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The grid inventory-based approach is helpful for diagnosing vegetation properties of a given region, and provides support to explore the methods of vegetation classification. In this study, Dajinshan Island in Shanghai City was selected to conduct vegetation classification and vegetation mapping according to the methods of Tabulation and Two-Way Indicator Species Analysis. We divided this island into 140 regular grids（40 m × 40 m） based on an unmanned aerial vehicle image, and then surveyed community composition by using the Braun-Blanquet method and quadrat-based census across the grids. Under the newly revised three-level vegetation classification system, the first level of classification（i.e., vegetation type） included deciduous broad-leaved forest, evergreen and deciduous broad-leaved mixed forest, evergreen broad-leaved forest, deciduous broad-leaved shrubland, evergreen and deciduous broad-leaved mixed shrubland, evergreen broad-leaved shrubland, and grassland. According to the dominant species and floristic characteristics, the second level of classification included 15 types of associations or communities. The third level included 22 types of communities based on physiognomy and community age. These results indicate that Dajinshan Island is not only a species-richest region in Shanghai, but also possesses the most typical and diverse natural or semi-nature forest communities across islands in East China. In terms of vegetation status, zonal forest communities develop to the middle and late successional stages, but a few secondary vegetation still stop in the early successional stage, which encounter to the ecological problems such as development stagnation caused by monkey disturbance. With respect to the vegetation classification methods, the grid inventory-based technique can fully reveal the phenomena of intermittent and transitional communities in the continuity of flora. [ABSTRACT FROM AUTHOR]

Extracting planting information of rice as early as possible can be great significance in provincial agricultural production for the food security. MODIS data has been demonstrated to be superior in extracting planting information of rice at large scale due to short observation period and wide swath and easy image acquisition. The method of MODIS index during early growth period of rice combined with threshold value was usually used in the conventional provincial decision-making service to ensure the timeliness of service and convenient operation. However, the method is largely influenced by human and phenology difference of rice in various regions leading to highly subjective and poor stability. In addition, the mixed pixels were likely causing misestimation of the MODIS product. Random forest algorithm can make up the deficiency of threshold method due to the characteristics of less manual intervention and difficult over-fitting. In the study, an extraction method of rice planting information was proposed using MODIS index and random forest during early growth period of rice. Firstly, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) were selected to extract planting information based on the variation characteristics of MODIS index from seeding to jointing stage in Jiangxi Province, and then feature data sets were constructed to model. The models were built to extract planting area of early rice and to inverse planting abundance using random forest algorithm. Finally, the accuracy of the planting and the abundance maps of early rice were validated by the verification samples from the measured points, validation sample region obtained by the Sentinel-1A image and the statistical data from Jiangxi Provincial Bureau of Statistics. The results showed that using MODIS index during early growth period of rice and random forest was an effective way to extract plant information. The classification accuracy of early rice planting area extraction model was 93.18% with the Kappa coefficient of 0.915, and the mapping accuracy and user accuracy were 92.04% and 91.23%, respectively when matching the verification samples. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), decision coefficient of abundance inversion model were 0.07, 0.104 and 0.855, respectively, and the better performance of the abundance inversion model was achieved in high abundance planting areas. The spatial distribution characteristics of early rice planting area and abundance were consistent with validation sample region. Compared with the statistical data, the model accuracy of early rice area was 92.33%. This method can ensure the timeliness of the service and solve the problem that the extraction of rice planting area in the conventional provincial decision-making service is greatly affected by the problems of rationality of threshold selection and mixed pixel, also have no complex operation. The finding can provide a reference to extract planting area of early rice during early growth period. [ABSTRACT FROM AUTHOR]

Rapid and accurate extraction of cropping patterns is of great significance for regional resource capacity evaluation, green and sustainable agricultural development and national food security. However, there are few spatial datasets with the precision, breadth of coverage, or sufficient information as required in the mapping of complex crop types and rotation patterns. Furthermore, the majority of existing research on crop extraction focuses on extracting crops either from high-resolution images in small areas or from low-to-medium resolution images in large areas, thus missing complex and dynamic cropping patterns. To make up this shortage, a phenology-based crop type and the mapping technique of cropping patterns were proposed based on the GEE platform and Sentinel-2 time series imagery for high-accuracy crop mapping of large areas. The Jianghan Plain, an important grain-producing region in south China, was studied. The method for semi-automatic extraction of a large number of samples was explored, and the classification accuracy of multi-grained cascade Forest (gcForest) and that of the Deep Neural Network (DNN) were compared to identify and map the types and cropping patterns of rice, wheat, rapeseed, cotton and lotus root in Jianghan Plain. The results showed that: 1) the main crops in the Jianghan Plain include rice growing in a single season, lotus root, wheat, rapeseed, and cotton. The findings also highlight the major single-season cropping structures, including single rice and lotus root, and the major rotation patterns, consisting of wheat-rice, wheat-cotton, rapeseed-rice, and rapeseed-cotton; 2) the formation and distribution of different crop types and crop rotation patterns are driven by multiple factors, such as climate, topography, socioeconomics, and farmers’ subjective wishes: rice is the most widely planted crop in the Jianghan Plain, especially single rice. In recent years, as the urbanization is intensified and the labor force in agricultural planting is aging, many farmers choose to plant single-season rice due to the lack of labor. Therefore, the planting area of single-season rice dominates and expands; wheat is mainly distributed in the western and northern regions of the plain, and its planting areas are relatively concentrated. Areas sown to wheat in the northern part of the plain mainly carry out wheat-soybean and wheat-rice-based crop rotation patterns, while wheat-cotton and wheat-rice patterns are more frequently seen in the western areas along the river. With the promotion of the national policy on farming with machine, areas sown to wheat in Jianghan Plain are expanding year by year; rapeseed is mainly distributed in the middle of the Plain, mostly in the modes of rapeseed-cotton and rapeseed-rice rotation, for rapeseed and cotton have a large demand for water at each growth stage, and are more suitable for planting in areas with moist soil; 3) the gcForest model based on 4 000 semi-automatic sampling points has the highest overall accuracy, which can reach 87.25%. The classification accuracy of the two models based on 4 000 sampling points is 8.08 and 5.5 percentage points higher than that based on 400 field sampling points respectively. This semi-automatic extraction approach of sampling points can effectively improve the classification accuracy. Besides, whether based on 400 or 4 000 sample points, the classification accuracy of the gcForest model is higher than that of the DNN, indicating that gcForest has an edge when extracting planting patterns in complex agricultural landscapes in south China. In conclusion, the results demonstrate that the phenology-based open-source Sentinel-2 sequential data can effectively support the mapping of planting patterns in large and complex agricultural areas. Thus, the mapping of crop planting patterns in south China presented in this study provides a scientific basis for formulating policies related to crop rotation and sustainable agricultural development. [ABSTRACT FROM AUTHOR]

With the continuous development of hyperspectral remote sensing technology, it has been widely used in vegetation mapping. However, sparse vegetation canopy, soil background, and spectral similarity between different types of vegetation are still the main challenges for vegetation types mapping in arid areas. As a result, it is difficult to achieve reliable classification accuracy by using spectral or texture features separately. Generalized Normal Distribution Optimization (GNDO) is a new feature optimization algorithm, with advantages in quality and stability of feature extraction results, comparing to traditional optimization algorithms. But it has not yet been applied to select bands of hyperspectral data. In order to validate the feasibility of combining ZY-1 02D spectral and texture features to classify vegetation types in arid areas, to verify the effectiveness of the GNDO method for bands selection of hyperspectral data, and to explore the effects of feature selection methods and training sample numbers on the classification accuracy of vegetation mapping, different Wrapper Optimization methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and GNDO, were applied to select spectral features for vegetation mapping, taking the area around Zongjia Town, Dulan County, Qinghai Province, China as the research area, then the band selection results of these methods were analyzed. Train sample set containing 30, 50, 100, 150, and 200 pixels per class were used to select bands and to train the classifier. Different methods (ALL (without bands selection), GA, PSO, GWO, GNDO) and different sizes of the trained sample sets (30, 50, 100, 150, and 200 pixels per class) were used to obtain 25 spectral feature-based classification data sets. Simultaneously, 8 texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment) were extracted using the Gray-level co-occurrence matrix (GLCM) method, and selected on basis of distinguishability for vegetation types. Texture features (TEX) were combined with spectral feature-based classification data sets. The random forest classification method was applied to classify vegetation types for the classification data sets, and the classification accuracy of classification data sets was evaluated and compared. The results show that 1) the blue region (400-450 nm), the red edge region (700-750 nm), and the red region (600-650 nm) are the most sensitive to distinguish the vegetation types in the study area; 2) the GNDO200 achieved the highest overall classification accuracy (80.44%) among the spectral feature-based classification data sets, which was better than the classification accuracy (78.86%) using all bands (ALL200); 3) with the increase of training samples, the overall classification accuracy of each classification data set showed an increasing trend, the classification accuracy of different feature selection methods showed different reliance on the number of training samples; 4) image texture features significantly improved the classification accuracy, and the GWO200+TEX dataset had the highest overall classification accuracy (82.86%). This study could verify the potential of the ZY1-02D, the new hyperspectral satellite data, for the classification of vegetation types in arid areas, and provide an idea for the selection of spectral and texture features in hyperspectral vegetation mapping. [ABSTRACT FROM AUTHOR]

As an ecologically vulnerable area in China, the Loess Plateau has been indicated by many studies to have a significant increase trend in vegetation coverage. However, it is required to clarify the growing trend before and after the implementation of the Grain for Green Project. Based on the third generation of Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (GIMMS NDVI3g) dataset, this study investigated the patterns of spatiotemporal variation of vegetation coverage and the possible environmental factors in the Loess Plateau during 1982-2013, 1982-1999 and 2000-2013 using the Sen+Mann-Kendall model, partial correlation analysis, Mann-Kendall test and Hurst index. The results are as follows: 1) the growing season Normalized Difference Vegetation Index (NDVI) of the study area increased progressively during the periods of 1982-2013, 1982-1999 and 2000-2013, with the rate of 0.019/10 a, 0.016/10 a and 0.057/10 a, respectively. 2) The growing season NDVI of five vegetation types significantly increased during the three periods, especially during 2000-2013, except the forest during 1982-1999. 3) Vegetation coverage showed a decreasing trend from southeast to northwest, and there was an abrupt decrease in the middle of the Loess Plateau. 4) The reverse characteristics of growing season NDVI change were stronger than the same characteristics in the Loess Plateau. Some 31.08% of the Loess Plateau will follow a continuous increasing trend, while 61.88% of the entire study area is predicted to decrease in future. 5) The vegetation coverage was mainly affected by climate change and human activities. Our results also indicated that increasing precipitation is a factor that results in the improvement of the vegetation coverage. In terms of human activity, our result indicted that there was a strong correlation between the cumulative afforestation area and growing season NDVI during 1999-2013. [ABSTRACT FROM AUTHOR]