The effects of the interaction between high growth temperatures and water stress on gas-exchange properties of Populus nigra saplings were investigated. Water stress was expressed as a function of soil water content (SWC) or fraction of transpirable soil water (FTSW). Isoprene emission and photosynthesis (A) did not acclimate in response to elevated temperature, whereas dark (Rn) and light (Rd) respiration underwent thermal acclimation. Rd was ~30% lower than Rn irrespective of growth temperature and water stress level. Water stress induced a sharp decline, but not a complete inhibition, of both Rn and Rd. There was no significant effect of high growth temperature on the responses of A, stomatal conductance (gs), isoprene emission, Rn or Rd to FTSW. High growth temperature resulted in a significant increase in the SWC endpoint. Photosynthesis was limited mainly by CO2 acquisition in water-stressed plants. Impaired carbon metabolism became apparent only at the FTSW endpoint. Photosynthesis was restored in about a week following rewatering, indicating transient biochemical limitations. The kinetics of isoprene emission in response to FTSW confirmed that water stress uncouples the emission of isoprene from A, isoprene emission being unaffected by decreasing gs. The different kinetics of A, respiration and isoprene emission in response to the interaction between high temperature and water stress led to rising Rd/A ratio and amount of carbon lost as isoprene. Since respiration and isoprene sensitivity are much lower than A sensitivity to water stress, temperature interactions with water stress may dominate poplar acclimatory capability and maintenance of carbon homeostasis under climate change scenarios. Furthermore, predicted temperature increases in arid environments may reduce the amount of soil water that can be extracted before plant gas exchange decreases, exacerbating the effects of water stress even if soil water availability is not directly affected.

Research paper: part of a special section on adaptations of forest ecosystems to air pollution and climate change Different sensitivity of isoprene emission, respiration and photosynthesis to high growth temperature coupled with drought stress in black poplar (Populus nigra) saplings

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.

/pdf/review-of-remote-sensing-applications-in-grassland-cxkcfc0q.pdf

Review of Remote Sensing Applications in Grassland Monitoring

Vegetation cover fraction (fCover) and related quantities are basic yet critical vegetation structure variables in various disciplines and applications. Ground- and aerial-based proximal and remote sensing techniques have been widely adapted across multiple spatial extents. However, the definitions of fCover-related nomenclatures have not yet been fully standardized, leading to confusing terms and making comparing historic measures difficult. With the issues potentially arising from an increasing diversity of fCover and related quantities estimation methods and corresponding uncertainties, there is also a growing need to spread knowledge on the current advances, challenges, and perspectives, especially in the context of no such existing review for ground- and aerial- based estimation. This paper provides the current knowledge mainly concerning passive image-based methods and active light detection and ranging (LiDAR) -based methods. We first harmonized the definitions of fCover and its related quantities (e.g., effective canopy cover, crown cover, stratified vegetation cover, and canopy fraction). Secondly, the typical applications of fCover and related quantities over a range of scales, fields, and ecosystems were summarized. Thirdly yet importantly, we offered a comprehensive review of traditional non-imaging methods, image-based methods (e.g., segmentation, unmixing, and spectral retrieval), point cloud-based methods (e.g., rasterization), and LiDAR return-based methods (e.g., return number index and return intensity retrieval) across different platforms (i.e., ground, unmanned aerial vehicle (UAV) and airplane). Our investigation of fCover and related quantities estimation touches upon various vegetation ecosystems, including agriculture cropland, grassland, wetland, and forest. Finally, the current challenges and future directions were discussed, such as image signal processing under complex heterogeneous surfaces and stratified cover and non-photosynthesis cover retrieval. We, therefore, expect that this review may offer an insight into fCover and related quantities estimation and serve as a reference for remote sensing scientists, agronomists, silviculturists, and ecologists. 

Review of ground and aerial methods for vegetation cover fraction (fCover) and related quantities estimation: definitions, advances, challenges, and future perspectives

Grasslands are one of the world’s largest ecosystems, accounting for 30% of total terrestrial biomass. Considering that aboveground biomass (AGB) is one of the most essential ecosystem services in grasslands, an accurate and faster method for estimating AGB is critical for managing, protecting, and promoting ecosystem sustainability. Unmanned aerial vehicles (UAVs) have emerged as a useful and practical tool for achieving this goal. Here, we review recent research studies that employ UAVs to estimate AGB in grassland ecosystems. We summarize different methods to establish a comprehensive workflow, from data collection in the field to data processing. For this purpose, 64 research articles were reviewed, focusing on several features including study site, grassland species composition, UAV platforms, flight parameters, sensors, field measurement, biomass indices, data processing, and analysis methods. The results demonstrate that there has been an increase in scientific research evaluating the use of UAVs in AGB estimation in grasslands during the period 2018–2022. Most of the studies were carried out in three countries (Germany, China, and USA), which indicates an urgent need for research in other locations where grassland ecosystems are abundant. We found RGB imaging was the most commonly used and is the most suitable for estimating AGB in grasslands at the moment, in terms of cost–benefit and data processing simplicity. In 50% of the studies, at least one vegetation index was used to estimate AGB; the Normalized Difference Vegetation Index (NDVI) was the most common. The most popular methods for data analysis were linear regression, partial least squares regression (PLSR), and random forest. Studies that used spectral and structural data showed that models incorporating both data types outperformed models utilizing only one. We also observed that research in this field has been limited both spatially and temporally. For example, only a small number of papers conducted studies over a number of years and in multiple places, suggesting that the protocols are not transferable to other locations and time points. Despite these limitations, and in the light of the rapid advances, we anticipate that UAV methods for AGB estimation in grasslands will continue improving and may become commercialized for farming applications in the near future.

/pdf/a-review-of-estimation-methods-for-aboveground-biomass-in-1nbfeq7j.pdf

A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV

Bayesian model averaging to improve the yield prediction in wheat breeding trials

Background Fractional vegetation cover (FVC) is an important basic parameter for the quantitative monitoring of the alpine grassland ecosystem on the Qinghai-Tibetan Plateau. Based on unmanned aerial vehicle (UAV) acquisition of measured data and matching it with satellite remote sensing images at the pixel scale, the proper selection of driving data and inversion algorithms can be determined and is crucial for generating high-precision alpine grassland FVC products. Methods This study presents estimations of alpine grassland FVC using optimized algorithms and multi-dimensional features. The multi-dimensional feature set (using original spectral bands, 22 vegetation indices, and topographical factors) was constructed from many sources of information, then the optimal feature subset was determined based on different feature selection algorithms as the driving data for optimized machine learning algorithms. Finally, the inversion accuracy, sensitivity to sample size, and computational efficiency of the four machine learning algorithms were evaluated. Results (1) The random forest (RF) algorithm (R2: 0.861, RMSE: 9.5%) performed the best for FVC inversion among the four machine learning algorithms driven by the four typical vegetation indices. (2) Compared with the four typical vegetation indices, using multi-dimensional feature sets as driving data obviously improved the FVC inversion accuracy of the four machine learning algorithms (R2 of the RF algorithm increased to 0.890). (3) Among the three variable selection algorithms (Boruta, sequential forward selection [SFS], and permutation importance-recursive feature elimination [PI-RFE]), the constructed PI-RFE feature selection algorithm had the best dimensionality reduction effect on the multi-dimensional feature set. (4) The hyper-parameter optimization of the machine learning algorithms and feature selection of the multi-dimensional feature set further improved FVC inversion accuracy (R2: 0.917 and RMSE: 7.9% in the optimized RF algorithm). Conclusion This study provides a highly precise, optimized algorithm with an optimal multi-dimensional feature set for FVC inversion, which is vital for the quantitative monitoring of the ecological environment of alpine grassland.

/pdf/improving-the-estimation-of-alpine-grassland-fractional-3jz7yrqcy5.pdf

Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features.

. Drought is an extremely complex natural disaster phenomenon. Sustained drought will lead to the aggravation of water shortage, food production reduction, land desertification and ecological crisis, which will have a great impact on social and economic development, industrial and agricultural production and ecological environment. In recent years, human activities have intensified, the global climate has been warming, and the frequency and intensity of extreme events such as drought have been continuously strengthened, which has been widely concerned by scholars at home and abroad. The Beijing-Tianjin-Hebei region, as the political and cultural center of China, is seriously affected by the drought, which has a certain impact on the social economy of the region. Therefore, it is of great practical significance to carry out remote sensing drought monitoring and understand the temporal and spatial characteristics of drought. Based on MOD13A2 and MOD11A2 in MODIS products, this paper constructed the NDVI-Ts feature space to obtain the Temperature Vegetation Dryness Index (TVDI) in Beijing-Tianjin-Hebei region, and analyzed the spatial and temporal variation characteristics of drought in Beijing-Tianjin-Hebei region from 2001 to 2014 with TVDI as the drought index. The results showed that: (1) the NDVI-Ts feature space in the Beijing-Tianjin-Hebei region showed that when the value of NDVI rose from 0 to 1, the maximum temperature of the surface gradually decreased, while the minimum temperature gradually rose. Both of them had a linear relationship with NDVI, and their spatial shape was similar to a triangle, indicating that the study of drought in Beijing-Tianjin-Hebei was effective by using this spatial feature; (2) the Beijing-Tianjin-Hebei region experienced drought every year in the period of 2001–2014, and the drought was more serious in 2001, 2004, 2008, 2009, 2011 and 2012; (3) The drought in the Beijing-Tianjin-Hebei region was widespread, and the severely arid regions were mainly concentrated in the southern and central regions. In terms of drought intensity, the most frequent occurrences in the period of 2001–2014 were Xingtai, Hengshui, Shijiazhuang, and Handan, and the annual drought in 14 years; the least number of occurrences was Chengde, a total of 4 times; the number of occurrences was second only to Chengde, Tangshan, Zhangjiakou, Qinhuangdao, a total of 7 times. Our results indicated that the Beijing-Tianjin-Hebei region was prone to drought and the drought degree was severe.

https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W10/747/2020/isprs-archives-XLII-3-W10-747-2020.pdf

Temporal and spatial changes of drought in beijing-tianjin-hebei region based on remote sensing technology

. The dynamic change of urban thermal environment caused by the change of land use type has become one of the important problems of urban ecological environment protection. In Guilin city as research area, based on the Google Earth Engine (GEE), the random forest algorithm was used to classify the land use classification of Landsat remote sensing images in 2010, 2014 and 2018, and the mono-window algorithm was used to calculate the surface temperature. The surface vegetation was solved according to the NDVI pixel binary model. Coverage, and finally dynamic statistics and comparative analysis of land use, vegetation cover and surface temperature. The main results as follows. (1) From 2010 to 2018, the average temperature in the main urban area of Guilin is on the rise (increased by 1.29 °C), and the temperature zones in each class are converted from low temperature zone, lower temperature zone and medium temperature zone to higher temperature zone and high temperature zone. (2) Lower temperature zone and the low temperature zone is mainly distributed in vegetation and water body coverage areas, while the medium temperature zone, higher temperature zone and the high temperature zone are mainly distributed in construction land and unused land cover area. (3) High vegetation cover area in 2014–2018 (reduced by 31.34%) The main reason for the sharp decline is the substantial increase in the area of construction land (expansion 30.19%). (4) GEE-based random forest algorithm Land use classification had higher classification accuracy (more than 80% in all three periods). The results can provide scientific basis for improving urban thermal environment and scientific reference for the development strategy of Guilin city.

/pdf/quantitative-remote-sensing-analysis-of-thermal-environment-4dctpn9m9k.pdf

Quantitative Remote Sensing Analysis of Thermal Environment Changes in the Main Urban Area of Guilin Based on Gee

High-precision canopy chlorophyll content (CCC) inversion for marsh vegetation is of great significance for marsh protection and restoration. However, it is difficult to collect the CCC measured data for marsh vegetation that matches the pixel scale of remote sensing image. This article proposes a new method based on unmanned aerial vehicle (UAV) multispectral images to obtain multiscale marsh vegetation CCC sample data. A random forest (RF) regression algorithm was used to evaluate the application performance of GF-1 wide field view (WFV), Landsat-8 Operational Land Imager (OLI), and Sentinel-2 multispectral instrument (MSI) satellite remote sensing data in marsh vegetation CCC inversion. In addition, parameter optimization of the RF regression model was used to construct an optimization algorithm suitable for marsh vegetation, and the importance of input variables was quantitatively evaluated. The results showed that the UAV multispectral images assisted in the acquisition of marsh vegetation CCC sample data, as the method expanded the number of CCC samples while quantifying the CCC sample data collection accuracy [ R 2 ≥ 0.86, root mean square error (RMSE) ≤ 6.98 SPAD], which improved the CCC inversion accuracy compared with traditional sampling methods. Extracting pure vegetation pixels through binary classification reduces the uncertainty of the UAV-scale CCC inversion results. Parameter optimization of the RF regression model further improves the CCC inversion accuracy at GF-1 WFV, Landsat-8 OLI, and Sentinel-2 MSI scales. Among the three satellite remote sensing data, Sentinel-2 MSI achieved the highest CCC inversion accuracy for marsh vegetation ( R 2 = 0.79, RMSE = 10.96 SPAD) due to the inclusion of red-edge bands that are more sensitive to vegetation properties. Red-edge Chlorophyll Index (Clred-edge) and Green Chlorophyll Index (Clgreen) have the highest influence on the CCC inversion accuracy among input variables.

/pdf/an-effective-method-for-canopy-chlorophyll-content-4zp2ligiko.pdf

An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data

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Papers

Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features.

Temporal and spatial changes of drought in beijing-tianjin-hebei region based on remote sensing technology

Quantitative Remote Sensing Analysis of Thermal Environment Changes in the Main Urban Area of Guilin Based on Gee

An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data