Wheat plays an important role in China’s and the world’s food supply, and it is closely related to economy, culture and life. The spatial distribution of wheat is of great significance to the rational planning of wheat cultivation areas and the improvement of wheat yield and quality. The current rapid development of remote sensing technology has greatly improved the efficiency of traditional agricultural surveys. The extraction of crop planting structure based on remote sensing images and technology is a popular topic in many researches. In response to the shortcomings of traditional methods, this research proposed a method based on the fusion of the pixel-based and object-oriented methods to map the spatial distribution of winter wheat. This method was experimented and achieved good results within Shandong Province. The resulting spatial distribution map of winter wheat has an overall accuracy of 92.2% with a kappa coefficient of 0.84. The comparison with the actual situation shows that the accuracy of the actual recognition of winter wheat is higher and better than the traditional pixel-based classification method. On this basis, the spatial pattern of winter wheat in Shandong was analyzed, and it was found that the topographic undulations had a great influence on the spatial distribution of wheat. This study vividly demonstrates the advantages and possibilities of combining pixel-based and object-oriented approaches through experiments, and also provides a reference for the next related research. Moreover, the winter wheat map of Shandong produced in this research is important for yield assessment, crop planting structure adjustment and the rational use of land resources.

Land cover change detection plays an important role in natural disaster monitoring, tracking urban expansion, and many social benefit areas. The spectral-based direct comparison (SDC) methods are commonly used for change detection, but such methods are vulnerable to the influence of external factors. In general, land changes among different land cover types have different characters of change magnitude. The class probability-based direct comparison (CPDC) methods consider land type information and reduce the influence of external factors, but these methods are strongly dependent on the training samples. To address the above problems, we proposed a novel change detection method that integrates spectral values and class probabilities (SVCP). First, a new change magnitude map based on spectral values and class probabilities is constructed by using the maximum interclass variance and Gaussian mixture model (GMM), which greatly enhances the differentiation between changed and unchanged areas. Second, the Kapur threshold selection method is improved by using the variance of the changed and unchanged areas as well as the class probabilities for adaptive thresholding. The SVCP approach was assessed by two case studies from Landsat 8 Operational Land Imager (OLI) images. The “change/no-change” detection and “from-to” change types were evaluated. The experimental results indicated that the SVCP method is more accurate in the change detection, with lower false and missed detection rates than the traditional methods.

Accurate crop rotation information is essential for understanding food supply, cropland management, and resource allocation, especially in the context of China’s basic situation of “small farmers in a big country”. However, crop rotation mapping for smallholder agriculture systems remains challenging due to the diversity of crop types, complex cropping practices, and fragmented cropland. This research established a sub-seasonal crop information identification framework for crop rotation mapping based on time series Sentinel-2 imagery. The framework designed separate identification models based on the different growth seasons of crops to reduce interclass similarity caused by the same crops in a certain growing season. Features were selected separately according to crops characteristics, and finally explored rotations between them to generate the crop rotation map. This framework was evaluated in the study area of Shandong Province, China, a mix of single-cropping and double-cropping smallholder area. The accuracy assessment showed that the two crop maps achieved an overall accuracy of 0.93 and 0.85 with a Kappa coefficient of 0.86 and 0.80, respectively. The results showed that crop rotation practice mainly occurred in the plains of Shandong, and the predominant crop rotation pattern was wheat and maize. In addition, Land Surface Water Index (LSWI), Soil-Adjusted Vegetation Index (SAVI), Green Chlorophyll Vegetation Index (GCVI), red-edge, and other spectral bands during the peak growing season enabled better performance in crop mapping. This research demonstrated the capability of the framework to identify crop rotation patterns and the potential of the multi-temporal Sentinel-2 for crop rotation mapping under smallholder agriculture system.

Accurate and up-to-date forest monitoring plays a significant role in the country’s society and economy. Many open-access global forest datasets can be used to analyze the forest profile of countries around the world. However, discrepancies exist among these forest datasets due to their specific classification systems, methodologies, and remote sensing data sources, which makes end-users difficult to select an appropriate dataset in different regions. This study aims to explore the accuracy, consistency, and discrepancies of eight widely-used forest datasets in Myanmar, including Hansen2010, CCI-LC2015, FROM-GLC2015/2017, FROM-GLC10, GLC-FCS2015/2020, and GlobeLand30-2020. Firstly, accuracy assessment is conducted by using 934 forest and non-forest samples with four different years. Then, spatial consistency of these eight datasets is compared in area and spatial distribution. Finally, the factors influencing the spatial consistency are analyzed from the aspects of terrain and climate. The results indicate that in Myanmar the forest area derived from GlobeLand30 has the best accuracy, followed by FROM-GLC10 and FROM-GLC2017. The eight datasets differ in spatial detail, with the mountains of northern Myanmar having the highest consistency and the seaward areas of southwestern Myanmar having the highest inconsistency, such as Rakhine and the Ayeyarwady. In addition, it is found that the spatial consistency of the eight datasets is closely related to the terrain and climate. The highest consistency among the eight datasets is found in the range of 1000–3500 m above sea level and 26°–35° slope. In the subtropical highland climate (Cwb) zone, the percentage of complete consistency among the eight datasets is as high as 60.62%, which is the highest consistency among the six climatic zones in Myanmar. Therefore, forest mapping in Myanmar should devote more effort to low topography, seaward areas such as border states like Rakhine, Irrawaddy, Yangon, and Mon. This is because these areas have complex and diverse landscape types and are prone to confusion between forest types (e.g., grassland, shrub, and cropland). The approach can also be applied to other countries, which will help scholars to select the most suitable forest datasets in different regions for analysis, thus providing recommendations for relevant forest policies and planning in different countries.