study in the Three Rivers Source Region, China DOI Open Access
Yunfei Ma, Xiaobo He, Donghui Shangguan

и другие.

Sustainability, Год журнала: 2025, Номер 17(6), С. 2348 - 2348

Опубликована: Март 7, 2025

Examining the effects of climate change (CC) and anthropogenic activities (AAs) on vegetation dynamics is essential for ecosystem management. However, time lag accumulation plant growth are often overlooked, resulting in an underestimation CC impacts. Combined with kernel normalized difference index (kNDVI), data during growing season from 2000 to 2023 Three Rivers Source Region (TRSR) trend correlation analyses were employed assess kNDVI dynamics. Furthermore, effect upgraded residual analysis applied explore how climatic human drivers jointly influence vegetation. The results show following: (1) showed a fluctuating but overall increasing trend, indicating improvement growth. Although future likely continue improving, certain areas—such as east western Yangtze River basin, south Yellow parts Lancang basin—will remain at risk deterioration. (2) Overall, both precipitation temperature positively correlated kNDVI, acting dominant factor affecting predominant temporal 0-month 1-month accumulation, while primarily 2–3-month 0–1-month accumulation. main category (PA_TL), which accounted 70.93% TRSR. (3) Together, AA drove dynamics, contributions 35.73% 64.27%, respectively, that played role. incorporating combined enhanced explanatory ability factors

Язык: Английский

Процитировано

1