Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects.

In an increasingly interconnected world, human–environment interactions involving flows of people, organisms, goods, information, and energy are expanding in magnitude and extent, often over long distances. As a universal paradigm for examining these interactions, the telecoupling framework (published in 2013) has been broadly implemented across the world by researchers from diverse disciplines. We conducted a systematic review of the first five years of telecoupling research to evaluate the state of telecoupling science and identify strengths, areas to be improved, and promising avenues for future study. We identified 89 studies using any derivation of the term telecoupling. These works emphasize trade flows, information transfer, and species dispersal at international, national, and regional scales involving one or a few countries, with China, Brazil, and the United States being the most frequently studied countries. Our review showed a rising trend in publications and citations on telecoupling, with 63% of identified telecoupling studies using the framework’s specific language (e.g., “flows”, “agents”). This result suggests that future telecoupling studies could apply the standardized telecoupling language and terminology to better coordinate, synthesize, and operationalize interdisciplinary research. Compelling topics for future research include operationalization of the telecoupling framework, commonalities among telecouplings, telecoupling mechanisms and causality, and telecoupled systems governance. Overall, the first five years of telecoupling research have improved our understanding of human–environment interactions, laying a promising foundation for future social–ecological research in a telecoupled world.

Human and natural systems are more interconnected across distances than ever before

Understanding the spatial pattern of cropping is crucial for proactive conservation efforts in vulnerable ecosystems. This paper presents a spatially explicit probability layer for the conterminous United States which identifies areas of high likelihood of future cropping (i.e., probability of being tilled) based on key geophysical measures of climate, geomorphology, and soil. This dataset identifies areas with a high propensity for cultivation, offering a critical baseline for prioritizing conservation strategies in remaining natural ecosystems at risk of conversion. Derived using underlying geophysical processes, this spatially explicit output can be combined with socio-economic data for more nuanced and regionally specific conservation planning. This resource provides a valuable tool for researchers, land managers, and policymakers seeking to mitigate the impacts of cropping on these ecosystems.