Agricultural greenhouse (AG), one of the fastest-growing technology-based approaches worldwide in terms of controlling the environmental conditions of crops, plays an essential role in food production, resource conservation and the rural economy, but has also caused environmental and socio-economic problems due to policy promotion and market demand. Therefore, long-term monitoring of AG is of utmost importance for the sustainable management of protected agriculture, and previous efforts have verified the effectiveness of remote sensing-based techniques for mono-temporal AG mapping in a relatively small area. However, currently, a continuous annual AG remote sensing-based dataset at large-scale is generally unavailable. In this study, an annual AG mapping method oriented to the provincial area and long-term period was developed to produce the first Landsat-derived annual AG dataset in Shandong province, China from 1989 to 2018 on the Google Earth Engine (GEE) platform. The mapping window for each year was selected based on the vegetation growth and the phenological information, which was critical in distinguishing AG from other misclassified categories. Classification for each year was carried out initially based on the random forest classifier after the feature optimization. A temporal consistency correction algorithm based on classification probability was then proposed to the classified AG maps for further improvement. Finally, the average User’s Accuracy, Producer’s Accuracy and F1-score of AG based on visually-interpreted samples over 30 years reached 96.56%, 86.64% and 0.911, respectively. Furthermore, we also found that the ranked features via calculating the importance of each tested feature resulted in the highest accuracy and the strongest stability in the initial classification stage, and the proposed temporal consistency correction algorithm improved the final products by approximately five percent on average. In general, the resultant AG sequence dataset from our study has revealed the expansion of this typical object of “Human–Nature” interaction in agriculture and has a potential application in use of greenhouse-related technology and the scientific planning of protected agriculture.

In order to protect the ecological environment and solve the poverty problem in the western region, China has established an ecological migration (EM) policy. This policy aims to relocate populations from poverty-stricken areas with fragile ecological environments, which inevitably leads to changes in land cover and the ecological environment. The objective of this study was to identify the effects of EM in a typical region (Wuwei), including changes in the land cover and ecological risk (ER). A land cover change monitoring method was implemented for the 2010–2019 period for six land cover classes using random forest, which is an effective supervised machine learning method. The land cover change patterns were analyzed by determining the area changes of the six classes and applying a land use transition matrix, and a landscape ecological risk model based on landscape disturbance and fragility was used. Our results demonstrate that the increase and decrease in the area of cultivated land, unused land, and construction land can be divided into two stages (2010–2015 and 2015–2019). The area of water and perennial snow doubled during the study periods. The major land cover transitions were between unused land and construction land and between unused land and crop land. In addition, the ER value for the Qilian Mountain National Nature Reserve decreased because of the implementation of EM in the study area, indicating that the ecological environment was effectively improved. The results demonstrate the advantage of the proposed approach in understanding the impact of EM on regional land cover changes and the ecological environment so as to provide guidance for follow-up planning and development.

Economic fruit forest is an important part of Chinese agriculture with high economic value and ecological benefits. Using UAV multi-spectral images to research the classification of economic fruit forests based on deep learning is of great significance for accurately understanding the distribution and scale of fruit forests and the status quo of national economic fruit forest resources. Based on the multi-spectral remote sensing images of UAV, this paper constructed semantic segmentation data of economic fruit forests, conducted a comparative study on the classification and identification of economic fruit forests of FCN, SegNet, and U-Net classic semantic segmentation models, and proposed an improved ISDU-Net model. The recognition accuracies of the ISDU-Net were 87.73%, 70.68%, 78.69%, and 0.84, respectively, in terms of pixel accuracy, average intersection ratio, frequency weight intersection ratio, and Kappa coefficient, which were 3.19%, 8.90%, and 4.51% higher than the original U-Net model. The results showed that the improved ISDU-Net could effectively improve the learning ability of the model, perform better in the prediction of short sample categories, obtain a higher classification accuracy of fruit forest crops, and provide a new idea for the research on accurate fruit forest identification.

Change detection, a critical and flourishing Earth observation technology, aims to identify changes through cross-temporal remote sensing images acquired over the same geographical area. With the widespread use in various change scenarios, it becomes essential to utilize heterogeneous images due to the high challenge of accessing the ideal homogeneous images. Nevertheless, domain shift, generated by different imaging factors (e.g., sensors, seasons, atmosphere, illumination), makes it unable to compare the heterogeneous images directly. To address this problem, we propose a deep domain adaptation and disentangled representation network for unsupervised heterogeneous change detection (DADR-HCD), which bridges the domain gap from the perspective of causal mechanisms and compares the differences in the content feature space. In the training stage, the deep features of the input bitemporal images are further disentangled into the domain-invariant (content) features and domain-specific (style) features through an explicit image translation network. Furthermore, unlike comparing the differences at the image level or deep feature space, the change probability maps are directly calculated based on the content feature similarity in the prediction stage, which minimizes the style noise and avoids the asymmetry of image-level translation. Finally, the binary change maps are obtained using threshold segmentation and morphological post-processing strategies. The comprehensive experimental results and detailed analysis on five typical datasets demonstrate the effectiveness and superiority of the proposed DADR-HCD network in the unsupervised heterogeneous change detection task.

Accurate information on the spatial and temporal distribution of abandoned cropland (AC) is crucial for protecting arable land, and maintaining regional food security and ecological stability. Nevertheless, the unavailability of dedicated monitoring for AC, along with the extended time frame required for remote sensing surveillance and the intricate transformation of land cover types following abandonment, poses considerable challenges in producing accurate AC maps for scientific purposes. To address these challenges, a new framework for AC identification was proposed. Specifically, the change detection was performed using the phenology-enhanced change vector analysis (PECVA) method, followed by a co-training semi-supervised classification method based on historical samples and sparse samples (HS-SSC) to classify the land cover type in change areas and obtain long-term land cover mapping results. Next, a land cover type change detector method was applied to identify the location and time of AC occurrences. Sufficient comparative experiments and accuracy validation were conducted to confirm the effectiveness of the PECVA and HS-SSC. The proposed method was verified in areas with single and double cropping per year, with average precision rates of 85.40% and 83.57% respectively for multi-year AC identification. This study offers a promising tool for identifying AC, which can aid in AC recultivation and serve for arable land conservation and land resource management. Our code is available at https://github.com/zhangtingting114/ACI.

Abandoned cropland (AC) is a major type of landcover and land use change that threats food security, nature conservation and the sustainability of land resources. However, in practical applications, existing AC identification methods often struggle to identify effectively and accurately due to issues such as error accumulation in model outputs, sparse labelling and insufficient model generalization capability. To address these issues, we propose a temporal-spectral series pattern self-supervised learning framework (TSPS) for AC identification. Specifically, we first propose the Transformer-based temporal spectral variation pattern identification network (TSVINet), which designed to solve the problems of error accumulation in model results and insufficient generalization observed in existing studies, without expanding the feature space. Then, a self-supervised pre-training strategy, Byol-Index, is proposed. It is used to learn robust and generalizable representations from satellite imagery time series data. Finally, an AC sample generation strategy is proposed to address the problem of sparse labels. To evaluate the proposed method, we conducted experiments in six different study regions in China. The experimental results show that the TSVINet proposed in this paper can effectively mitigate the problem of error accumulation in model results during the AC identification process. It achieves an average accuracy of 93.11% in the six study areas. Through qualitative analysis, it is evident that the pre-training strategy can effectively reduce the dependence of downstream AC identification tasks on labelled data. Overall, TSPS makes the best use of existing sample data and provides a simple and cost-effective solution for accurate AC identification on a large scale.