No abstract

Monitoring salinity information of salinized soil efficiently and precisely using the unmanned aerial vehicle (UAV) is critical for the rational use and sustainable development of arable land resources. The sensitive parameter and a precise retrieval method of soil salinity, however, remain unknown. This study strived to explore the sensitive parameter and construct an optimal method for retrieving soil salinity. The UAV-borne multispectral image in China’s Yellow River Delta was acquired to extract band reflectance, compute vegetation indexes and soil salinity indexes. Soil samples collected from 120 different study sites were used for laboratory salt content measurements. Grey correlation analysis and Pearson correlation coefficient methods were employed to screen sensitive band reflectance and indexes. A new soil salinity retrieval index (SSRI) was then proposed based on the screened sensitive reflectance. The Partial Least Squares Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were employed to construct retrieval models based on the sensitive indexes. The results found that green, red, and near-infrared (NIR) bands were sensitive to soil salinity, which can be used to build SSRI. The SSRI-based RF method was the optimal method for accurately retrieving the soil salinity. Its modeling determination coefficient (R2) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R2, RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211.

Vegetation greenery is essential for the sensory and psychological wellbeing of residents in residential communities. To enhance the quality of regulations and policies to improve people’s living environments, it is crucial to effectively identify and monitor vegetation greenery from the perspective of the residents using effective images and methods. In this study, Baidu street view (BSV) images and a Normalized Vegetation Greenery Index (NVGI) based method were examined to distinguish vegetation greenery in residential communities of Beijing, China. The magnitude of the vegetation was quantified and graded, and spatial analysis techniques were employed to investigate the spatial characteristics of vegetation greenery. The results demonstrated that (1) the identified vegetation greenery using the proposed NVGI-based method was closely correlated with those of the reference classification (r = 0.993, p = 0.000), surpassing the comparison results from the SVM method, a conventional remote sensing classification means; (2) the vegetation greenery was distributed unevenly in residential communities and can be categorized into four grades, 63.79% of the sampling sites were found with relatively low (Grade II) and moderate (Grade III) vegetation greenery distribution, most of the districts in the study area contained zero-value green view index sites; and (3) there was significant spatial heterogeneity observed in the study area, with low-value clustering (cold spots) predominantly located in the central region and high-value clustering (hot spots) primarily concentrated in the peripheral zone. The findings of this study can be applied in other cities and countries that have street view images available to investigate greenery patterns within residential areas, which can help improve the planning and managing efforts in urban communities.