Cross-scale mapping of above-ground biomass and shrub dominance by integrating UAV and satellite data in temperate grassland

Chen, Ang; Xu, Cong; Zhang, Min; Guo, Jian; Xing, Xiaoyu; Yang, Dong; Xu, Bin; Yang, Xiuchun

doi:10.1016/j.rse.2024.114024

Cited by 9 publications

(2 citation statements)

References 91 publications

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“…Grasslands are among the most important global ecosystems, covering about 40% of all land area and providing a wide range of ecosystem services. They not only maintain biodiversity, regulate climate, provide food, and conserve water but also serve as an essential carbon reservoir [1]. Grasshoppers are the most widely distributed and common type of insects in natural grasslands.…”

Section: Introductionmentioning

confidence: 99%

Changes in the Range of Four Advantageous Grasshopper Habitats in the Hexi Corridor under Future Climate Conditions

Li,

Gan,

et al. 2024

Insects

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Angaracris rhodopa (Fischer et Walheim), Calliptamus abbreviatus (Ikonnikov), Myrmeleotettix palpalis (Zubowsky), and Oedaleus decorus asiaticus (Bey-Bienko) are the main grasshoppers that harm the natural grassland in the Hexi Corridor in Gansu, northwest China. In this study, the MaxEnt model was employed to identify the key environmental factors affecting the distribution of the four grasshoppers’ habitats and to assess their distribution under current and future climate conditions. The aim was to provide a basis for grasshopper monitoring, prediction, and precise control. In this study, distribution of suitable habitats for A. rhodopa, C. abbreviates, M. palpalis, O. decorus asiaticus were predicted under current and future climatic scenarios using the Maxent model. The average AUC (area under the ROC curve) and TSS (true skill statistic) values of the four grasshoppers were greater than 0.9, and the simulation results were excellent and highly reliable. The mean annual precipitation was the main factor limiting the current range of suitable areas for these four species. Under the current climate, A. rhodopa, C. abbreviatus, and O. decorus asiaticus were mainly distributed in the central and eastern parts of the Hexi Corridor, and M. palpalis was distributed throughout the Hexi Corridor, with a suitable area of 1.29 × 104, 1.43 × 104, 1.44 × 104, and 2.12 × 104 km2, accounting for 13.7%, 15.2%, 15.3%, and 22.5% of the total area of the grasslands in the Hexi Corridor, respectively. The highly suitable areas of A. rhodopa, C. abbreviatus, and O. decorus asiaticus were mainly distributed in the eastern-central part of Zhangye City, the western part of Wuwei City, and the western and southern parts of Jinchang City, with areas of 0.20 × 104, 0.29 × 104, and 0.35 × 104 km2, accounting for 2.2%, 3%, and 3.7% of the grassland area, respectively. The high habitat of M. palpalis was mainly distributed in the southeast of Jiuquan City, the west, middle, and east of Zhangye City, the west of Wuwei City, and the west and south of Jinchang City, with an area of 0.32 × 104 km2, accounting for 3.4% of the grassland area. In the 2030s, the range of A. rhodopa, C. abbreviatus, and O. decorus asiaticus was predicted to increase; the range of M. palpalis will decrease. The results of this study could provide a theoretical basis for the precise monitoring and control of key areas of grasshoppers in the Hexi Corridor.

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Section: Introductionmentioning

confidence: 99%

Changes in the Range of Four Advantageous Grasshopper Habitats in the Hexi Corridor under Future Climate Conditions

Li,

Gan,

et al. 2024

Insects

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“…Unmanned aerial vehicles (UAVs) have been developed rapidly in recent years. Compared to satellites, UAVs have the advantage of providing near-Earth information with a high spatiotemporal resolution and easily controlled flight [11]. A UAV as a platform combined with spectral sensors has become an efficient method for biomass estimation of various crops.…”

Section: Introductionmentioning

confidence: 99%

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

Hu,

Zhou,

Cao

et al. 2024

Remote Sensing

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Milk vetch (Astragalus sinicus L.) is a winter-growing plant that can enhance soil fertility and provide essential nutrients for subsequent season crops. The fertilizing capacity of milk vetch is closely related to its above-ground biomass. Compared to the manual measurement methods of milk vetch biomass, remote sensing-based estimation methods have the advantages of rapid, noninvasive, and large-scale measurement. However, few studies have been conducted on remote sensing-based estimation of milk vetch biomass. To address this shortcoming, this study proposes combining unmanned aerial vehicle (UAV)-based hyperspectral imagery and machine learning algorithms for accurate estimation of milk vetch biomass. Through the analysis of hyperspectral images and feature selection based on the Pearson correlation and principal component analysis, vegetation indices (VIs), including near-infrared reflectance (NIR), red-edge spectral transform index (RE), and difference vegetation index (DVI), are selected as estimation metrics of the model development process. Four machine learning methods, including random forest (RF), multiple linear regression (MLR), deep neural network (DNN), and support vector machine (SVM), are used to construct the biomass models. The results show that the RF estimation model exhibits the highest coefficient of determination (R2) of 0.950 and the lowest relative root-mean-squared error (RRMSE) of 14.86% among all the models. Notably, the DNN model demonstrates promising performance on the test set, with the R2 and RRMSE values slightly superior and inferior to those of the RF, respectively. The proposed method based on UAV imagery and machine learning can provide an accurate and reliable large-scale estimation of milk vetch biomass.

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UAV or satellites? How to find the balance between efficiency and accuracy in above ground biomass estimation of artificial young coniferous forest?

Tao,

Yi,

Bao

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

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Cross-scale mapping of above-ground biomass and shrub dominance by integrating UAV and satellite data in temperate grassland

Cited by 9 publications

References 91 publications

Changes in the Range of Four Advantageous Grasshopper Habitats in the Hexi Corridor under Future Climate Conditions

Changes in the Range of Four Advantageous Grasshopper Habitats in the Hexi Corridor under Future Climate Conditions

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

UAV or satellites? How to find the balance between efficiency and accuracy in above ground biomass estimation of artificial young coniferous forest?

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