Deep Learning for Multi-Source Data-Driven Crop Yield Prediction in Northeast China

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

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Quantitatively analyzing the driving factors of vegetation change in China: Climate change and human activities

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102667 - 102667

Published: June 5, 2024

Understanding the impact of climate change and human activities on vegetation dynamics is crucial for ecosystem management. Employing Residual Trend method integrating Normalized Difference Vegetation Index (NDVI) data with land use/cover, this study assesses impacts across China from 2000 to 2018. The findings indicate a consistent upward trend China's Growing Season NDVI (GSN), averaging rate 0.0032/yr. Human are primary drivers change, contributing 82.47% GSN in China, while accounts 17.53%. effect showed considerable variation different river basins, Huaihe River Basin experiencing highest (93.53%) Continental lowest (76.27%). Conversely, experienced greatest (23.73%), compared minimal influence (6.47%). results offer contribution rates each type changed unchanged use, persistent forestland, grassland, cropland, grassland forest conversion 28.65%, 22.09%, 13.76%, 4.61%, respectively. Persistent forestland emerges as most efficacious use facilitating restoration. Within forestlands Yangtze, Pearl, Southeast Basins, accounted 26.99%, 42.18%, 43.50% alterations, These provide scientific basis formulating effective management protection strategies.

Language: Английский

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A New Large-Scale Monitoring Index of Desertification Based on Kernel Normalized Difference Vegetation Index and Feature Space Model

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(10), P. 1771 - 1771

Published: May 16, 2024

As a new vegetation monitoring index, the KNDVI has certain advantages in characterizing evolutionary process of regional desertification. However, there are few reports on desertification based and feature space models. In this study, seven parameters, including kernel normalized difference index (KNDVI) Albedo, were introduced to construct different models for remote-sensing monitoring. The optimal model was determined with measured data; then, spatiotemporal evolution pattern Gulang County from 2013 2023 analyzed revealed. main conclusions as follows: (1) Compared NDVI MSAVI, showed more characterization process. (2) point–line KNDVI-Albedo had highest accuracy, reaching 94.93%, while NDVI-TGSI lowest accuracy 54.38%. (3) From 2023, overall situation trend improvement “firstly aggravation then alleviation.” Additionally, gravity center first shifted southeast northeast, indicating that northeast’s aggravating rate higher than southwest during period. (4) area stable largest, followed by slightly weakened zone, most significant transition extreme severe research results provide important decision support precise governance

Language: Английский

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Exploring the impact of natural and human activities on vegetation changes: An integrated analysis framework based on trend analysis and machine learning

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 374, P. 124092 - 124092

Published: Jan. 17, 2025

Language: Английский

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The heterogeneity of Pinus yunnanensis plantation growth was driven by soil microbial characteristics in different slope aspects

BMC Plant Biology, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 27, 2025

The slope aspect is an important environmental factor, which can indirectly change the acceptable solar radiation of forests. However, mechanism how this changes underground ecosystem and thus affects growth aboveground trees not clear. In study, Pinus yunnanensis plantation was taken as research object, effects soil microbial characteristics on tree under different aspects depths were systematically analyzed. height (H) ground diameter (GD) sunny 7.64% 8.69% higher than those shady slope. pH, alkaline hydrolyzable nitrogen (AHN), available phosphorous (AP), potassium (AK), total (TN), (TP), (TK) significantly between aspects. With increase in depth, content organic matter (OM), AHN, AP, AK decreased. There significant differences diversity community structure aspects, but there no difference among depths. abundance Proteobacteria a lower that slope, richness Firmicutes Planctomycetota increased, structural equation model showed influence bacteria fungi much greater growth, microorganisms. caused microorganisms, further affected led to heterogeneity forest growth. insights gleaned from study hold potential inform formulation customized management strategies, thereby enhancing resource utilization efficiency fostering vitality ecosystems. Furthermore, offers theoretical underpinning for targeted cultivation coniferous plantations.

Language: Английский

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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

Sustainability, Journal Year: 2025, Volume and Issue: 17(6), P. 2348 - 2348

Published: March 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

Language: Английский

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Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 598 - 598

Published: March 12, 2025

As global climate change intensifies, its impact on the ecological environment is becoming increasingly pronounced. Among these, land surface temperature (LST) and vegetation cover status, as key indicators, have garnered widespread attention. This study analyzes spatiotemporal dynamics of LST Kernel Normalized Difference Vegetation Index (KNDVI) in 11 provinces along Yangtze River their response to based MODIS Terra satellite data from 2000 2020. The linear regression showed a significant KNDVI increase 0.003/year (p < 0.05) rise 0.065 °C/year 0.01). Principal Component Analysis (PCA) explained 74.5% variance, highlighting dominant influence urbanization. K-means clustering identified three regional patterns, with Shanghai forming distinct group due low variability. Generalized Additive Model (GAM) analysis revealed nonlinear LST–KNDVI relationship, most evident Hunan, where cooling effects weakened beyond threshold 0.25. Despite 0.07 increase, high-temperature areas Chongqing Jiangsu expanded by over 2500 km2, indicating limited mitigation. reveals complex interaction between KNDVI, which may provide scientific basis for development management adaptation strategies.

Language: Английский

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Dual Pathways of Carbon Neutrality in urban green spaces: Assessment and Regulatory Strategies

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106311 - 106311

Published: March 1, 2025

Language: Английский

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Analysis of vegetation dynamics from 2001 to 2020 in China's Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning

Ecological Informatics, Journal Year: 2024, Volume and Issue: 84, P. 102887 - 102887

Published: Nov. 9, 2024

Language: Английский

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Dynamic evolution of the ecological footprint of arable land in the Yellow and Huaihai Main grain producing area based on structural equation modeling and analysis of driving factors

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102720 - 102720

Published: July 11, 2024

Arable land is shrinking, and the ecological strain on such growing daily. To ensure its sustainability, uncovering dynamic changes driving forces crucial. We assessed arable footprint (CEF) in Yellow Huaihai main grain-producing areas from 2010 to 2020, employing productive analyses. Additionally, we built a structural equation model for per capita CEF area, incorporating China's economic theory economic-social-ecological system identify influencing factors. Our findings indicate following: (1) area showed fluctuating upward trend during 2010–2020, while carrying capacity of decreased, resulting surplus, except 2017; (2) land's sustainable pressure index increased, signifying low safety grain producing efficiency indicating resource improvements; (3) reveals that output, conditions, socioeconomics, inputs all impact footprints respective order. results offer valuable insights securing national food sustainability preserving land.

Language: Английский

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