Quantifying the direct and indirect effects of terrain, climate and human activity on the spatial pattern of kNDVI-based vegetation growth: A case study from the Minjiang River Basin, Southeast China

Gu, Zipeng; Chen, Xingwei; Ruan, Weifang; Zheng, Meiling; Gen, Kaili; Li, Xiaochen; Deng, Haijun; Chen, Ying; Liu, Meibing

doi:10.1016/j.ecoinf.2024.102493

Cited by 16 publications

(3 citation statements)

References 72 publications

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“…The introduction of kNDVI as an advanced vegetative index in yield prediction models represents a promising development in precision agriculture. kNDVI's capacity to adjust traditional NDVI readings to account for variations in environmental conditions allows for a more accurate representation of crop vigor, especially under diverse and fluctuating agricultural settings [48]. kNDVI significantly enhances the mitigation of saturation issues associated with NDVI in high-biomass scenarios, thereby improving the sensitivity to variations in crop stress conditions.…”

Section: Discussionmentioning

confidence: 99%

“…It refines the classical NDVI by integrating a kernel function, enhancing the index's responsiveness in high-biomass environments. Unlike NDVI, which is based on the raw difference between near-infrared (NIR) and red reflectance, the kNDVI employs a non-linear statistical model through kernel methods [48,49]. The adoption of the radial basis function (RBF) kernel modifies the interaction between NIR and red spectral bands, incorporating a scale parameter σ to better capture the nuanced variations in vegetation [50].…”

Section: Vegetation Indicesmentioning

confidence: 99%

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

Lu,

Li,

et al. 2024

Agriculture

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

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Section: Discussionmentioning

confidence: 99%

Section: Vegetation Indicesmentioning

confidence: 99%

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

Lu,

Li,

et al. 2024

Agriculture

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“…In addition, the feature space model could cover multi-dimensional information about the evolutionary process of desertification, such as soil, vegetation, and climate, and could evaluate the development trend and influencing factors of desertification more comprehensively [36]. Based on the feature space, this study introduced the KNDVI as the desertification characterization parameter to construct a desertification remote-sensing monitoring index and achieved accurate results [37]. This is because the KNDVI could reduce the influences of the atmosphere and soil on the vegetation index to a certain extent by improving the algorithm so that it could more accurately reflect the vegetation situation.…”

Section: Transformation Of Desertification Degree In Gulang Countymentioning

confidence: 99%

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

Guo,

Zhang,

et al. 2024

Remote Sensing

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

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Impacts of climate change and human activities on vegetation dynamics on the Mongolian Plateau, East Asia from 2000 to 2023

Yan,

Cheng,

Xin

et al. 2024

J. Arid Land

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Quantifying the direct and indirect effects of terrain, climate and human activity on the spatial pattern of kNDVI-based vegetation growth: A case study from the Minjiang River Basin, Southeast China

Cited by 16 publications

References 72 publications

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

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

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

Impacts of climate change and human activities on vegetation dynamics on the Mongolian Plateau, East Asia from 2000 to 2023

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