: Terraces are typical artificial landforms on the Loess Plateau, with ecological functions in water and soil conservation, agricultural production, and biodiversity. Recording the spatial distribution of terraces is the basis of monitoring their extent and understanding their ecological effects. The current terrace extraction method mainly relies on high-resolution imagery, but its accuracy is limited due to vegetation coverage distorting the features of terraces in imagery. High-resolution topographic data reflecting the morphology of true terrace surfaces are needed. Terraces extraction on the Loess Plateau is challenging because of the complex terrain and diverse vegetation after the implementation of “vegetation recovery”. This study presents an automatic method of extracting terraces based on 1 m resolution digital elevation models (DEMs) and 0.3 m resolution Worldview-3 imagery as auxiliary information used for object-based image analysis (OBIA). A multi-resolution segmentation method was used where slope, positive and negative terrain index (PN), accumulative curvature slope (AC), and slope of slope (SOS) were determined as input layers for image segmentation by correlation analysis and Sheffield entropy method. The main classification features based on DEMs were chosen from the terrain features derived from terrain factors and texture features by gray-level co-occurrence matrix (GLCM) analysis; subsequently, these features were determined by the importance analysis on classification and regression tree (CART) analysis. Extraction rules based on DEMs were generated from the classification features with a total classification accuracy of 89.96%. The red band and near-infrared band of images were used to exclude construction land, which is easily confused with small-size terraces. As a result, the total classification accuracy was increased to 94%. The proposed method ensures comprehensive consideration of terrain, texture, shape, and spectrum characteristics, demonstrating huge potential in hilly-gully loess region with similarly complex terrain and diverse vegetation covers.

The Loess Plateau in China is internationally known for its unique geographical features and has therefore been studied by many researchers. This research exploits the regional differences in the spatial morphological characteristics of Loess shoulder lines in different landform types as an important basis for geomorphological regionalization. In this study, we used ensemble empirical mode decomposition (EEMD), multi-fractal detrended fluctuation analysis (MF-DFA), and detrended cross-correlation analysis (DCCA) to analyze topographic data series extracted from shoulder lines. Loess shoulder-line land variations series data from the Suide, Ganquan, and Chunhua areas on the Loess Plateau were selected and a combination of the two above-mentioned methods was used to study land variations at these three sample sites. The results revealed differences in the topographic variations of the multi-fractal characteristics and the topographic spatial variation in the Loess shoulder line of the three sample sites. Furthermore, the extent to which the results were affected by noise and the analysis scale differed among the three areas.

More and more attention has been paid to farmland water conservancy project (FWCP) maintenance in China, which can reallocate water resources in a more rational and efficient manner. Compared with the traditional survey such as field survey, FWCP maintenance can be improved efficiently with geospatial technology. To improve the level of FWCP maintenance in China, a collaborative sensing system framework by integrating satellite, aerial, and ground remote sensing is put forward. The structure of the system framework includes three sections, namely the data acquisition, the operational work, and the application and service. Through the construction and operation of such collaborative sensing system, it will break through the limitation of any single remote sensing platform and provide all-around and real-time information on FWCP. The collaborative monitoring schemes for the designed FWCP maintenance can engage ditch riders to maintain more effectively, which will enable them to communicate more specifically with smallholders in the process of irrigation. Only when ditch riders and farmers are fully involved, irrigation efficiency will be improved. Furthermore, the collaborative sensing system needs feasible standards for multi-source remote sensing data processing and intelligent information extraction such as data fusion, data assimilation, and data mining. In a way, this will promote the application of remote sensing in the field of agricultural irrigation and water saving. On the whole, it will be helpful to improve the traditional maintenance problems and is also the guarantee for establishing a long-term scientific management mechanism of FWCP maintenance in developing countries, especially in China.