Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage.

Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. The satellite-UAV-ground integrated inversion method is an innovative solution. In this study, the core region of the Yellow River Delta (YRD) is used as a study area. Ground measurements data, UAV multispectral and Sentinel-2A multispectral imagery are used as data sources. First, representative plots in the Hekou District were selected as the core test area, and 140 ground sampling points were selected. Based on the measured SPAD values and UAV multispectral images, UAV-based SPAD inversion models were constructed, and the most accurate model was selected. Second, by comparing satellite and UAV imagery, a reflectance correction for satellite imagery was performed. Finally, based on the UAV-based inversion model and satellite imagery after reflectance correction, the inversion results for SPAD values in multi-scale were obtained. The results showed that green, red, red-edge and near-infrared bands were significantly correlated with SPAD values. The modeling precisions of the best inversion model are R² = 0.926, Root Mean Squared Error (RMSE) = 0.63 and Mean Absolute Error (MAE) = 0.92, and the verification precisions are R² = 0.934, RMSE = 0.78 and MAE = 0.87. The Sentinel-2A imagery after the reflectance correction has a pronounced inversion effect; the SPAD values in the study area were concentrated between 40 and 60, showing an increasing trend from the eastern coast to the southwest and west, with obvious spatial differences. This study synthesizes the advantages of satellite, UAV and ground methods, and the proposed satellite-UAV-ground integrated inversion method has important implications for real-time, rapid and precision SPAD values collected on multiple scales.

Assessing and mapping distribution, area, and density of riparian forests in southern Iran using Sentinel-2A, Google earth, and field data.

Riparian forests in Iran are valuable ecosystems which have many ecological values. Because of destruction of these forests in recent years, providing spatio-temporal information from area and distribution of these ecosystems has been receiving much attention. This study was performed for mapping distribution, area and density of riparian forests in southern Iran using Sentinel-2A, Google Earth, and field data. First Sentinel-2A satellite image of the study area was provided. The field work was performed to take the training areas and to assess the forest density of riparian forests in Khuzestan province. In the first part of this study, after selecting training areas as pixel-based samples on the Sentinel-2A satellite image, supervised classification of image was performed using support vector machine (SVM) algorithm to classify the distribution of riparian forests. After classification of Sentinel-2A satellite image, the boundary of riparian forests map was checked and corrected on Google Earth images. In the second part of this study, field data, Normalized Difference Vegetation Index (NDVI), and regression model were used to assess the density of riparian forests. Finally, the accuracy of the final riparian forest map (showing both distribution and density of riparian forests) was assessed using Google Earth images. Results showed that the final riparian forest map (showing both distribution and density of riparian forests) with overall accuracy 89% and kappa index 0.81 had a good accuracy for classifying the distribution and density of riparian forests in Khuzestan province. These results demonstrate the accuracy of SVM algorithm for classifying the distribution of riparian forests and also capability of NDVI for classifying the density of riparian forests in this study. Results also showed that regression model (R2 = 0.97) is reliable for estimating riparian forest density. The results demonstrated that there are 68447.18 ha of riparian forest around the main rivers in Khuzestan province, mainly distributed in the northwest and southeast of the province. From this area, 54694.15 ha have been covered by dense forests and 13753.03 ha by sparse forests. Results of this research have created the useful data of area, distribution and density of riparian forests in 10-m spatial resolution which is necessary for conservation and management of these forests in southern Iran. It is suggested that mapping area, distribution and density of these forests would be performed using SVM algorithm and NDVI in the certain temporal periods for protective management of these ecosystems in time series.