RESUMEN
Rapid acquisition of the spatial distribution of soil nutrients holds great implications for farmland soil productivity safety, food security and agricultural management. To this end, we collected 1297 soil samples and measured the content of soil total nitrogen (TN), soil available phosphorus (AP) and soil available potassium (AK) in Zengcheng, north of the Pearl River Delta, China. Hyperspectral remote sensing images (115 bands) of the Chinese Environmental 1A satellite were used as auxiliary variables and dimensionality reduction was performed using Pearson correlation analysis and principal component analysis. The TN, AP and AK of soil were predicted in the study area based on auxiliary variables after dimensionality reduction, along with stepwise linear regression (SLR), support vector machine (SVM), random forest (RF) and back-propagation neural network (BPNN) models; 324 independent points were used to verify the predictive performance. The BPNN model, which demonstrated the best predictive accuracy among all methods, combined ordinary kriging (OK) with mapping the spatial variations of soil nutrients. Results show that the BPNN model with double hidden layers had better predictive accuracy for soil TN (root mean square error (RMSE) = 0.409 mg kg-1, R² = 44.24%), soil AP (RMSE = 40.808 mg kg-1, R² = 42.91%) and soil AK (RMSE = 67.464 mg kg-1, R² = 48.53%) compared with the SLR, SVM and RF models. The back propagation neural network-ordinary kriging (BPNNOK) model showed the best predictive results of soil TN (RMSE = 0.292 mg kg-1, R² = 68.51%), soil AP (RMSE = 29.62 mg kg-1, R² = 69.30%) and soil AK (RMSE = 49.67 mg kg-1 and R² = 70.55%), indicating the best fitting ability between hyperspectral remote sensing bands and soil nutrients. According to the spatial mapping results of the BPNNOK model, concentrations of soil TN (north-central), soil AP (central and southwest) and soil AK (central and southeast) were respectively higher in the study area. The most important bands (464â»517 nm) for soil TN (b10, b14, b20 and b21), soil AP (b3, b19 and b22) and soil AK (b4, b11, b12 and b25) exhibited the best response and sensitivity according to the SLR, SVM, RF and BPNN models. It was concluded that the application of hyperspectral images (visible-near-infrared data) with BPNNOK model was found to be an efficient method for mapping and monitoring soil nutrients at the regional scale.
RESUMEN
Marine fish shows high heterogeneity in spatial aggregation. We analyzed the inter-deca-dal variations of stock density for Trichiurus japonicus in East China Sea (ECS) using geo-statistical approaches such as spatial autocorrelation and hotspot analysis, based on the data of T. japonicus from both bottom trawl fishery and research surveys in the open waters of ECS during 1971 to 2011, combined with the sea surface temperature (SST) and surface salinity data in the PN section in August. The global spatial autocorrelation statistics showed that Moran's I firstly decreased and then went up, indicating that the spatial aggregation patterns of T. japonicus was weakened in the beginning and then increased during 1971 to 2011. The surface salinity in the PN section displayed the opposite trend during the same period. The local spatial autocorrelation statistics showed that the population firstly moved to the southern ECS and then to the northern ECS except in 1971 in which the population concentrated in the middle of ECS because of the restriction of offshore fishing ground. The movement of hotspot areas of T. japonicus adaptively varied with the first EOF mode of SST in summer (sumEOF1), which indicated that the hotspot areas first moved southeastward with decreasing sumEOF1, and moved northeastward with increasing sumEOF1, but all of the hotspot areas were close to the northward branch of the Kuroshio Current.