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

Agriculture, Год журнала: 2024, Номер 14(6), С. 794 - 794

Опубликована: Май 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.

Язык: Английский

---

Monitoring and evaluation of ecological restoration in open-pit coal mine using remote sensing data based on a OM-RSEI model

International Journal of Mining Reclamation and Environment, Год журнала: 2025, Номер unknown, С. 1 - 23

Опубликована: Янв. 29, 2025

Язык: Английский

---

Analysis of the Influence of Driving Factors on Vegetation Changes Based on the Optimal-Parameter-Based Geographical Detector Model in the Yima Mining Area

Forests, Год журнала: 2024, Номер 15(9), С. 1573 - 1573

Опубликована: Сен. 7, 2024

The growth of vegetation directly maintains the ecological security coal mining areas. It is great significance to monitor dynamic changes in areas and study driving factors spatial division. This focuses on Yima area Henan Province. Utilizing MODIS multi-dimensional explanatory variable data, Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst optimal-parameter-based geographical detector model (OPGD) are employed analyze spatiotemporal future trends EVI (enhanced index) from 2000 2020. further investigates underlying that contribute vegetation. results indicate following: (1) During period studied, was primarily characterized by a moderate-to-low cover. exhibited significant variation, with notable pattern “western improvement eastern degradation”. indicated experienced greatly outnumbered underwent degradation. Moreover, there an inclination towards deterioration future. (2) Based optimal parameter geographic detector, it found 2 km scale for analysis change this area. combination determined employing five data discretization methods selecting interval classification range 5–10. approach effectively addresses subjective bias scales discretization, leading enhanced accuracy identification its factors. (3) heterogeneity influenced various factors, such as topography, socio-economic conditions, climate, etc. Among these population density mean annual temperature were primary forces area, Q > 0.29 elevation being strongest factor (Q = 0.326). interaction between night light most powerful explanation 0.541), average value other 0.478, which cofactor among interactions. interactions any two their impact vegetation’s changes, each had suitable affecting vegetative within region. research provides scientific support conserving restoring system.

Язык: Английский

---

Study on spatio-temporal evolution of ecosystem services, spatio-temporal pattern of tradeoff/synergy relationship and its driving factors in Shendong mining area

Frontiers in Environmental Science, Год журнала: 2024, Номер 12

Опубликована: Авг. 6, 2024

Objectives: The game between socio-economic development and ecological has always been the core issue in coal areas, but internal mechanism of tradeoff cooperative dynamic change ecosystem services mining areas under long-term mineral resources is still lacking in-depth research. Methods: Therefore, taking Shendong area as an example, this study used InVEST model to evaluate changes four major service functions from 1990 2020, namely, water yield (WY), net primary productivity (NPP), soil conservation (SC) habitat quality (HQ). Meanwhile, correlation analysis was explore trade-off synergistic relationship among these services. On basis, coupling effect further discussed by using constraint line method. Finally, key drivers trade-offs/synergies region are explored geodetectors explanations each influence factor for RMS errors obtained. Results: results show that 1) retention decrease first then increase, increase slowly, mainly southeast area. 2) In terms relationship, all showed hump-like is, there obvious threshold effect. 3) area, dominant services, occurs quality. 4) driving tradeoff/synergy, land use type, temperature, rainfall main factors cause spatial differentiation synergy intensity Conclusions: provide a scientific basis improvement environment sustainable utilization exploitation.

Язык: Английский

---

Spatiotemporal Evolution and Driving Mechanisms of kNDVI in Different Sections of the Yangtze River Basin Using Multiple Statistical Methods and the PLSPM Model

Remote Sensing, Год журнала: 2025, Номер 17(2), С. 299 - 299

Опубликована: Янв. 16, 2025

Spatiotemporal vegetation changes serve as a key indicator of regional ecological environmental quality and provide crucial guidance for developing strategies protection sustainable development. Currently, change studies in the Yangtze River Basin primarily rely on Normalized Difference Vegetation Index (NDVI). However, NDVI is susceptible to atmospheric soil conditions exhibits saturation phenomena areas with high coverage. In contrast, kernel (kNDVI) demonstrates significant advantages suppressing background noise improving thresholds through nonlinear transformation, thereby enhancing sensitivity changes. To elucidate spatiotemporal characteristics driving mechanisms Basin, this study constructed temporal kNDVI using MOD09GA data from 2000 2022. Considering sectional heterogeneity, rather than analyzing entire region whole previous studies, research examined evolution by sections four statistical metrics. Subsequently, Partial Least Squares Path Modeling (PLSPM) was innovatively introduced quantitatively analyze influence topographic, climatic, pedological, socioeconomic factors. Compared traditional correlation analysis geographical detector method, PLSPM, theoretically driven can simultaneously process path relationships among multiple latent variables, effectively revealing intensity pathways factors’ influences, while providing more credible interpretable explanations variation mechanisms. Results indicate that overall exhibited an upward trend, midstream demonstrating most improvement minimal interannual fluctuations, upstream displaying east-increasing west-stable spatial pattern, downstream coexisting degradation characteristics, these trends expected persist. Driving mechanism reveals predominantly influenced climatic factor, dominated terrain, displayed terrain–soil coupling effects. Based findings, it recommended focus adaptation management climate change, need coordinate relationship between topography human activities, should concentrate controlling negative impacts urban expansion vegetation.

Язык: Английский

---

Spatiotemporal Changes of Vegetation Growth and Its Influencing Factors in the Huojitu Mining Area from 1999 to 2023 Based on kNDVI

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 536 - 536

Опубликована: Фев. 5, 2025

Vegetation indices are important representatives of plant growth. Climate change and human activities seriously affect vegetation. This study focuses on the Huojitu mining area in Shendong region, utilizing kNDVI index calculated via Google Earth Engine (GEE) cloud platform. The Mann–Kendall mutation test linear regression analysis were employed to examine spatiotemporal changes vegetation growth over a 25-year period from 1999 2023. Through correlation analysis, geographic detector models, land use map fusion, combined with climate, topography, soil, mining, data, this investigates influencing factors evolution. key findings as follows: (1) is more suitable for analyzing compared NDVI. (2) Over past 25 years, has exhibited an overall fluctuating upward trend, annual rate 0.0041/a. average value 0.121. Specifically, initially increased gradually, then rapidly increased, subsequently declined rapidly. (3) significantly improved, areas improved accounting 89.08% total area, while degraded account 11.02%. (4) Precipitation air temperature primary natural fluctuations precipitation being dominant factor (r = 0.81, p < 0.01). spatial heterogeneity influenced by use, soil nutrients, activities, having greatest impact (q 0.43). Major contribute 46.45% improvement 13.43% degradation. provide scientific basis ecological planning development area.

Язык: Английский

---

Multiscale spatiotemporal analysis of carbon dioxide emissions in China from 2012 to 2021 based on kernel normalized difference vegetation index and nighttime light data

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(6)

Опубликована: Май 14, 2025

Язык: Английский

---

Vegetation Dynamics and Driving Mechanisms Considering Time-Lag and Accumulation Effects: A Case Study of Hubao–Egyu Urban Agglomeration

Land, Год журнала: 2024, Номер 13(9), С. 1337 - 1337

Опубликована: Авг. 23, 2024

The Hubao–Egyu Urban Agglomeration (HBEY) was a crucial ecological barrier in northern China. To accurately assess the impact of climate change on vegetation growth, it is essential to consider effects time lag and accumulation. In this study, we used newly proposed kernel Normalized Difference Vegetation Index (kNDVI) as metric for condition, employed partial correlation analysis ascertain accumulation period response by considering different scenarios (No/Lag/Acc/LagAcc) various combinations. Moreover, further modified traditional residual model. results are follows: (1) From 2000 2022, HBEY experienced extensive persistent greening, with kNDVI slope 0.0163/decade. Precipitation identified dominant climatic factor influencing dynamics. (2) HBEY, effect temperature most distinct, particularly affecting cropland grassland. precipitation pronounced (3) Incorporating into models increases explanatory power impacts dynamics 6.95% compared models. Our findings hold implications regional regulation research.

Язык: Английский

---

Spatiotemporal Patterns of Vegetation Evolution in a Deep Coal Mining Subsidence Area: A Remote Sensing Study of Liangbei, China

Remote Sensing, Год журнала: 2024, Номер 16(17), С. 3204 - 3204

Опубликована: Авг. 29, 2024

This study aims to provide a comprehensive analysis of the impacts high-intensity coal mining on vegetation in Liangbei Town, typical deep area central China. Using Landsat remote sensing data from 2000 2023, processed by Google Earth Engine (GEE) platform, calculates Normalized Difference Vegetation Index (NDVI). Temporal and spatial distribution patterns were assessed using LandTrendr algorithm, Sen’s slope estimation, Mann–Kendall test, coefficient variation, Hurst index. growth dynamics further analyzed through transfer matrix intensity frameworks. Driving factors influencing trends evaluated local climate surface deformation variables SAR imagery. Dimension: From annual NDVI Township showed an upward trend with rate 0.0894 (10a)−1, peaking at 0.51 2020. Spatial The displayed pattern being lower center higher around edges, values concentrated between 0.4 0.51, covering 50.34% total area. Trend Change: Between 83.28% experienced significant improvement NDVI, shifting primarily slight improvement, encompassing 10.98 km². shift exhibited marked tendency. Factors: Deep is eastern part, imagery indicating maximum subsidence 0.26 m. As increases, significantly decreases. findings suggest that future, 91.13% will display antipersistent change trend. offers critical insights into interaction activities cover can serve as reference for environmental evolution management similar areas.

Язык: Английский

---

Spatial-temporal patterns of ecological-environmental attributes within different geological-topographical zones: a case from Hailun District, Heilongjiang Province, China

Frontiers in Environmental Science, Год журнала: 2024, Номер 12

Опубликована: Июль 10, 2024

The climate change and extension of human activities are shedding more stresses on ecosystems. Ecological zoning could help manage the ecosystem deal with environmental problems effectively. Geology topography affect ecology primarily vital perspectives ecological zoning. It is worth preliminarily understanding spatial-temporal patterns ecological-environmental attributes within various geological-topographical zones (GTEZs). objective this study was to delineate GTEZs present a analysis soil land surface parameters GTEZs. Firstly, Landsat imageries, high resolution satellite imagery products, digital elevation model, regional geological map, black thickness, bulk density, meteorological data, ground survey were collected conducted. Secondly, in Hailun District delineated according topographical background. Thirdly, composition, monthly temperature (LST), enhanced vegetation index (EVI), net primary productivity (NPP) derived from imageries. Finally, different revealed analyzed. Results show that sand alluvial plain zone silt-clay undulating mainly possess thick fine-medium granule higher covered by crops grass, flourish most August highest EVI NPP. While sand-conglomerate hill zone, sandstone granite relatively thin medium-coarse lower forest, June July, has yearly total With thinner thickness NPP, tend have vulnerability disturbance contribution carbon neutrality target.

Язык: Английский

---