A new method for grassland degradation monitoring by vegetation species composition using hyperspectral remote sensing

Lyu, Xin; Li, Xiaobing; Dang, Dongliang; Dou, Huashun; Xuan, Xiaojing; Liu, Siyu; Li, Mengyuan; Gong, Jirui

doi:10.1016/j.ecolind.2020.106310

Cited by 46 publications

(29 citation statements)

References 39 publications

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“…Meanwhile, Lyu et al [8] monitored the degradation from a new perspective of the vegetation species composition in the degraded grasslands. In detail, they first extracted spectral features from EO-1 hyperspectral images and reduced their dimensionality via PCA.…”

Section: Grassland Degradation Monitoringmentioning

confidence: 99%

“…In addition, the number of studies using hyperspectral images is significantly less than that of the multispectral. [126] threshold-based FVC RF NDVI, MSAVI Sternberg et al [125] threshold-based FVC linear regression NDVI Wang et al [129] threshold-based NPP CASA NDVI, LAI product Zhumanova et al [130] threshold-based FVC univariate regression NDVI Xu et al [128] threshold-based bare-sand ratio mixed pixel decomposition multispectral bands Reiche et al [121] supervised maximum-likelihood / / vegetation indices Mansour et al [10] RF / / multispectral bands Li et al [122] multiresolution segmentation / / multispectral bands Wu et al [41] feature space / / vegetation indices Yang et al [133] multivariate statistical analysis / / vegetation indices Lyu et al [8] MESMA, FCLS / / hyperspectral bands Pi et al [23] convolution neural network / / hyperspectral bands Guo et al [134] linear regression, feature space / / albedo index, MSAVI Han et al [131] multivariate hierarchical analysis / / vegetation indices, products Pi et al [124] 3D convolution neural network / / hyperspectral bands Li et al [123] CIMD, stepwise discriminant function / / hyperspectral bands…”

Section: Grassland Degradation Monitoringmentioning

confidence: 99%

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Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

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The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.

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Section: Grassland Degradation Monitoringmentioning

confidence: 99%

Section: Grassland Degradation Monitoringmentioning

confidence: 99%

Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

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“…These species will gradually replace the dominant grass species and foundation species. This change in grassland vegetation structure is also an important feature of grassland degradation [60]. Due to the limitations of current data acquisition, the feature was not considered in this study.…”

Section: Limitations and Future Research Directionsmentioning

confidence: 99%

Comprehensive Grassland Degradation Monitoring by Remote Sensing in Xilinhot, Inner Mongolia, China

Lyu

Gong

et al. 2020

Sustainability

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Grassland degradation is a complex process and cannot be thoroughly measured by a single indicator, such as fractional vegetation cover (FVC), aboveground biomass (AGB), or net primary production (NPP), or by a simple combination of these indicators. In this research, we combined measured data with vegetation and soil characteristics to establish a set of standards applicable to the monitoring of regional grassland degradation by remote sensing. We selected indicators and set their thresholds with full consideration given to vegetation structure and function. We optimized the indicator simulation, based on which grassland degradation in the study area during 2014–2018 was comprehensively evaluated. We used the feeding intensity of herbivores to represent the grazing intensity. We analyzed the effects of climate and grazing activities on grassland degradation using the constraint line method. The results showed degradation in approximately 69% of the grassland in the study area and an overall continued recovery of the degraded grassland from 2014 to 2018. We did not identify any significant correlation between temperature and grassland degradation. The increase in precipitation promoted the recovery of degraded grassland, whereas increased grazing may have aggravated degradation. Our findings can not only improve the scientific quality and accuracy of grassland degradation monitoring by remote sensing but also provide clear spatial information and decision-making help in sustainable management of grassland regions.

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“…The production regulations of these cheeses stipulate that the alimentation of cattle must rely on local forage for the most part of their diet, although this forage is becoming less available due to grassland abandonment (Figure 1). VegeT cannot substitute other tools/procedures to evaluate the forage quantity and quantity neither the biological values of grasslands [3,9,18,[65][66][67][68]. However, it can be combined with other tools to facilitate the definition and planning of the best management practices for maintaining grasslands that, other than being a biodiversity heritage, could be excellent resources for agro-food production chains [69].…”

Section: Vegetmentioning

confidence: 99%

VegeT: An Easy Tool to Classify and Facilitate the Management of Seminatural Grasslands and Dynamically Connected Vegetation of the Alps

Giupponi

Leoni

2020

Land

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Alpine pastures and meadows are agroecosystems of biological, cultural-historical, and economic importance that are undergoing profound imbalances and which are in a rapid decline due to changes in management and/or abandonment. The European Union is making efforts to protect this heritage and resource. However, the dialog among the different professionals in charge of studying and managing these agroecosystems needs to be as easy and comprehensible as possible for grasslands conservation/restoration actions to be successful. This research introduces VegeT, an easy-to-use tool to facilitate information transfer between botanists and practitioners responsible for providing guidelines for the correct management of mountain grasslands. VegeT is a Microsoft Excel® worksheet that allows the classification of seminatural grasslands and dynamically connected vegetation (shrublands and forests) of the Alps employing two ecological indexes: the index of nutrients (N) and the index of mowing tolerance (MV). VegeT was elaborated upon the floristic-ecological analysis of the vegetation of Taleggio Valley (Italian Alps) performed applying multivariate analysis techniques. From the analyses, it emerged that N and MV are the main variables on which to base a classification system of alpine mountain grasslands and dynamically connected vegetation able to facilitate the interpretation of floristic-vegetation data and to return useful information for management decisions.

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A new method for grassland degradation monitoring by vegetation species composition using hyperspectral remote sensing

Cited by 46 publications

References 39 publications

Review of Remote Sensing Applications in Grassland Monitoring

Review of Remote Sensing Applications in Grassland Monitoring

Comprehensive Grassland Degradation Monitoring by Remote Sensing in Xilinhot, Inner Mongolia, China

VegeT: An Easy Tool to Classify and Facilitate the Management of Seminatural Grasslands and Dynamically Connected Vegetation of the Alps

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