Identifying ecologically vulnerable areas and understanding the responses of phenology to negative changes in vegetation growth are important bases for ecological restoration. However, identifying ecologically vulnerable areas is difficult because it requires high spatial resolution and dense temporal resolution data over a long time period. In this study, a novel method is presented to identify ecologically vulnerable areas based on the normalized difference vegetation index (NDVI) time series from MOD09A1. Here, ecologically vulnerable areas are defined as those that experienced negative changes frequently and greatly in vegetation growth after the disturbances during 2000–2018. The number and magnitude of negative changes detected by the Breaks for Additive Season and Trend (BFAST) algorithm based on the NDVI time-series data were combined to identify ecologically vulnerable areas. TIMESAT was then used to extract the phenology metrics from an NDVI time series dataset to characterize the vegetation responses due to the abrupt negative changes detected by the BFAST algorithm. Focus was given to Jilin Province, a region of China known to be ecologically vulnerable because of frequent drought. The results showed that 13.52% of the study area, mostly in Jilin Province, is ecologically vulnerable. The vulnerability of trees is the lowest, while that of sparse vegetation is the highest. The response of phenology is such that the relative amount of vegetation biomass and length of the growing period were decreased by negative changes in growth for dense vegetation types. The present research results will be useful for the protection of environments being disturbed by regional ecological restoration.

The extraction and evaluation of crop production units are important foundations for agricultural production and management in modern smallholder regions, which are very significant to the regulation and sustainable development of agriculture. Crop areas have been recognized efficiently and accurately via remote sensing (RS) and machine learning (ML), especially deep learning (DL), which are too rough for modern smallholder production. In this paper, a delimitation-grading method for actual crop production units (ACPUs) based on RS images was explored using a combination of a mask region-based convolutional neural network (Mask R-CNN), spatial analysis, comprehensive index evaluation, and cluster analysis. Da’an City, Jilin province, China, was chosen as the study region to satisfy the agro-production demands in modern smallholder areas. Firstly, the ACPUs were interpreted from perspectives such as production mode, spatial form, and actual productivity. Secondly, cultivated land plots (C-plots) were extracted by Mask R-CNN with high-resolution RS images, which were used to delineate contiguous cultivated land plots (CC-plots) on the basis of auxiliary data correction. Then, the refined delimitation-grading results of the ACPUs were obtained through comprehensive evaluation of spatial characteristics and real productivity clustering. For the conclusion, the effectiveness of the Mask R-CNN model in C-plot recognition (loss = 0.16, mean average precision (mAP) = 82.29%) and a reasonable distance threshold (20 m) for CC-plot delimiting were verified. The spatial features were evaluated with the scale-shape dimensions of nine specific indicators. Real productivities were clustered by the incorporation of two-step cluster and K-Means cluster. Furthermore, most of the ACPUs in the study area were of a reasonable scale and an appropriate shape, holding real productivities at a medium level or above. The proposed method in this paper can be adjusted according to the changes of the study area with flexibility to assist agro-supervision in many modern smallholder regions.

Mastering the spatial distribution of water retention capacity and scientifically delineating the ecological space for water conservation are of great significance to the management of regional land and water resources. In this paper, a water conservation ecological spatial delimitation framework suitable for water-deficient areas was put forward. The water retention capacity of the study area in 1983, 1990, 2000, 2010, and 2016 was evaluated by using the InVEST Water Yield model and water balance method, respectively. On this basis, a flexible inflection point model based on the contribution degree of functional units was established. Then the ecological space for water conservation was delimited. The framework was applied to the delimitation of the key water conservation areas in Jilin province, China. The results showed that: (1) the spatial distribution pattern of water conservation in Jilin province gradually decreased from east to west. The spatial difference was significant. The maximum value of water conservation in Jilin province was 730 mm. From 1983 to 2016, water conservation, which accounted for 75.71% of the area, showed an upward trend. The overall water retention capacity showed the characteristics of the overall increase and the local decline. (2) From the absolute amount of the effect of unit area change on water conservation, the intensity from the high to the low was forestland, cultivated land, grassland, unused land, buildings, and water. (3) The area of water conservation less than 474 mm accounted for more than 80% of the total study area. The overall water retention capacity was low. High importance ecological space area of water conservation was comprehensively defined as 36.97%, which was mainly distributed in the natural forest area of Changbai Mountain in the east and the south of Song Liao Plain. Therefore, this study provided a basic layout of relatively concentrated ecological spatial distribution for water conservation types at different levels in Jilin province. The study results and conclusions of this paper will provide a reference for water conservation assessment and the regional land’s natural resources management.