Sugarcane is a multifunctional crop mainly used for sugar and renewable bioenergy production. Accurate and timely estimation of the sugarcane yield before harvest plays a particularly important role in the management of agroecosystems. The rapid development of remote sensing technologies, especially Light Detecting and Ranging (LiDAR), significantly enhances aboveground fresh weight (AFW) estimations. In our study, we evaluated the capability of LiDAR mounted on an Unmanned Aerial Vehicle (UAV) in estimating the sugarcane AFW in Fusui county, Chongzuo city of Guangxi province, China. We measured the height and the fresh weight of sugarcane plants in 105 sampling plots, and eight variables were extracted from the field-based measurements. Six regression algorithms were used to build the sugarcane AFW model: multiple linear regression (MLR), stepwise multiple regression (SMR), generalized linear model (GLM), generalized boosted model (GBM), kernel-based regularized least squares (KRLS), and random forest regression (RFR). The results demonstrate that RFR (R² = 0.96, RMSE = 1.27 kg m⁻²) performs better than other models in terms of prediction accuracy. The final fitted sugarcane AFW distribution maps exhibited good agreement with the observed values (R² = 0.97, RMSE = 1.33 kg m⁻²). Canopy cover, the distance to the road, and tillage methods all have an impact on sugarcane AFW. Our study provides guidance for calculating the optimum planting density, reducing the negative impact of human activities, and selecting suitable tillage methods in actual cultivation and production.

The advancement of deep learning (DL) technology and Unmanned Aerial Vehicles (UAV) remote sensing has made it feasible to monitor coastal wetlands efficiently and precisely. However, studies have rarely compared the performance of DL with traditional machine learning (Pixel-Based (PB) and Object-Based Image Analysis (OBIA) methods) in UAV-based coastal wetland monitoring. We constructed a dataset based on RGB-based UAV data and compared the performance of PB, OBIA, and DL methods in the classification of vegetation communities in coastal wetlands. In addition, to our knowledge, the OBIA method was used for the UAV data for the first time in this paper based on Google Earth Engine (GEE), and the ability of GEE to process UAV data was confirmed. The results showed that in comparison with the PB and OBIA methods, the DL method achieved the most promising classification results, which was capable of reflecting the realistic distribution of the vegetation. Furthermore, the paradigm shifts from PB and OBIA to the DL method in terms of feature engineering, training methods, and reference data explained the considerable results achieved by the DL method. The results suggested that a combination of UAV, DL, and cloud computing platforms can facilitate long-term, accurate monitoring of coastal wetland vegetation at the local scale.

The spatial distribution patterns of salt marsh plant communities and their biomass provide useful information for monitoring the stability and productivity of coastal salt marsh ecosystems in space and time. However, the spatial patterns of plant vegetation and its aboveground biomass (AGB) in a coastal salt marsh remain unclear. This study mapped the spatial distributions of salt marsh communities and their AGB based on image and LiDAR data acquired by an unmanned aerial vehicle (UAV) in the Yangtze River Estuary. The differences in vegetation structure and AGB at regions located at different distances from tidal creeks were also tested. The results show that biomass estimated through a random forest model is in good agreement (R² = 0.90, RMSE = 0.1 kg m⁻²) with field-measured biomass. The results indicate that an AGB estimation model based on UAV-LiDAR data and a random forest algorithm with high accuracy was useful for efficiently estimating the AGB of salt marsh vegetation. Moreover, for Phragmites australis, both its proportion and AGB increased, while the proportion and AGB of Scirpus mariqueter, Carex scabrifolia, and Imperata cylindrica decreased with increasing distance from tidal creeks. Our study demonstrates that tidal creeks are important for shaping spatial patterns of coastal salt marsh communities by altering soil salinity and soil moisture, so reasonable and scientific measures should be taken to manage and protect coastal ecosystems.

Interrelationships between urban and rural areas are fundamental for the development and safeguarding of viable future living conditions and quality of life. These areas are not well-delineated or self-sufficient, and existing interrelations may privilege one over the other. Major urban challenges facing China and Europe are related to changes in climate, environment, and to decision-making that makes urban and rural landscapes more susceptible to environmental pressures. Focusing on the six European and Chinese cities and surrounding rural areas, under study in the joint EC and MOST-funded REGREEN project, we examine how nature-based solutions (NBS) may assist in counteracting these pressures. We explore urban-rural dependencies and partnerships regarding NBS that can enhance resilience in Europe and China. We analyse differences between European and Chinese systems of governance, reflecting on the significance of the scale of research needed to understand how NBS provide benefits. We highlight interactions between differently delineated sheds (watershed, airshed, natureshed, and peopleshed), which influence the interrelationships between urban and rural areas. There may be one-way or two-way interdependence, and the impact may be uni or multi-directional. The European and Chinese solutions, exemplified in this article, tackle the nexus of environmental and peoplesheds. We discuss complex human interactions (and how to model them) that may, or may not, lead to viable and equitable partnerships for implementing NBS in cities within Europe and in China.

Urban morphology refers to the physical form of a city that is constantly transformed and updated in the process of urbanization. A valuable source of data on ‘built forms’ is modern remote sensing technology, which provides a variety of products on building footprints and heights at national, continental, and global levels. A large-scale comparison of urban morphologies is important for assessing urban development as well as its influence on urban ecology; however, this has not been well documented so far. This study includes 41 cities in China and 36 in Europe with various city sizes, population densities, and climate features. We applied 3D landscape metrics and principal component analysis (PCA) to compare the spatial aspects of the urban morphology of these cities. We found: (1) measurements of the building height, surface fluctuation, and texture directionality of urban building layouts in China are higher than those of European cities, while the latter are high-density and compact built landscapes; (2) a significant clustering phenomenon for Chinese and European cities revealed by PCA, with the former showing a much more aggregated pattern, indicating a relatively uniform morphology of urban buildings in China; (3) distinctions between cities in China and Europe are suggested by the first principal component, to which building height, surface fluctuation, building complexity, and spatial distance among buildings contribute significantly; and (4) the second principal component (mainly represented by maximum building height, surface area, volume, and shape metrics) can separate large metropolitan cities and provincial capitals from cities with lower urban population, smaller size, and slower economic development. Our results demonstrate the potential of 3D landscape metrics for measuring urban morphology. Together with a temporal analysis, these metrics are useful for quantifying how urban morphology varies in space and time on a large scale, as well as evaluating the process of urbanization.

Tidal flats, which are non-vegetated land–sea transition areas, have an important ecological function in the global ecosystem. However, they have been shrinking in recent years due to natural and anthropogenic activities. Although many studies focus on tidal flats in the Yangtze River estuary (YRE) in China, how reclamation and plant invasion affect the expansion and erosion of tidal flats are still unclear. In this study, we analyzed all of the available Landsat TM/ETM+ /OLI imagery from the period 1996 to 2018 using the Google Earth Engine (GEE) cloud computing platform to obtain annual maps of coastal tidal flats of YRE at 30 m spatial resolution. We chose three sample tidal flats where severe Spartina alterniflora (S. alterniflora) invasion, reclamation, and control areas existed to explore the joint impact of plant invasion and reclamation on tidal flats. We also point out the main driving factor of tidal flat expansion of each island in YRE by multiple linear regression. Our results suggest that the tidal flats of YRE had obvious expansion from 1996 to 2018, and the speed of expansion is getting slower because of the decreasing deposits in the Yangtze River. Invasive S. alterniflora is effective at promoting silting, and tidal flats with S. alterniflora invasion expanded 2.54 times faster than the control group. Chongming and Hengsha Islands were mainly affected by sediment concentration, while Changxing and Jiuduansha Islands were affected by reclamation and S. alterniflora invasion, respectively. The results could be used to support coastal zone management and biodiversity conservation of the YRE.