Geoparcel-Based Spatial Prediction Method for Grassland Fractional Vegetation Cover Mapping

Wu, Tianjun; Luo, Jiancheng; Gao, Lijing; Sun, Yingwei; Yang, Yingpin; Yinqing, Zhou; Dong, Wen; Xin, Zhang

doi:10.1109/jstars.2021.3110896

Cited by 8 publications

(3 citation statements)

References 59 publications

(66 reference statements)

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“…Through field investigations and Region of Interest (ROI) analysis in ENVI, it was found that the spectral curves of A. frigida communities in desert grasslands become flat and featureless due to drought stress. This change makes it difficult to distinguish pixels that are mixed with S. breviflora in the image, leading to issues such as same-spectrum foreign bodies and homogeneous spectra [ 23 , 24 ]. This is also a challenging task in the current research on desert grassland land cover classification and inversion and has become a key technical challenge.…”

Section: Discussionmentioning

confidence: 99%

Research on Classification of Grassland Degeneration Indicator Objects Based on UAV Hyperspectral Remote Sensing and 3D_RNet-O Model

Jin,

Du,

et al. 2024

Sensors

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Real-time and high-precision land cover classification is the foundation for efficient and quantitative research on grassland degradation using remote sensing techniques. In view of the shortcomings of manual surveying and satellite remote sensing, this study focuses on the identification and classification of grass species indicating grassland degradation. We constructed a UAV-based hyperspectral remote sensing system and collected field data in grassland areas. By applying artificial intelligence technology, we developed a 3D_RNet-O model based on convolutional neural networks, effectively addressing technical challenges in hyperspectral remote sensing identification and classification of grassland degradation indicators, such as low reflectance of vegetation, flat spectral curves, and sparse distribution. The results showed that the model achieved a classification accuracy of 99.05% by optimizing hyperparameter combinations based on improving residual block structures. The establishment of the UAV-based hyperspectral remote sensing system and the proposed 3D_RNet-O classification model provide possibilities for further research on low-altitude hyperspectral remote sensing in grassland ecology.

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Section: Discussionmentioning

confidence: 99%

Research on Classification of Grassland Degeneration Indicator Objects Based on UAV Hyperspectral Remote Sensing and 3D_RNet-O Model

Jin,

Du,

et al. 2024

Sensors

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“…Unique prediction alone is not enough to map the vegetation accurately. Fractional vegetation mapping is required to estimate the regular coverage in the spatial resolution method [6]. Considering the surface analysis framework in urban areas, the intensity variations and heat are monitored despite temperature changes in various zones.…”

Section: Introductionmentioning

confidence: 99%

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Vijayalakshmi¹,

Kumar²

2023

Intelligent Automation &Amp; Soft Computing

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Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions. It is challenging to determine vegetation using traditional map classification approaches. The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties. It is more demandable to determine the multiple spectral analyses for improving the accuracy of vegetation mapping through remotely sensed images. The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping. The architecture comprises three approaches, feature-based approach, region-based approach, and texture-based approach for classifying the vegetation area. The novel Deep Meta fusion model (DMFM) is created with a unique fusion framework of residual stacking of convolution layers with Unique covariate features (UCF), Intensity features (IF), and Colour features (CF). The overhead issues in GPU utilization during Convolution neural network (CNN) models are reduced here with a lightweight architecture. The system considers detailing feature areas to improve classification accuracy and reduce processing time. The proposed DMFM model achieved 99% accuracy, with a maximum processing time of 130 s. The training, testing, and validation losses are degraded to a significant level that shows the performance quality with the DMFM model. The system acts as a standard analysis platform for dynamic datasets since all three different features, such as Unique covariate features (UCF), Intensity features (IF), and Colour features (CF), are considered very well.

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“…Kriging also has certain expansion, such as Universal Kriging(UK) (Xuan Thanh et al, 2015), Kriging with External Drift(KED) (Berndt et al, 2014). Wu et al (2021) uses geographical map spot as basic mapping units. In comparison with traditional regular grid-based methods, it achieves higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction Using Random Forest Spatial Interpalation with Sample Augmentation: A Case Study for Precipitation Mapping

Sijia

Luo

et al. 2022

Preprint

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Spatial prediction (SP) based on machine learning (ML) has been applied to soil water quality, air quality, marine environment, etc. However, there are still deficiencies in dealing with the problem of small samples. Normally, ML require large amounts of training samples in order to prevent overfitting. The data augmentation method of mixup and synthetic minority over-sampling technique (SMOTE) ignores the similarity of geographic information. Therefore, this paper proposes a modified upsampling method and combines it with the random forest spatial interpolation (RFSI) to deal with the small sample problem in geographical space. The modified unsampling mainly reflected in the following two aspects. Firstly, in the process of selecting nearest points, it is to select points with similar geographic information in some aspects of the category after classification. Secondly, the selected difference is the difference of each category. In order to verify the effectiveness of the proposed method, we select precipitation as the target factor and conduct a comparative experiment. The experimental results show that the combination of the modified upsampling method and RFSI effectively improves the accuracy of spatial prediction.

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Geoparcel-Based Spatial Prediction Method for Grassland Fractional Vegetation Cover Mapping

Abstract: Grassland resources guarantee the balance of ecosystems and the sustainable development of animal husbandry.

Cited by 8 publications

References 59 publications

Research on Classification of Grassland Degeneration Indicator Objects Based on UAV Hyperspectral Remote Sensing and 3D_RNet-O Model

Research on Classification of Grassland Degeneration Indicator Objects Based on UAV Hyperspectral Remote Sensing and 3D_RNet-O Model

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Spatial Prediction Using Random Forest Spatial Interpalation with Sample Augmentation: A Case Study for Precipitation Mapping

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