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Sulfur dioxide (SO2) is a major atmospheric pollutant and abiotic stressor. Although physiological studies on abiotic stressors have focused on fully expanded leaves, the resistance of leaf functional traits to SO2 during individual leaf development has not been studied. Thus, this study aimed to conduct SO2 static artificial fumigation experiments to evaluate changes in leaf functional traits and resistance to SO2 for three common landscape tree species (Syringa oblata Lindl. (S. oblata), Prunus cerasifera var. atropurpurea Jack. (P. cerasifera), and Ulmus pumila 'Jinye' (U. pumila)) in Changchun City and ontogeny under SO2 stress. Samples were collected on three days in autumn (1 September, 9 September, and 19 September 2019) for two different leaf stages (10 days and 40 days). In addition, remote sensing data were combined to explore the resistance mechanisms of broadleaf forests to different SO2 concentration classes during different seasons on a large scale. The results showed that the chlorophyll content, leaf temperature, green-peak reflectance, and Fv/Fm (maximal photochemical efficiency) at 10 days were significantly lower than that at 40 days, regardless of sampling date or SO2 concentration. Additionally, in general the SO2 resistance for 10 days leaves was consistently smaller than those for 40 days leaves in 3 tree species. On 9 September, 10 days leaves of the three tree species showed different leaf resistance performances under different SO2 concentrations in the order: P. cerasifera > S. oblata > U. pumila. Lastly, the extent of resistance decreased with increasing ρ(SO2) classes in different seasons, and the SO2 resistance was affected by season. We conclude that mature leaves are more resistant to SO2 stress than young leaves are. These results will provide scientific guidance on artificial plant community construction and prevention of future vegetation degradation. [ABSTRACT FROM AUTHOR]

• An integrated susceptibility index was constructed based on the "Soil-Plant-Atmosphere continuum". • The central and southeastern regions of Shandong province were more susceptible to pine caterpillar outbreaks than the rest. • Susceptibility of pine caterpillar outbreaks increased with time. • Synergism between environmental factors induced greater susceptibility of pine caterpillar outbreaks. Outbreaks of the pine caterpillar (Dendrolimus spp.) have substantial impacts on coniferous forest ecosystem structure, distribution, and productivity, as well as the economy. Furthermore, there is emerging evidence that increasing frequencies of fires and windstorms driven by climate change are contributing to more severe pine caterpillar outbreaks. Therefore, there is a need to identify areas susceptible to pine caterpillar outbreaks to apply prevention measures efficiently and facilitate sustainable forest management. This study constructed an integrated index for assessing the susceptibility of forest ecosystems in Shandong Province, China to pine caterpillar outbreaks based on the indicators of mainly forest structure, climate, topography, landform, and soil conditions. This index suggests the risk and susceptibility of a forest area to a pine caterpillar outbreak. In addition, a geodetector model was used to identify the synergistic or antagonistic effects of environmental factors on risk of pine caterpillar outbreak. The results showed that: (1) mountain and hilly areas are more susceptible to pine caterpillar outbreaks, whereas surrounding areas show a medium susceptibility in central and southeastern part of Shandong Province; (2) there was an increasing susceptibility of the study area to pine caterpillar outbreak in the 1990s, 2000s and 2010s; (3) synergism between certain topography and climate variables enhanced the susceptibility of the study area to pine caterpillar outbreaks, including the interaction between elevation and sunshine hours and between slope and humidity. The integrated susceptibility index was shown to perform well in indicating the risk and susceptibility of forest ecosystems to pine caterpillar outbreaks. The present study can act as a reference for forest management within the identification of areas with high susceptibility to pine caterpillar outbreak. [ABSTRACT FROM AUTHOR]

Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R2 value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data. [ABSTRACT FROM AUTHOR]

In the context of climate change, the remote sensing identification of crops is extremely important for the rapid development of agricultural economy and the detailed assessment of the agro-meteorological disasters. The Jilin Province is the main grain production area in China, with a reputation of being a "golden corn belt". The main crops in the Jilin Province are rice, corn, and soybean. A large amount of remote sensing data and programming codes from the Google Earth engine (GEE) platform allow for large-area farmland recognition. However, the substantial amount of crop sample information hinders the mapping of crop types over large farmland areas. To save costs and quickly and accurately map the crop types in a study area, multi-source remote sensing data and historical crop labels based on the GEE platform were used in this study, together with the random forest classification method and optimal feature selection to classify farming areas in the Jilin Province. The research steps were as follows: (1) select samples based on the historical crop layer of the farmland; and (2) obtain the classification characteristics of rice, corn, and soybean using multi-source remote sensing data, calculating the feature importance scores. Using different experimental combinations, an optimal classification method was then selected to classify crops in the Jilin Province. The results indicated variable impacts of vegetation indices (of different periods) on crop classification. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and green chlorophyll vegetation index (GCVI) in June exerted a significant impact on the classification of rice, corn, and soybean, respectively. The overall accuracy of crop classification during different periods based on historical crop labels reached 0.70, which is acceptable in crop classification research. The study results demonstrated that the proposed method has promising potential for mapping large-scale crop areas. [ABSTRACT FROM AUTHOR]

Global warming has increased the prevalence and severity of natural disasters, such as the increased frequency and intensity of drought events. In this study, we employed the standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI) to analyze and predict the temporal and spatial variation of drought and its characteristics in the Songliao Plain under two global warming scenarios (1.5 and 2 °C). We used climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project. The results show that drought will become more frequent in the future; it is more serious at representative concentration pathway (RCP) 8.5 than RCP4.5 and under global warming of 1.5 °C than 2 °C. Geographically, the SPEI indicates that there are signs of drought in the northwest and northeast of Songliao Plain while the SPI indicates that drought decreases from north to south. In terms of drought characteristics, the drought barycenter expressed by the SPEI moves to the northeast, while the drought barycenter expressed by the SPI moves to the northwest. The SPI relies on a single meteorological factor, making the SPI mutation test more stable. Finally, compared to the historical period (1976–2005), the frequency and duration of drought have increased, and it is more reliable to use the SPEI to monitor drought in this area. [ABSTRACT FROM AUTHOR]

• DPSIRM model was used to evaluate the ecological security of Xilingol grassland. • Remote sensing, meteorology, socio-economic and survey data are used in this paper. • We use grid GIS method to obtain more accurate evaluation results. • We analyzed the spatial-temporal evolution characteristics of ecological security. • Changes in the center of gravity in different security levels are also explored. The quantitative evaluation of the ecological security can reflect the changes in the quality of ecological environment and provide a scientific basis for the formulation of land-use policy and for the construction of the ecological environment. In this study, we selected Xilingol League in Inner Mongolia, China, as the research area, and established the driving force-pressure-state-impact-response-management (DPSIRM) model to evaluate grassland ecological security. The results were as follows: (1) During 2005–2015, the overall trend of ecological security level in the study area increased, with the western region being relatively ecologically insecure and the eastern region being more secure. (2) The spatial distribution trend of ecological security level from west to east was " very serious alerts " → " serious alerts " → " moderate alerts " → " light alerts "→ " no alert ". (3) The " very serious alerts " and " serious alerts " are mainly concentrated in Sonid Left Banner and Sonid Right Banner, but from the perspective of spatial migration of ecological security, unsafe and less safe tend to shift to the northeast. This study provides a new idea for grassland ecological security evaluation research, has important practical significance for understanding the ecological security level of Xilingol grassland area, and will guide the reasonable development and utilization of Xilingol grassland. [ABSTRACT FROM AUTHOR]

• Regions with lower ecological security index changed from northwest to southeast. • The center of gravity concentrated in the middle of the watershed. • Add ecological function area index for ecological security evaluation. • Adjust the proportion of land use types, can improve the level of ecological security. • Countermeasures and approaches of ecological security regulation are put forward. Watershed ecological security provides the basic guarantee of regional sustainable development; thus, reliable watershed ecological security evaluation is of great significance in revealing the watershed ecological security status and promoting the healthy development of the watershed society and ecological environment. To construct the ecological function area, this study used the East-Liao River basin as the analysis object and discussed the delimitation method of ecological security evaluation, which provided the reference and basis for studying and exploring the optimal ecological regulation method of the basin. The following results were obtained: from 2000 to 2020, the regions with lower ecological security index changed from northwest to southeast of the watershed, and presented the spatial distribution of "unsafe" → "generally safe" → "relatively safe" → "safe", with the center of gravity concentrated in the middle of the watershed; the spatial differences of ecological security became significant with the addition of ecological function zones, and they show a certain spatial aggregation trend; from 2000 to 2020, the ecological security level was predominantly safer, and the distribution of gravity center showed a circular state; the ecological security level of the watershed can be improved by adjusting the proportion of regional land use types. According to the results, countermeasures and approaches of watershed ecological security regulation were suggested, and four subareas were established: forbidden development zones, traditional utilization zones, ecologically sensitive/fragile zones, and key ecological function zones; this would provide a basis for further analysis of environmental governance in the East-Liao River basin of Jilin Province. Nevertheless, the countermeasures and approaches of ecological security regulation proposed in this study will also be useful for land use planning and runoff management in other watersheds around the world. [ABSTRACT FROM AUTHOR]

• Overall, climate warming is beneficial to sugarcane distribution. • There is a risk of drought in the main sugarcane producing areas. • The cropland fragmentation is significant in sugarcane climate suitable area. • The sugar industry has faced challenges seriously in recent years under global warming and human factors. Guangxi is the primary producer of sugarcane in China and provides a highly suitable habitat for sugarcane growth. However, its distribution range has changed significantly in recent years due to climate change as well as human factors. Without extensive knowledge of the changing trends in suitable sugarcane planting areas, efforts to improve its productivity in Guangxi may be insufficient. In this study, the interdecadal change in sugarcane distribution in Guangxi in response to climate change from 1960 to 2019 was estimated using the MaxEnt model and the landscape pattern of land use in the suitable sugarcane area was analyzed. In addition, we discuss the effects of global warming on sugarcane production in the sustainable development of the sugar industry in Guangxi. Our results indicate: (1) from 1960 to 2019, approximately 65% of Guangxi Province could grow sugarcane. Chongzuo City, Nanning City and Parts of Baise City, are highly suitable areas, and unsuitable areas are mainly concentrated in the north. In general, sugarcane climate suitability extended further in low-altitude areas, and then extended to high- altitude areas. However, from the 2000s to the 2010s, climate suitability showed a decreasing trend, decreasing from 16.036 × 106 ha to 15.4985 × 106 ha (2) The order of land use area in the suitable sugarcane climate range was as follows: woodland > cropland > grassland > construction land > water. With the increase in climate suitability, the distribution of cultivated land expanded. From 1980 to 2005, cropland in suitable areas showed a fragmentation trend. By 2010, the cropland patches disappeared after fragmentation. (3) Due to landscape constraints, infertile soil, and labor costs, the sugar industry faces various challenges. The evaluation of climate suitability could provide a theoretical reference for a planting layout of sugarcane, and landscape pattern analysis of suitable sugarcane climate areas is conducive to the integration of small pieces of land into large ones, making mechanization possible. Overall, strict layout and management measures are required in sugarcane planting areas. [ABSTRACT FROM AUTHOR]