Characteristics and Spatial Variability of Saline-Alkaline Soil Degradation in the Typical Yellow River Delta Area of Kenli County, China

As an important area of reserve land resources, the Yellow River Delta is faced with the problem of soil salinization. Grasping the characteristics of soil salinity as well as its spatial variation patterns is an important foundation of prevention, control and utilization of saline soil. This study selected Kenli County of the Yellow River Delta, obtained soil salinity data through field survey and lab experiment, and used statistical, GIS interpolation and buffer analysis methods to analyze the characteristics of soil salinity and its spatial variation patterns. Our results showed that the general soil salinity in the study area was mainly moderate and there was a significant positive correlation between different soil layers of 0 - 15 cm, 15 - 30 cm and 30 - 45 cm and soil salinity increased with the increase of soil depth. The areas with high soil salinity in each soil layer mainly distributed in the east near the Bo Sea in the county, while the areas with lower soil salinity mainly distributed in the southwest, centre and the two sides of the Yellow River in the northeast. Soil salinity showed a trend of decrease with the increase in distance to the Bo Sea, while stretching from the Yellow River, it showed increase tendency with the increase in distance to the Yellow River. The order from high soil salinity to low of different vegetation types was naked land → suaeda glauca → tamarix → vervain → reed → couch grass → paddy → cotton → winter wheat → maize;the order for different geomorphic types was depression → slightly sloping ground → slow hillock → high flood land. This study preliminary delineated the characteristics of soil salinity as well as its spatial variation patterns in the study area, and provided scientific basis for soil resource sustainable utilization in the Yellow River Delta.

Estimating Total Nitrogen Content in Brown Soil of Orchard Based on Hyperspectrum

The best hyperspectral estimation model of soil total nitrogen (TN) was established, which provided the basis for rapid and accurate estimation of soil total nitrogen content, scientific and rational fertilization and soil informatization management. A total of 92 brown soil samples were collected from the orchard of Qixia County, Yantai City, Shandong Province. After drying and grinding, the hyperspectrum of the soil was measured in the laboratory using ASD FieldSpec3. The TN contents of brown soil were measured by Kjeldahl method. The sensitive wavelengths were selected by multiple linear stepwise regression method. The hyperspectral estimation model of TN was established by Random Forest (RF) and Support Vector Machines (SVM). The models were validated by independent samples. The best estimation model was obtained. The sensitive wavelengths were 956 nm, 995 nm, 1020 nm, 1410 nm, 1659 nm and 2020 nm. The coefficients of determination (R2) of the two estimation models were 0.8011 and 0.8283, the root mean square errors (RMSE) were 0.022 and 0.025, and relative errors (RE) were 0.1422 and 0.1639, respectively. Random Forest model and Support Vector Machines model are feasible in estimating TN contents, but the Support Vector Machines model is better.

Applications of Hyperspectral Remote Sensing in Ground Object Identification and Classification

Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and classification of ground objects at home and abroad. The research results of identification and classification of forest tree species, grassland and urban land features were summarized. Then the researches of classification methods were summarized. Finally the prospects of hyperspectral remote sensing in ground object identification and classification were prospected.

Information Extraction Method of Soil Salinity in Typical Areas of the Yellow River Delta Based on Landsat Imagery

In order to get RS method to extract soil salinity of the Yellow River Delta, we set Kenli County as typical Yellow River Delta to be research area and get data of soil salinity through field investigation. By using RS image of Landsat-8 of March 14, 2014 and analyzing information features of each band and surface spectral features of research areas, we select out sensitive bands and build Soil Salinity Information Extraction (SSIE) model and vegetation index NDVI model for comparison. And then, we accordingly classify grades of soil salinity and get soil salinity information by decision tree approach based on expert knowledge. The results show that overall accuracy of SSIE model is 93.04% and coefficient of Kappa is 0.7869, while overall accuracy of NDVI model is 83.67% and coefficient of Kappa is 0.7017 respectively. By comparing with measured proportions of each class, we see that results from SSIE model is more accurate, which indicates significant advantage for soil salinity information extraction. This research provides scientific basis to get and monitoring soil salinity of the Yellow River Delta region quickly and accurately.