Human Expansion-Induced Biodiversity Crisis over Asia from 2000 to 2020.

Asia stands out as a priority for urgent biodiversity conservation due to its large protected areas (PAs) and threatened species. Since the 21st century, both the highlands and lowlands of Asia have been experiencing the dramatic human expansion. However, the threat degree of human expansion to biodiversity is poorly understood. Here, the threat degree of human expansion to biodiversity over 2000 to 2020 in Asia at the continental (Asia), national (48 Asian countries), and hotspot (6,502 Asian terrestrial PAs established before 2000) scales is investigated by integrating multiple large-scale data. The results show that human expansion poses widespread threat to biodiversity in Asia, especially in Southeast Asia, with Malaysia, Cambodia, and Vietnam having the largest threat degrees (∼1.5 to 1.7 times of the Asian average level). Human expansion in highlands induces higher threats to biodiversity than that in lowlands in one-third Asian countries (most Southeast Asian countries). The regions with threats to biodiversity are present in ∼75% terrestrial PAs (including 4,866 PAs in 26 countries), and human expansion in PAs triggers higher threat degrees to biodiversity than that in non-PAs. Our findings provide novel insight for the Sustainable Development Goal 15 (SDG-15 Life on Land) and suggest that human expansion in Southeast Asian countries and PAs might hinder the realization of SDG-15. To reduce the threat degree, Asian developing countries should accelerate economic transformation, and the developed countries in the world should reduce the demands for commodity trade in Southeast Asian countries (i.e., trade leading to the loss of wildlife habitats) to alleviate human expansion, especially in PAs and highlands.

Human expansion into Asian highlands in the 21st Century and its effects.

Most intensive human activities occur in lowlands. However, sporadic reports indicate that human activities are expanding in some Asian highlands. Here we investigate the expansions of human activities in highlands and their effects over Asia from 2000 to 2020 by combining earth observation data and socioeconomic data. We find that ∼23% of human activity expansions occur in Asian highlands and ∼76% of these expansions in highlands comes from ecological lands, reaching 95% in Southeast Asia. The expansions of human activities in highlands intensify habitat fragmentation and result in large ecological costs in low and lower-middle income countries, and they also support Asian developments. We estimate that cultivated land net growth in the Asian highlands contributed approximately 54% in preventing the net loss of the total cultivated land. Moreover, the growth of highland artificial surfaces may provide living and working spaces for ∼40 million people. Our findings suggest that highland developments hold dual effects and provide new insight for regional sustainable developments.

Digital mapping of zinc in urban topsoil using multisource geospatial data and random forest.

This study aimed to map the spatial patterns of Zn in urban topsoil by using multisource geospatial data and machine learning method. Geological map, digital elevation models, and Landsat images were used to extract data related to geology, relief, and land use types and a vegetation index. Urban functional types were derived from the fusion of Systeme Probatoire d'Observation de la Terre 5 images, points of interest, and real-time Tencent user data. A geodetector was adopted to select key environmental covariates. Random forest (RF) and geographically weighted regression (GWR) were employed to model and map Zn concentrations in urban topsoil. The results showed that urban functional type, geology, NDVI, elevation, slope, and aspect were key environmental covariates. Compared with land use types, urban functional types could better reflect the spatial variation in Zn. The RF and GWR models were established using the key environmental covariates, with leave-one-out cross-validated R values of 0.68 and 0.58 and root mean square errors of 0.51 and 0.57, respectively. The results indicated that digital mapping of Zn in urban topsoil by using multisource geospatial data and RF was feasible. RF might be more suitable to fit the stochastic characteristics of Zn in urban topsoils than GWR, which considers deterministic trends in modeling.

Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches.

Due to rapid urbanization in China, lead (Pb) continues to accumulate in urban topsoil, resulting in soil degradation and increased public exposure. Mapping Pb concentrations in urban topsoil is therefore vital for the evaluation and control of this exposure risk. This study developed spatial models to map Pb concentrations in urban topsoil using proximal and remote sensing data. Proximal sensing reflectance spectra (350-2500 nm) of soils were pre-processed and used to calculate the principal components as landscape factors to represent the soil properties. Other landscape factors, including vegetation and land-use factors, were extracted from time-sequential Landsat images. Two hybrid statistical approaches, regression kriging (RK) and geographically weighted regression (GWR), were adopted to establish prediction models using the landscape factors. The results indicated that the use of landscape factors derived from combined remote and proximal sensing data improved the prediction of Pb concentrations compared with useing these data individually. GWR obtained better results than RK for predicting soil Pb concentration. Thus, joint proximal and remote sensing provides timely, easily accessible, and suitable data for extracting landscape factors.

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils.

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar's test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies.

Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice.

This study systematically analyzed the performance of multivariate hyperspectral vegetation indices of rice (Oryza sativa L.) in estimating the arsenic content in agricultural soils. Field canopy reflectance spectra was obtained in the jointing-booting growth stage of rice. Newly developed and published multivariate vegetation indices were initially calculated to estimate soil arsenic content. The well-performing vegetation indices were then selected using successive projections algorithm (SPA), and the SPA selected vegetation indices were adopted to calibrate a multiple linear regression model for estimating soil arsenic content. Results showed that a three-band vegetation index (R716-R568)/(R552-R568) performed best in the newly developed vegetation indices in estimating soil arsenic content. The photochemical reflectance index (PRI) and red edge position (REP) performed well in the published vegetation indices. Moreover, the linear combination of two vegetation indices ((R716-R568)/(R552-R568) and REP) selected using SPA improved the estimation of soil arsenic content. These results indicated that the newly developed three-band vegetation index (R716-R568)/(R552-R568) might be recommended as an indicator for estimating soil arsenic content in the study area. PRI and REP could be used as universal vegetation indices for monitoring soil arsenic contamination.

Soil organic carbon content estimation with laboratory-based visible-near-infrared reflectance spectroscopy: feature selection.

This study, with Yixing (Jiangsu Province, China) and Honghu (Hubei Province, China) as study areas, aimed to compare the successive projection algorithm (SPA) and the genetic algorithm (GA) in spectral feature selection for estimating soil organic carbon (SOC) contents with visible-near-infrared (Vis-NIR) reflectance spectroscopy and further to assess whether the spectral features selected from one site could be applied to another site. The SOC content and Vis-NIR reflectance spectra of soil samples were measured in the laboratory. Savitzky-Golay smoothing and log10(1/R) (R is reflectance) were used for spectral preprocessing. The reflectance spectra were resampled using different spacing intervals ranging from 2 to 10 nm. Then, SPA and GA were conducted for selecting the spectral features of SOC. Partial least square regression (PLSR) with full-spectrum PLSR and the spectral features selected by SPA (SPA-PLSR) and GA (GA-PLSR) were calibrated and validated using independent datasets, respectively. Moreover, the spectral features selected from one study area were applied to another area. Study results showed that, for the two study areas, the SPA-PLSR and GA-PLSR improved estimation accuracies and reduced spectral variables compared with the full spectrum PLSR in estimating SOC contents; GA-PLSR obtained better estimation results than SPA-PLSR, whereas SPA was simpler than GA, and the spectral features selected from Yixing could be well applied to Honghu, but not the reverse. These results indicated that the SPA and GA could reduce the spectral variables and improve the performance of PLSR model and that GA performed better than SPA in estimating SOC contents. However, SPA is simpler and time-saving compared with GA in selecting the spectral features of SOC. The spectral features selected from one dataset could be applied to a target dataset when the dataset contains sufficient information adequately describing the variability of samples of the target dataset.

Monitoring arsenic contamination in agricultural soils with reflectance spectroscopy of rice plants.

The objective of this study was to explore the feasibility and to investigate the mechanism for rapidly monitoring arsenic (As) contamination in agricultural soils with the reflectance spectra of rice plants. Several data pretreatment methods were applied to improve the prediction accuracy. The prediction of soil As contents was achieved by partial least-squares regression (PLSR) using laboratory and field spectra of rice plants, as well as linear regression employing normalized difference spectral index (NDSI) calculated from fild spectra. For laboratory spectra, the optimal PLSR model for predicting soil As contents was achieved using Savitzky-Golay smoothing (SG), first derivative and mean center (MC) (root-mean-square error of prediction (RMSEP)=14.7 mg kg(-1); r=0.64; residual predictive deviation (RPD)=1.31). For field spectra, the optimal PLSR model was also achieved using SG, first derivative and MC (RMSEP=13.7 mg kg(-1); r=0.71; RPD=1.43). In addition, the NDSI with 812 and 782 nm obtained a prediction accuracy with r=0.68, RMSEP=13.7 mg kg(-1), and RPD=1.36. These results indicated that it was feasible to monitor the As contamination in agricultural soils using the reflectance spectra of rice plants. The prediction mechanism might be the relationship between the As contents in soils and the chlorophyll-a/-b contents and cell structure in leaves or canopies of rice plants.