Comprehensive Research on Remote Sensing Monitoring of Grassland Degradation: A Case Study in the Three-River Source Region, China

et al. 2022

Sustainability

Identifying the relative contributions of climate change and human activities to alpine grassland dynamics is critical for understanding grassland degradation mechanisms. In this study, first, the actual NPP (NPPa) was obtained by MOD17A3. Second, we used the Zhou Guangsheng model to simulate the potential met net primary productivity (NPPp). Finally, the NPP generated by anthropogenic activities (NPPh) was estimated by calculating the difference between NPPp and NPPa. Then, the relative contributions of climate change and human activities to NPP changes in grasslands were quantitatively assessed by analyzing trends in NPPp and NPPa. Thereby, the drivers of NPP change in the Yellow River source grassland were identified. The results showed that the temperature and precipitation in the study area showed a warm-humid climate trend from 2000 to 2020. The NPPp and NPPa increased at a rate of 1.07 g C/m2 and 1.51 g C/m2 per year, respectively, while the NPPh decreased at a rate of 0.46 g C/m2 per year. It can be seen that human activities had a positive effect on the change of NPP in the Yellow River source grassland from the change rate. The relative contribution analysis showed that 55.90% of grassland NPP increased due to climate change, 40.16% of grassland NPP increased due to human activities, and the grassland degradation was not significant. The research results can provide a theoretical basis and technical support for the next step of the Yellow River source grassland ecological protection project.

Section: Discussionmentioning

confidence: 56%

Change in Alpine Grassland NPP in Response to Climate Variation and Human Activities in the Yellow River Source Zone from 2000 to 2020

et al. 2022

Sustainability

“…A feature space proposed in [41] for monitoring desertification degree of semiarid grasslands was constructed by MSAVI and surface albedo, which generated a new index called the semiarid steppe desertification index. Zhang et al [42] took the disturbance of climate fluctuations into account so that a new index, climate utilization efficiency (CUE), was constructed based on NPP to represent this disturbance. Then, CUE, NPP, FVC, and surface bareness were processed by PCA to further build a new comprehensive index for monitoring the grassland degradation in the Three-River Source Region of China.…”

Section: Grassland Degradation Monitoringmentioning

confidence: 99%

“…In addition, for different research purposes, other indices such as the land surface water index (LSWI) and some red-edge indices have been fully utilized [39,40]. In some studies [41][42][43], researchers can even customize some new indices depending on the characteristics of grasslands under study. In [44], Xue and Su detailed the spectral characteristics of vegetation and summarized the development of over 100 vegetation indices and their specific applicability and representation under different conditions.…”

Section: Introductionmentioning

confidence: 99%

Review of Remote Sensing Applications in Grassland Monitoring

Wang

et al. 2022

Remote Sensing

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.

“…The indicators to evaluate grassland vegetation dynamic by remote sensing technology mainly include the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), vegetation coverage (F v ), and net primary productivity (NPP) [6][7][8]. Some recent studies have proposed ground bareness (F b ) as another important parameter of global land cover change [9,10]. As an opposite concept of F v , F b contains the attribute of surface reflectivity and temperature information of grassland vegetation rather than a complementary set of coverage.…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Grassland Dynamic and Its Response to Drought Based on Multi-Index in the Mongolian Plateau

Wang

et al. 2022

Plants

Self Cite

This study applied grassland related multi-index and assessed the effects of climate change by investigating grassland responses to drought. This process was performed to study grassland vegetation dynamic accurately and evaluate the effect of drought in the Mongolian Plateau (MP). The spatial–temporal characteristics of grassland dynamic in terms of coverage (Fv), surface bareness (Fb), and net primary production (NPP) from 2000 to 2013 were explored. We implemented the maximum Pearson correlation to analyze the grassland vegetation in response to drought by using self-calibrating Palmer Drought Severity Index (scPDSI). Results show that Fv and NPP present an increasing trend (0.18 vs. 0.43). Fb showed a decreasing trend with a value of −0.16. The grassland Fv and NPP positively correlated with scPDSI, with a value of 0.12 and 0.85, respectively, and Fb was −0.08. The positive correlation between Fv and NPP accounted for 84.08%, and the positive correlation between Fv and scPDSI accounted for 93.88%. On the contrary, the area with a negative correlation between Fb and scPDSI was 57.43%. The grassland in the MP showed a recovery tendency. The increase in grassland caused by positive reaction was mainly distributed in the middle of Mongolia (MG), whereas that caused by counter response was mainly distributed in the east and west MG and northeast Inner Mongolia autonomous region of China (IM). The relevant results may provide useful information for policymakers about mitigation strategies against the inverse effects of drought on grassland and help to ease the losses caused by drought.