Monitoring of deforestation events in the tropics using multidimensional features of Sentinel 1 radar data

Many countries and regions are currently developing new forest strategies to better address the challenges facing forest ecosystems. Timely and accurate monitoring of deforestation events is necessary to guide tropical forest management activities. Synthetic aperture radar (SAR) is less susceptible to weather conditions and plays an important role in high-frequency monitoring in cloudy regions. Currently, most SAR image-based deforestation identification uses manually supervised methods, which rely on high quality and sufficient samples. In this study, we aim to explore radar features that are sensitive to deforestation, focusing on developing a method (named 3DC) to automatically extract deforestation events using radar multidimensional features. First, we analyzed the effectiveness of radar backscatter intensity (BI), vegetation index (VI), and polarization feature (PF) in distinguishing deforestation areas from the background environment. Second, we selected the best-performing radar features to construct a multidimensional feature space model and used an unsupervised K-mean clustering method to identify deforestation areas. Finally, qualitative and quantitative methods were used to validate the performance of the proposed method. The results in Paraguay, Brazil, and Mexico showed that (1) the overall accuracy (OA) and F1 score (F1) of 3DC were 88.1–98.3% and 90.2–98.5%, respectively. (2) 3DC achieved similar accuracy to supervised methods without the need for samples. (3) 3DC matched well with Global Forest Change (GFC) maps and provided more detailed spatial information. Furthermore, we applied the 3DC to deforestation mapping in Paraguay and found that deforestation events occurred mainly in the second half of the year. To conclude, 3DC is a simple and efficient method for monitoring tropical deforestation events, which is expected to serve the restoration of forests after deforestation. This study is also valuable for the development and implementation of forest management policies in the tropics

The Complete Genome Sequence of ‘Candidatus Liberibacter solanacearum’, the Bacterium Associated with Potato Zebra Chip Disease

Zebra Chip (ZC) is an emerging plant disease that causes aboveground decline of potato shoots and generally results in unusable tubers. This disease has led to multi-million dollar losses for growers in the central and western United States over the past decade and impacts the livelihood of potato farmers in Mexico and New Zealand. ZC is associated with ‘Candidatus Liberibacter solanacearum’, a fastidious alpha-proteobacterium that is transmitted by a phloem-feeding psyllid vector, Bactericera cockerelli Sulc. Research on this disease has been hampered by a lack of robust culture methods and paucity of genome sequence information for ‘Ca. L. solanacearum’. Here we present the sequence of the 1.26 Mbp metagenome of ‘Ca. L. solanacearum’, based on DNA isolated from potato psyllids. The coding inventory of the ‘Ca. L. solanacearum’ genome was analyzed and compared to related Rhizobiaceae to better understand ‘Ca. L. solanacearum’ physiology and identify potential targets to develop improved treatment strategies. This analysis revealed a number of unique transporters and pathways, all potentially contributing to ZC pathogenesis. Some of these factors may have been acquired through horizontal gene transfer. Taxonomically, ‘Ca. L. solanacearum’ is related to ‘Ca. L. asiaticus’, a suspected causative agent of citrus huanglongbing, yet many genome rearrangements and several gene gains/losses are evident when comparing these two Liberibacter. species. Relative to ‘Ca. L. asiaticus’, ‘Ca. L. solanacearum’ probably has reduced capacity for nucleic acid modification, increased amino acid and vitamin biosynthesis functionalities, and gained a high-affinity iron transport system characteristic of several pathogenic microbes

A novel spectral index for mapping blue colour-coated steel roofs (BCCSRs) in urban areas using Sentinel-2 data

Blue colour-coated steel roofs (BCCSRs) offer a lightweight and economical option to concrete and other cladding in buildings, but they are also controversial for altering the surface energy budget and water cycle. Obtaining spatial information about BCCSRs is crucial for exploring the environmental impacts of man-made landscapes. However, existing methods are not always effective due to the variety of BCCSR types and background conditions. To overcome these limitations, we proposed a new index (called BCCSI) based on Sentinel-2 multispectral images to map the commonly used BCCSRs. Five typical study areas were chosen worldwide to develop and validate the BCCSI. Based on spectral analysis, we constructed the BCCSI using the blue, red, green, and shortwave infrared 2 (SWIR2) bands to highlight the BCCSR while suppressing the background condition. Compared with five existing indices, the BCCSI was effective in the visual evaluation, separability analysis and BCCSR mapping. Moreover, the BCCSI achieved similar accuracy to the supervised classifier while avoiding the time-consuming and laborious effort of sample collection. Furthermore, the BCCSI showed its applicability in medium-resolution satellite data, such as Landsat-8 imagery. Thus, the proposed BCCSI provides a viable scheme for global BCCSR mapping and analysis

Dynamics and Drivers of Water Clarity Derived from Landsat and In-Situ Measurement Data in Hulun Lake from 2010 to 2020

Water clarity (Secchi disk depth, SDD), as a proxy of water transparency, provides important information on the light availability to the lake ecosystem, making it one of the key indicators for evaluating the water ecological environment, particularly in nutrient-rich inland lakes. Hulun Lake, the fifth largest lake in China, has faced severe water quality challenges in the past few decades, e.g., high levels of phosphorus and nitrogen, leading to lake eutrophication. However, under such a serious context, the temporal and spatial dynamics of SDD in Hulun Lake are still unclear. In this paper, we obtained the best model input parameters by using stepwise linear regression models to test field measurements against remote sensing band information, and then developed the SDD satellite algorithm suitable for Hulun Lake by comparing six models (i.e., linear, quadratic, cubic, exponential, power, and logarithmic). The results showed that (1) B3/(B1 + B4) [red/(blue-near-infrared)] was the most sensitive parameter for transparency (R = 0.84) and the exponential model was the most suitable transparency inversion model for Hulun Lake (RMSE = 0.055 m, MAE = 0.003 m), (2) The annual mean SDD of Hulun Lake was higher in summer than in autumn, the summer SDD decreased from 2010 (0.23 m) to 2020 (0.17 m), and the autumn SDD increased from 2010 (0.06 m) to 2020 (0.16 m). The SDD in the littoral zones of Hulun Lake was less than that in the central part; (3) meteorological conditions (i.e., precipitation and wind speed) were highly correlated with the variation of SDD. Cropland expansion was the possible reason for the low SDD at the entrance of Hulun Lake flow. The findings of this study have important implications for the development and implementation of ecological protection and restoration strategies in the Hulun Lake basin

Modular Robotic Limbs for Astronaut Activities Assistance

In order to meet the assist requirements of extravehicular activity (EVA) for astronauts, such as moving outside the international space station (ISS) or performing on-orbit tasks by a single astronaut, this paper proposes an astronaut robotic limbs system (AstroLimbs) for extravehicular activities assistance. This system has two robotic limbs that can be fixed on the backpack of the astronaut. Each limb is composed of several basic module units with identical structure and function, which makes it modularized and reconfigurable. The robotic limbs can work as extra arms of the astronaut to assist them outside the space station cabin. In this paper, the robotic limbs are designed and developed. The reinforcement learning method is introduced to achieve autonomous motion planning capacity for the robot, which makes the robot intelligent enough to assist the astronaut in unstructured environment. In the meantime, the movement of the robot is also planned to make it move smoothly. The structure scene of the ISS for extravehicular activities is modeled in a simulation environment, which verified the effectiveness of the proposed method

Air Quality Improvement in China: Evidence from PM_2.5 Concentrations in Five Urban Agglomerations, 2000–2021

Air pollution endangers human health and sustainable socio-economic development, especially in urban agglomeration (UA). The Chinese government has implemented a series of policies and standards to improve air quality. However, few studies have compared variations in PM2.5 concentrations across multiple UAs, and current research often lacks analysis relative to the clean air policies implemented by the government. In this study, we used econometric and geostatistical methods to assess the distribution and spatial evolution of PM2.5 concentrations in five UAs (the Beijing–Tianjin–Hebei UA (BTHUA), middle reaches of the Yangtze River UA (MYRUA), Chengdu–Chongqing UA (CCUA), Harbin Changchun UA (HCUA), and Beibu Gulf UA (BGUA)) in China from 2000 to 2021 to explore the effectiveness of the clean air policies implemented by the government on air pollution control, to analyze the ambient air quality of UAs, and to make recommendations for public outdoor activities. The results indicated that the clean air policy implemented by the Chinese government in 2013 achieved significant treatment results. PM2.5 concentrations were plotted as an inverted U-shaped curve based on time, which showed an upward trend before 2013 and a downward trend after 2013. PM2.5 concentrations showed a similar seasonal pattern, with a single-valley “V” shape. PM2.5 concentration was the highest in winter and the lowest in summer. The PM2.5 concentration of HCUA and BGUA was lower than that of CCUA, MYRUA, and BTHUA. The increase in PM2.5 concentration mainly occurred in autumn and winter, while the decrease mainly occurred in spring. In 2021, the PM2.5 air quality compliance rates (3) in BTHUA, MYRUA, CCUA, HCUA, and BGUA were 44.57%, 80.00%, 82.04%, 99.74%, and 100%, respectively. However, in 2021, 19.19% of the five UAs still had an ambient air quality of Grade II (i.e., 50 PM2.5 < 100). People with abnormally sensitive breathing in these areas should reduce their outdoor activities. These results contribute to epidemiological studies on human health and disease prevention and suggest reasonable pathways by which governments can improve air quality through sustainable urban planning

Accurate vegetation destruction detection using remote sensing imagery based on the three-band difference vegetation index (TBDVI) and dual-temporal detection method

Satellite remote sensing, as an important tool for Earth observation, has been widely used to monitor various vegetation destruction events (VDEs), such as logging, wildfires and insect infestations. However, due to the spectral diversity of VDE and the complexity of background environments (BE), achieving accurate VDE detection remains a challenge. To overcome this limitation, this study developed a novel index, called the three-band difference vegetation index (TBDVI), which fully considered the spectral characteristics of both various BEs and multiple VDEs, for the accurate detection of vegetation destruction in complex scenarios. Three experiments were chosen to prove the performance of TBDVI, including (1) various possible vegetation changes; (2) various possible background changes; and (3) multiple real vegetation destruction events. The results showed that TBDVI was suitable for various vegetation change scenarios and complex background conditions, with F1 scores of 0.906–0.979. Moreover, TBDVI accurately identified the extent of VDE caused by logging, insect infestation, landslides, wildfires, and floods, with F1 scores of 0.922–0.965. Compared with existing spectral indices (VIs) (i.e., normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and normalized burn ratio (NBR)), TBDVI has obvious advantages in reducing the impact of the background environment. In addition, TBDVI exhibits cross-sensor applicability and has potential for large-scale and high-frequency vegetation monitoring. In conclusion, TBDVI is an effective and robust spectral metric that is important for the conservation and management of vegetation resources

Correlates of extinction risk in Chinese endemic birds

Abstract Background China has a relative high degree of endemism of birds due to its large area, diversified topography, and varied climates and habitats. Among the 77 Chinese endemic birds, 29 species are classified as threatened according to the officially released China Biodiversity Red List in 2015. Chinese endemic birds should be the focus of conservation because their local extinction in China means complete global extinction. However, to date, no study has explicitly examined the patterns and processes of extinction and threat in Chinese endemic birds. Methods We obtained eleven biological traits and four extrinsic factors that are commonly hypothesized to influence extinction risk. After phylogenetic correction, these factors were used separately and in combination to assess their associations with extinction risk. Results We found that 37.7% of Chinese endemic birds were listed as threatened (Vulnerable, Endangered and Critically Endangered). Small range size, high hunting vulnerability, and high human population density were important predictors of high extinction risk in Chinese endemic birds. Conclusions Our study is the first to systematically investigate the patterns and processes of extinction risk in Chinese endemic birds. We suggest that endemic species with small range size and living in area with high human densities require conservation priorities. Conservation efforts should also focus on the reduction of human threats, such as human hunting and habitat degradation, for the effective preservation of Chinese endemic birds

Simulation of Land Use and Carbon Storage Evolution in Multi-Scenario: A Case Study in Beijing-Tianjin-Hebei Urban Agglomeration, China

In considering regional sustainable development, optimizing the distribution of land use and land cover (LULC) and improving terrestrial ecosystem carbon storage (CS) have emerged as major concerns. In this study, considering the synergistic effect between LULC and CS, a coupling model (named MPI) that integrates Multi-objective Optimization (MOP) model, the Patch-generating Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, was proposed to simulate the 2030 CS and explore its spatial-temporal characteristics in a Beijing-Tianjin-Hebei urban agglomeration (BTH). The MPI model, which combines the advantages of the above three models, can optimize the LULC structure, simulate the LULC distribution, and efficiently extract CS variation. The results indicated that: (1) LULC changes in BTH were mostly represented in transfers between cropland, forest, and grassland; (2) three different scenarios were simulated using the MPI model, named BAU (Business as usual), EDP (Ecological development priority), and EEB (Ecological and economic balanced). The simulation results of the three scenarios are in line with their respective goals, and the results are quite different; (3) cropland, water, and bare land, will be reduced, and the constant shrinking of water is a pressing issue that must be addressed; and (4) the EEB scenario balanced ecological services and economic rewards, increased the ecosystem carbon sink function, and is an efficient way to investigate “carbon neutrality”. The application of the MPI model is of reference value for exploring the optimal configuration of land resources

Simulation of Land Use and Carbon Storage Evolution in Multi-Scenario: A Case Study in Beijing-Tianjin-Hebei Urban Agglomeration, China

In considering regional sustainable development, optimizing the distribution of land use and land cover (LULC) and improving terrestrial ecosystem carbon storage (CS) have emerged as major concerns. In this study, considering the synergistic effect between LULC and CS, a coupling model (named MPI) that integrates Multi-objective Optimization (MOP) model, the Patch-generating Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, was proposed to simulate the 2030 CS and explore its spatial-temporal characteristics in a Beijing-Tianjin-Hebei urban agglomeration (BTH). The MPI model, which combines the advantages of the above three models, can optimize the LULC structure, simulate the LULC distribution, and efficiently extract CS variation. The results indicated that: (1) LULC changes in BTH were mostly represented in transfers between cropland, forest, and grassland; (2) three different scenarios were simulated using the MPI model, named BAU (Business as usual), EDP (Ecological development priority), and EEB (Ecological and economic balanced). The simulation results of the three scenarios are in line with their respective goals, and the results are quite different; (3) cropland, water, and bare land, will be reduced, and the constant shrinking of water is a pressing issue that must be addressed; and (4) the EEB scenario balanced ecological services and economic rewards, increased the ecosystem carbon sink function, and is an efficient way to investigate &ldquo;carbon neutrality&rdquo;. The application of the MPI model is of reference value for exploring the optimal configuration of land resources