Abandoned Farmland Extraction and Feature Analysis Based on Multi-Sensor Fused Normalized Difference Vegetation Index Time Series—A Case Study in Western Mianchi County DOI Creative Commons
Jiqiu Deng, Yiwei Guo, Xiaohong Chen

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(5), P. 2102 - 2102

Published: March 2, 2024

Farmland abandonment monitoring is one of the key aspects land use and cover research, as well being an important prerequisite for ecological environmental protection food security. A Normalized Difference Vegetation Index (NDVI) time series analysis a common method used farmland data extraction; however, extracting this information using high-resolution still difficult due to limitations caused by cloud influence low temporal resolution. To address problem, study STARFM GF-6 Landsat 8 fusion enhance continuity cloudless images. dataset was constructed combining phenological cycle crops in area then abandoned based on NDVI analysis. The overall accuracy results STARFM-fused 93.42%, which 15.5% higher than obtained only 28.52% those data. Improvements were also achieved when SVM fused dataset, indicating that can effectively improve results. Then, we analyzed spatial distribution pattern concluded rate increased with increase road network density decreased distance residential areas. This provide decision-making guidance scientific technological support facilitate mechanisms area, conducive sustainable development farmland.

Language: Английский

Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities DOI
Zheng Gong, Wenyan Ge, Jiaqi Guo

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 217, P. 149 - 164

Published: Aug. 29, 2024

Language: Английский

Citations

26
Global increase in the occurrence and impact of multiyear droughts DOI
Liangzhi Chen, Philipp Brun,

Pascal Buri

et al.

Science, Journal Year: 2025, Volume and Issue: 387(6731), P. 278 - 284

Published: Jan. 16, 2025

Persistent multiyear drought (MYD) events pose a growing threat to nature and humans in changing climate. We identified inventoried global MYDs by detecting spatiotemporally contiguous climatic anomalies, showing that have become drier, hotter, led increasingly diminished vegetation greenness. The terrestrial land affected has increased at rate of 49,279 ± 14,771 square kilometers per year from 1980 2018. Temperate grasslands exhibited the greatest declines greenness during MYDs, whereas boreal tropical forests had comparably minor responses. With becoming more common, this quantitative inventory occurrence, severity, trend, impact provides an important benchmark for facilitating effective collaborative preparedness toward mitigation adaptation such extreme events.

Language: Английский

Citations

5
Comparing the performance of vegetation indices for improving urban vegetation GPP estimation via eddy covariance flux data and Landsat 5/7 data DOI Creative Commons
Qi Zeng, Xuehe Lu, Sanmei Chen

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103023 - 103023

Published: Jan. 1, 2025

Language: Английский

Citations

2
Globally quantitative analysis of the impact of atmosphere and spectral response function on 2-band enhanced vegetation index (EVI2) over Sentinel-2 and Landsat-8 DOI
Zhijun Zhen, Shengbo Chen, Tiangang Yin

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 205, P. 206 - 226

Published: Oct. 14, 2023

Language: Английский

Citations

39
Deep Learning for Multi-Source Data-Driven Crop Yield Prediction in Northeast China DOI Creative Commons
Jian Lü, Jian Li,

Hongkun Fu

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(6), P. 794 - 794

Published: May 22, 2024

The accurate prediction of crop yields is crucial for enhancing agricultural efficiency and ensuring food security. This study assesses the performance CNN-LSTM-Attention model in predicting maize, rice, soybeans Northeast China compares its effectiveness with traditional models such as RF, XGBoost, CNN. Utilizing multi-source data from 2014 to 2020, which include vegetation indices, environmental variables, photosynthetically active parameters, our research examines model’s capacity capture essential spatial temporal variations. integrates Convolutional Neural Networks, Long Short-Term Memory, an attention mechanism effectively process complex datasets manage non-linear relationships within data. Notably, explores potential using kNDVI multiple crops, highlighting effectiveness. Our findings demonstrate that advanced deep-learning significantly enhance yield accuracy over methods. We advocate incorporation sophisticated technologies practices, can substantially improve production strategies.

Language: Английский

Citations

12
Global patterns and drivers of post-fire vegetation productivity recovery DOI
Hongtao Xu, Hans W. Chen, Deliang Chen

et al.

Nature Geoscience, Journal Year: 2024, Volume and Issue: 17(9), P. 874 - 881

Published: Aug. 23, 2024

Language: Английский

Citations

12
Ecological assessment and driver analysis of high vegetation cover areas based on new remote sensing index DOI Creative Commons
Xiaoyong Zhang, Weiwei Jia,

Shixin Lu

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102786 - 102786

Published: Aug. 23, 2024

Language: Английский

Citations

11
Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data DOI Creative Commons
Haiying Yu,

Qianhua Yang,

Shouzheng Jiang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(7), P. 1280 - 1280

Published: April 5, 2024

Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address possible threats posed by changing environments. On basis of kernel normalized difference index (kNDVI) key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), potential evapotranspiration (PET)) basin during period from 1982 2022, we utilized multivariate statistical approach analyze spatiotemporal patterns dynamics, identified variables, discerned respective impacts change (CC) human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% YRB, with 83.2% exhibiting significant increases kNDVI (p < 0.05). Conversely, 6.9% vegetated areas displayed browning trend, particularly concentrated alpine urban areas. With Hurst exceeding 0.5 97.5% areas, YRB tends be extensively greened future. Climate variability emerges as pivotal determinant shaping diverse spatial temporal patterns, PRE exerting dominance 41.9% followed TEM (35.4%), SR (13%), PET (9.7%). Spatially, increased significantly enhanced growth arid zones, while controlled non-water-limited such irrigation zones. Vegetation dynamics were driven combination CC HA, relative contributions 55.8% 44.2%, respectively, suggesting that long-term dominant force. Specifically, contributed seen region southeastern part basin, human-induced factors benefited Loess Plateau (LP) inhibiting pastoral These findings provide critical insights inform formulation adaptation conservation thereby enhancing resilience environmental conditions.

Language: Английский

Citations

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

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 948, P. 174903 - 174903

Published: July 20, 2024

Language: Английский

Citations

10
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

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(7), P. 1258 - 1258

Published: April 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.

Language: Английский

Citations

9