Global change, population growth, and urbanization have been exerting a severe influence on the environment, including the social system and ecosystem. To find solutions based on nature, clarifying the complicated mechanisms and feedback among land use/land cover changes, ecosystem services, and human well-being, is increasingly crucial. However, the in-depth linkages among these three elements have not been clearly and systematically illustrated, present research paths have not been summarized well, and the future research trends on this topic have not been reasonably discussed. In this sense, the purpose of this paper is to provide an insight into how land use/land cover changes, ecosystem services, and human well-being are linked, as well as their relationships, interacting ways, applications in solving ecological and socioeconomic problems, and to reveal their future research trends. Here, we use a systematic literature review of the peer-reviewed literature to conclude the state of the art and the progress, emphasize the hotspot, and reveal the future trend of the nexus among the three aspects. Results show that (1) ecosystem services are generally altered by the changes in land use type, spatial pattern, and intensity; (2) the nexus among land use change, ecosystem services, and human well-being is usually used for supporting poverty alleviation, ecosystem health, biodiversity conservation, and sustainable development; (3) future research on land use/land cover changes, ecosystem services, and human well-being should mainly focus on strengthening multiscale correlation, driving force analysis, the correlation among different group characteristics, land use types and ecosystem service preferences, and the impact of climate change on ecosystem services and human well-being. This study provides an enhanced understanding of the nexus among the three aspects and a reference for future studies to mitigate the relevant problems.

The use of the spatiotemporal data fusion method as an effective data interpolation method has received extensive attention in remote sensing (RS) academia. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) is one of the most famous spatiotemporal data fusion methods, as it is widely used to generate synthetic data. However, the ESTARFM algorithm uses moving windows with a fixed size to get the information around the central pixel, which hampers the efficiency and precision of spatiotemporal data fusion. In this paper, a modified ESTARFM data fusion algorithm that integrated the surface spatial information via a statistical method was developed. In the modified algorithm, the local variance of pixels around the central one was used as an index to adaptively determine the window size. Satellite images from two regions were acquired by employing the ESTARFM and modified algorithm. Results showed that the images predicted using the modified algorithm obtained more details than ESTARFM, as the frequency of pixels with the absolute difference of mean value of six bands’ reflectance between true observed image and predicted between 0 and 0.04 were 78% by ESTARFM and 85% by modified algorithm, respectively. In addition, the efficiency of the modified algorithm improved and the verification test showed the robustness of the modified algorithm. These promising results demonstrated the superiority of the modified algorithm to provide synthetic images compared with ESTARFM. Our research enriches the spatiotemporal data fusion method, and the automatic selection of moving window strategy lays the foundation of automatic processing of spatiotemporal data fusion on a large scale.

With the rise of the strategy of Coordinated Development for the Beijing-Tianjin-Hebei Region, it is necessary to evaluate the trade-offs/synergies of the survival environment and human well-being in Hebei, the capital region of China. However, existing methods cannot analyze and express trade-offs/synergies of two or more variables simultaneously. Therefore, this paper proposes a new framework to express the trade-offs/synergies among land-use intensity, ecosystem services, and human well-being. In this paper, we first identified the land-use intensity change and land-use transformation and evaluated ecosystem services and human well-being in Hebei from 2000–2015 under the Millennium Ecosystem Assessment framework. Then, the trade-offs/synergies of the three indicators were determined by GIS-based methods and MATLAB. The results show that land-use intensity and human well-being mainly present a synergistic relationship, while ecosystem services and land-use intensity mainly present a trade-off relationship, and ecosystem services and human well-being also present a trade-off relationship in Hebei during 2000–2015. In addition, some regional solutions to achieve sustainable development were proposed: region 1 needs to adjust land-use structure, region 2 needs to protect the ecological environment to improve the supply of ecosystem services, and region 3 needs to commit to improving the regional comprehensive human well-being. This study not only proposes a new framework for analyzing trade-offs/synergies of land use intensity, ecosystem services, and human well-being, but it also provides regional solutions for Hebei to achieve sustainable development.

It is very important to obtain regional crop growth conditions efficiently and accurately in the agricultural field. The data assimilation between crop growth model and remote sensing data is a widely used method for obtaining vegetation growth information. This study aims to present a parallel method based on graphic processing unit (GPU) to improve the efficiency of the assimilation between RS data and crop growth model to estimate rice growth parameters. Remote sensing data, Landsat and HJ-1 images were collected and the World Food Studies (WOFOST) crop growth model which has a strong flexibility was employed. To acquire continuous regional crop parameters in temporal-spatial scale, particle swarm optimization (PSO) data assimilation method was used to combine remote sensing images and WOFOST and this process is accompanied by a parallel method based on the Compute Unified Device Architecture (CUDA) platform of NVIDIA GPU. With these methods, we obtained daily rice growth parameters of Zhuzhou City, Hunan, China and compared the efficiency and precision of parallel method and non-parallel method. Results showed that the parallel program has a remarkable speedup (reaching 240 times) compared with the non-parallel program with a similar accuracy. This study indicated that the parallel implementation based on GPU was successful in improving the efficiency of the assimilation between RS data and the WOFOST model and was conducive to obtaining regional crop growth conditions efficiently and accurately.