The Retrieval of Forest and Grass Fractional Vegetation Coverage in Mountain Regions Based on Spatio-Temporal Transfer Learning

Huang, Yuxuan; Zhou, Xiang; Lv, Tingting; Tao, Zui; Zhang, Hongming; Li, Ruoxi; Zhai, Mingjian; Liang, Houyu

doi:10.3390/rs15194857

Cited by 3 publications

(1 citation statement)

References 66 publications

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“…Protecting and increasing vegetation coverage contribute to achieving the goals of ecological, economic, and social sustainability. Therefore, protecting and increasing vegetation coverage are important measures for maintaining ecological balance, protecting the environment, and enhancing human well-being [2]- [9] .…”

Section: Introductionmentioning

confidence: 99%

Estimation of vegetation coverage based on dimidiate pixel model using remote sensing data in Songhua Lake basin, Jilin Province

Cao,

Zhang,

Yang

et al. 2024

International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024)

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Section: Introductionmentioning

confidence: 99%

Estimation of vegetation coverage based on dimidiate pixel model using remote sensing data in Songhua Lake basin, Jilin Province

Cao,

Zhang,

Yang

et al. 2024

International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024)

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Climate and Altitude Drive Spatial and Temporal Changes in Forests on the Eastern Tibetan Plateau—Evidence from the Shaluli Mountain

Feng,

Shi,

Niu

et al. 2024

Forests

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Forests are widely distributed in terrestrial ecosystems, covering about one-third of the global land area. They play a key role in sequestering carbon, releasing oxygen, mitigating climate change, and maintaining ecosystem balance. The ecology of the Tibetan Plateau is very fragile, but the impact of environmental change on regional forest ecosystems is not yet clear. Located in the Eastern Tibetan Plateau, the Shaluli Mountain has the richest biodiversity and the widest distribution of forests on the Tibetan Plateau. Assessing the dynamics of forest change is the basis for correctly formulating forest management measures, and is important for regional biodiversity conservation. However, traditional field surveys have the shortcomings of high cost, being time-consuming, and having poor regional coverage in forest dynamics monitoring. Remote sensing methods can make up for these shortcomings. Therefore, in this study, satellite remote sensing images were used to extract forest information from 2000 to 2020 in Shaluli Mountain, and the main drivers of forest change were analyzed with full consideration of the Spatially Stratified Heterogeneity (SSH) of environmental factors. The results found that the forest area increased from 23,144.20 km2 in 2000 to 28,429.53 km2 in 2020, and the average Percentage of Forest Cover (PFC) increased from 19.76% to 21.67%, with significant improvement in forest growth. The annual minimum temperature (TMN), altitude, annual maximum temperature (TMX), and annual precipitation (PRE) were the main driving factors of forest change, with an average driving power (q-value) of 0.4877, 0.2706, 0.2342, and 0.2244, and TMN was the primary limiting factor for forest growth. In addition, the driving power of all environmental factors on forest change increased from 2000 to 2020. The results of this study can provide a basis for the development of forest management strategies, and provide reference materials for regional biodiversity conservation.

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A Multispectral Feature Selection Method Based on a Dual-Attention Network for the Accurate Estimation of Fractional Vegetation Cover in Winter Wheat

Yang,

Li,

Zhang

et al. 2024

Remote Sensing

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Spectral information plays a crucial role in fractional vegetation cover (FVC) estimation, and selecting the appropriate spectral information is essential for improving the accuracy of FVC estimation. Traditionally, spectral feature selection is primarily guided by physical mechanisms or empirical statistical models. This has led to the use of multispectral and hyperspectral images, which often result in missing or redundant information, thereby decreasing the efficiency and accuracy of FVC estimation. This study proposes a novel dual-attention network to select the feature bands of Sentinel-2 multispectral images for the accurate FVC estimation of winter wheat. In the first step, the importance of hyperspectral band reflectances was determined using simulated data from the PROSAIL model, by combining the dual-attention mechanism with the convolutional neural network (DAM-CNN). In the second step, the importance of Sentinel-2 multispectral bands was converted from the hyperspectral band importance identified in the previous stage, and subsequently ranked accordingly. Based on the feature ranking results, multispectral simulated data translated from hyperspectral simulated data were used for CNN training, and multispectral feature selection was conducted based on FVC accuracy. Finally, the selected features were assessed based on their performance in FVC estimation using a CNN model with real data. The experimental results indicate that during the key growth period of winter wheat, the combination of red, green, and red-edge bands significantly influences the FVC estimation accuracy. Band 3 (Green), band 4 (Red), band 5 (Red-edge 1), and band 6 (Red-edge 2) of Sentinel-2 satellite images contribute most significantly to winter wheat FVC estimation, achieving an accuracy comparable to that obtained using all bands, while reducing the training time by 19.1%, as confirmed by field survey data.

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The Retrieval of Forest and Grass Fractional Vegetation Coverage in Mountain Regions Based on Spatio-Temporal Transfer Learning

Cited by 3 publications

References 66 publications

Estimation of vegetation coverage based on dimidiate pixel model using remote sensing data in Songhua Lake basin, Jilin Province

Estimation of vegetation coverage based on dimidiate pixel model using remote sensing data in Songhua Lake basin, Jilin Province

Climate and Altitude Drive Spatial and Temporal Changes in Forests on the Eastern Tibetan Plateau—Evidence from the Shaluli Mountain

A Multispectral Feature Selection Method Based on a Dual-Attention Network for the Accurate Estimation of Fractional Vegetation Cover in Winter Wheat

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