Review of Remote Sensing Applications in Grassland Monitoring

Abstract

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.

Relationship between plant species diversity and aboveground biomass in alpine grasslands on the Qinghai–Tibet Plateau: Spatial patterns and the factors driving them

“…The optimal AGB model of this study had an R 2 of 0.55, which is less accurate than previous simulations of the AGB of grasslands in the Three-River Headwaters Region according to the studies of Liang et al (2016) (R 2 of 0.701) and Wang et al (2022) (R 2 of 0.60) but more accurate than the study of Wang et al (2018) (R 2 of 0.31). The optimal model of Liang et al (2016) used grassland height, which has a direct relationship with productivity; consequently, their inverse AGB accuracy is higher.…”

“…However, there is still much uncertainty in the inverse of grassland height. In contrast, Wang et al (2022) used 1,620 samples obtained over 10 years, on the one hand the sample size was larger, and on the other hand the model was trained for environmental changes over a 10 year period, so the model accuracy was higher than this study. The R 2 of the optimal model in this study was 0.60, and the RMSE was 1.65 n/m 2 in the simulation of PSD; the RMSE of the RF model in Zhao et al (2022) was 1.94 n/m 2 , and the RMSE of the optimal HASM-XGBoot model reached 1.19 n/m 2 .…”

“…The accuracy of the variable model was highest according to the STEP method, which is consistent with the results of Ge et al (2022) showing that the accuracy of the variable model was highest when the STEP method was used. Wang et al (2022) used three variable selection methods, although they did not use the STEP method, their results show that variable selection improves model accuracy. The STEP method used in this study introduces variables into the model one by one and eliminates insignificant variables, thus ensuring that the final set of explanatory variables obtained is optimal.…”

“…However, the PSD-AGB relationship varies with the spatial scale of the study (Chase and Leibold, 2002), Ni et al (2007) showed that the PSD-AGB relationship varies at different ecological scales and geographic scales. Previous studies have been limited by the inability to achieve large scale PSD and AGB, so the PSD-AGB relationship at large scales is still inadequate, while the current remote sensing-based technology can provide high precision estimation of PSD and AGB at the large scales (Choe et al, 2021;Wang et al, 2022), which providing us with the possibility to study the spatial relationship between the two. In addition, the relationship between species diversity and biomass is not only influenced by multiple environmental factors but also by spatial factors (Spyros Tsiftsis, 2018;Li et al, 2020;Du et al, 2022;Ma et al, 2022).…”

Remote sensing as a tool of biological conservation and grassland monitoring in mountain areas of Southeastern Kazakhstan

“…Still, they will also challenge lowland communities that depend on the mountains' ecosystem services, such as freshwater, for their livelihoods. Timely and accurate monitoring of grassland changes and understanding the degree of degradation are crucial to elaborate adequate policy for the sustainable use and conservation of unique mountain ecosystems (Wang et al, 2022). A series of studies propose new approaches and algorithms to assess different characteristics of mountain grasslands.…”