The biophysical effects of potential changes in irrigated crops on diurnal land surface temperature in Northeast China

Irrigated crops have experienced a significant global expansion. The biophysical response of climate change to irrigated crop expansion in different regions, particularly in terms of monitoring the influence mechanism of nighttime land surface temperature (LST) change, however, remains insufficiently explored. Taking the three northeastern provinces of China as our study area, we apply window analysis, partial correlation analysis, and geographical detector to quantitatively characterize the spatial and temporal distribution pattern of daytime and nighttime LST (diurnal LST) and biophysical parameters, and the main driving mechanism of diurnal LST change. The results showed that irrigated crop expansion led to asymmetric changes in daytime (−2.11 ± 0.2°C, 97.4%) and nighttime (0.64 ± 0.2°C, 79.9%) LST. ΔLSTDT had a negative correlation with ΔLE (63%), but a positive correlation with ΔSSR and ΔH (91% and 77%). This revealed that the cooling effect caused by the superposition of the output latent heat flux and the absorbed solar shortwave radiation was greater than its heating effect. ΔLSTNT and ΔLE had a positive connection across 69% of the region. ΔLSTNT demonstrated a negative correlation with ΔSSR and ΔH in 82% and 75% of the regions, respectively. At this time, the superposition of latent heat flux and heating potential term produces a greater heating effect. The explanatory power of the single factor (the mean of q<0.50) of biophysical parameters for diurnal LST variation was significantly smaller than that of the interaction factor (the mean of q>0.50, p<0.01). This study shows more detailed dynamic information of diurnal LST and biophysical parameters from 8day scale. The findings highlighted the critical role of asymmetric changes in the diurnal surface thermal environment caused by irrigated crop expansion in the global climate from a land surface hydrothermal energy balance perspective

A multi-proxy reconstruction of spatial and temporal variations in Asian summer temperatures over the last millennium

To investigate climate variability in Asia during the last millennium, the spatial and temporal evolution of summer (June–July–August; JJA) temperature in eastern and south-central Asia is reconstructed using multi-proxy records and the regularized expectation maximization (RegEM) algorithm with truncated total least squares (TTLS), under a point-by-point regression (PPR) framework. The temperature index reconstructions show that the late 20th century was the warmest period in Asia over the past millennium. The temperature field reconstructions illustrate that temperatures in central, eastern, and southern China during the 11th and 13th centuries, and in western Asia during the 12th century, were significantly higher than those in other regions, and comparable to levels in the 20th century. Except for the most recent warming, all identified warm events showed distinct regional expressions and none were uniform over the entire reconstruction area. The main finding of the study is that spatial temperature patterns have, on centennial time-scales, varied greatly over the last millennium. Moreover, seven climate model simulations, from the Coupled Model Intercomparison Project Phase 5 (CMIP5), over the same region of Asia, are all consistent with the temperature index reconstruction at the 99 % confidence level. Only spatial temperature patterns extracted as the first empirical orthogonal function (EOF) from the GISS-E2-R and MPI-ESM-P model simulations are significant and consistent with the temperature field reconstruction over the past millennium in Asia at the 90 % confidence level. This indicates that both the reconstruction and the simulations depict the temporal climate variability well over the past millennium. However, the spatial simulation or reconstruction capability of climate variability over the past millennium could be still limited. For reconstruction, some grid points do not pass validation tests and reveal the need for more proxies with high temporal resolution, accurate dating, and sensitive temperature signals, especially in central Asia and before AD 1400

Parallel acceleration of vegetation growth rate and senescence rate across the Northern Hemisphere from 1982 to 2015

Growth and senescence rates are critical ecological indicators of seasonality shifts of vegetation, with both sensitive to climate change. Here we investigated daily mean vegetation growth and senescence rates, and the major climate forcing across the Northern Hemisphere (>30°N) using satellite-derived normalized difference vegetation index (NDVI) and flux-based gross primary productivity (GPP) from 1982 through 2015. Both growth and senescence rates are higher at high latitudes than those at low latitudes, with spatially-averaged values increased by 1.0 × 10−4 and 0.7 × 10−4 NDVI-units·day−1 per degree latitude. These increases were greater in Eurasia than in North America. A parallel acceleration of growth (0.8 ×10−4 NDVI-units·day−1·decade−1) and senescence (0.6 ×10−4 NDVI-units·day−1·decade−1) rates was found for the 34-year study period. The warming-induced increases in vegetation peak growth (peak NDVI) contributed strongly to this parallel acceleration, while unequal advances or delays of three key phenological indicators (the start (SOS), peak (POS), and end (EOS) of the growing season) exerted influential effects on the rates. However, no single climatic factor during any period appeared responsible for the variations in growth and senescence rates. In areas with growth and senescence rates that are determined by peak growth, temperature and precipitation during the growth period accelerated both rates through elevating peak growth. On the other hand, in areas with growth rate determined by SOS, rising temperature before SOS decelerated the growth rate by advancing SOS. In areas with senescence rate determined by EOS, both temperature and radiation during the senescence period contributed to changes in senescence rate by influencing EOS. In sum, a central focus should be placed on the linkages among climate, phenology, and growth and senescence rates for quantifying vegetation seasonality and associated ecosystem function under the changing climate

Ecological protection and restoration technology of large-scale open-pit coal mining area in the northern sand-proof belt

The northern sand-proof belt is the key zone for preventing and controlling desertification and sandification in China, and it is the core area of the “Three Norths” project, which is characterized by infertile soil, high wind speed, drought and water shortage, sparse vegetation, and low ecological resilience, and the contradiction between large-scale open-pit coal mining and ecological protection is prominent, which directly affects the regional ecological security and the national energy security, and there is an urgent need to carry out the research on the ecological protection and restoration of large-scale open-pit mining areas. The study of ecological protection and restoration of large-scale open-pit mining area is urgently needed. To address the problems of land destruction, dust and sand, falling water level, soil sanding, and vegetation degradation caused by large-scale open-pit coal mining in the northern sand belt, the study is based on the area of the northern sand belt and the size of the open-pit coal mine. Taking the large-scale surface coal mining area in Inner Mongolia, which has the largest area and proportion of surface coal production in the northern sand belt, as the research area, the research idea of “loss-reducing mining - three-dimensional water preservation - soil creation and soil revitalization - systematic restoration - integrated supervision and management” is revealed. Adhering to the research idea of “loss-reducing mining - three-dimensional water conservation - soil creation and living soil - system restoration - integrated supervision”, the research area reveals the transmission mechanism of geotechnical damage and ecological degradation of large-scale surface mines, the mechanism of ecological loss-reducing mining, the mechanism of ecological self-sustainability, and develops key technologies such as loss-reducing mining with ecological protection, ecological design, water resource protection and comprehensive utilization, erosion-resistant geomorphological remodeling, fine reconstruction of the living soil layer in the discharge site, soil improvement, and quality enhancement and capacity increase, and joint restoration of fungus-algae-grass, ecosystem degradation supervision and other key technologies, form an ecological protection and restoration technology system suitable for the open-pit coal mine area in the northern sand-proof belt, and build a large-scale integrated demonstration area for ecological protection and restoration of the open-pit mine area. We will create a low-cost, high-efficiency, sustainable and replicable ecological protection and restoration model for large-scale open-pit coal mining areas, provide support for scientific mining and ecological safety in large-scale open-pit coal mining areas in the northern sand-proof belt of China, and realize the goal of coordinating energy supply and ecological protection in coal development and the goal of “developing the gold and silver mountains and recreating the green mountains and green water”

Model-Based Identification of <i>Larix sibirica</i> Ledeb. Damage Caused by <i>Erannis jacobsoni</i> Djak. Based on UAV Multispectral Features and Machine Learning

While unmanned aerial vehicle (UAV) remote sensing technology has been successfully used in crop vegetation pest monitoring, a new approach to forest pest monitoring that can be replicated still needs to be explored. The aim of this study was to develop a model for identifying the degree of damage to forest trees caused by Erannis jacobsoni Djak. (EJD). By calculating UAV multispectral vegetation indices (VIs) and texture features (TF), the features sensitive to the degree of tree damage were extracted using the successive projections algorithm (SPA) and analysis of variance (ANOVA), and a one-dimensional convolutional neural network (1D-CNN), random forest (RF), and support vector machine (SVM) were used to construct damage degree recognition models. The overall accuracy (OA), Kappa, Macro-Recall (Rmacro), and Macro-F1 score (F1macro) of all models exceeded 0.8, and the best results were obtained for the 1D-CNN based on the vegetation index sensitive feature set (OA: 0.8950, Kappa: 0.8666, Rmacro: 0.8859, F1macro: 0.8839), while the SVM results based on both vegetation indices and texture features exhibited the poorest performance (OA: 0.8450, Kappa: 0.8082, Rmacro: 0.8415, F1macro: 0.8335). The results for the stand damage level identified by the models were generally consistent with the field survey results, but the results of SVMVIs+TF were poor. Overall, the 1D-CNN showed the best recognition performance, followed by the RF and SVM. Therefore, the results of this study can serve as an important and practical reference for the accurate and efficient identification of the damage level of forest trees attacked by EJD and for the scientific management of forest pests

Model-Based Identification of Larix sibirica Ledeb. Damage Caused by Erannis jacobsoni Djak. Based on UAV Multispectral Features and Machine Learning

While unmanned aerial vehicle (UAV) remote sensing technology has been successfully used in crop vegetation pest monitoring, a new approach to forest pest monitoring that can be replicated still needs to be explored. The aim of this study was to develop a model for identifying the degree of damage to forest trees caused by Erannis jacobsoni Djak. (EJD). By calculating UAV multispectral vegetation indices (VIs) and texture features (TF), the features sensitive to the degree of tree damage were extracted using the successive projections algorithm (SPA) and analysis of variance (ANOVA), and a one-dimensional convolutional neural network (1D-CNN), random forest (RF), and support vector machine (SVM) were used to construct damage degree recognition models. The overall accuracy (OA), Kappa, Macro-Recall (Rmacro), and Macro-F1 score (F1macro) of all models exceeded 0.8, and the best results were obtained for the 1D-CNN based on the vegetation index sensitive feature set (OA: 0.8950, Kappa: 0.8666, Rmacro: 0.8859, F1macro: 0.8839), while the SVM results based on both vegetation indices and texture features exhibited the poorest performance (OA: 0.8450, Kappa: 0.8082, Rmacro: 0.8415, F1macro: 0.8335). The results for the stand damage level identified by the models were generally consistent with the field survey results, but the results of SVMVIs+TF were poor. Overall, the 1D-CNN showed the best recognition performance, followed by the RF and SVM. Therefore, the results of this study can serve as an important and practical reference for the accurate and efficient identification of the damage level of forest trees attacked by EJD and for the scientific management of forest pests