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Continuous monitoring of coastal water qualities is critical for water resource management and marine ecosystem sustainability. While remote sensing data such as Sentinel-2 satellite imagery routinely provide high-resolution observations for time-series analysis, the cloud-based Google Earth Engine (GEE) platform supports simple image retrieval and large-scale processing. Using coastal waters of Hong Kong as the study area, this study utilized GEE to (i) query and pre-process all Sentinel-2 observations that coincided with in situ measurements; (ii) extract the spectra to develop empirical models for water quality parameters using artificial neural networks; and (iii) visualize the results using spatial distribution maps, time-series charts and an online application. The modeling workflow was applied to 22 water quality parameters and the results suggested the potential to predict the levels of several nutrients and inorganic constituents. In-depth analyses were conducted for chlorophyll-a, suspended solids and turbidity which produced high correlations between the predicted and observed values when validated with an independent dataset. The selected input variables followed spectral characteristics of the optical constituents. The results were considered more robust compared to previous works in the same region due to the automatic extraction of all available images and larger number of observations from different years and months. Besides visualizing long-term spatial and temporal variabilities through distribution maps and time-series charts, potential anomalies in the monitoring period including algal bloom could also be captured using the models developed from historical data. An online application was created to allow novice users to explore and analyze water quality trends with a simple web interface. The integrated use of remotely-sensed images, in situ measurements and cloud computing can offer new opportunities for implementing effective monitoring programs and understanding water quality dynamics. Although the obtained levels of accuracies were below the desired standard, the end-to-end cloud computing workflow demonstrated in this study should be further investigated considering the cost and computational efficiency for timely information delivery.

Early naturalists such as Humboldt observed that changes in topography and anthropogenic disturbances influenced vegetation structure and the composition of animal communities. This holistic view of community assembly continues to shape conservation and restoration strategies in an era of landscape degradation and biodiversity loss. Today, remote sensing affords ecologists the tools for obtaining rapid and precise measures of topography, disturbance history and vegetation structure. Nonetheless, the capacity of such measures to predict the structure of diverse and functionally important insect communities has not been fully explored. We sampled ground‐dwelling ant assemblages with pitfall traps along a successional gradient (15 grasslands, 21 shrublands and 44 forests) in subtropical Asia, and measured the taxonomic (TD) and functional diversity (FD). We used airborne Light Detection and Ranging (LiDAR) and aerial photography—to measure topography, anthropogenic‐fire history and vegetation structure at each site. Using structural equation models, we tested the hypothesis that vegetation structure mediated the effects of topography and anthropogenic‐fire history on ant assemblage TD and FD, with stronger effects on the latter. We found that low elevation and anthropogenic‐fire history promoted ant TD, and by mediating vegetation structure, these factors further controlled ant FD. Specifically, assemblages of ant species occupying more similar niches—as indicated by their lower FD—were found in secondary forests that had more structurally homogeneous vegetation. These sites also had low insolation and high water moisture content, and were not recently burned as revealed by LiDAR‐derived metrics and aerial images. Furthermore, remotely sensed vegetation structures were closely associated with individual ant traits, such as body size and eye length, which reflect species' preferences for habitat structure. Synthesis. Our study uncovers the interactive effects of topography, disturbance history and vegetation structure in determining the TD and FD of ant assemblages in subtropical landscapes. Moreover, it demonstrates that remote sensed data can be leveraged to efficiently elucidate the complex effects of environmental change and disturbances on vegetation structure and consequently insect biodiversity, representing ecological proxies to refine ground investigation plans and support appropriate conservation and restoration measures for degraded landscapes.

Identification and mapping of various habitats with sufficient spatial details are essential to support environmental planning and management. Considering the complexity of diverse habitat types in a heterogeneous landscape, a context-dependent mapping framework is expected to be superior to traditional classification techniques. With the aim to produce a territory-wide habitat map in Hong Kong, a three-stage mapping procedure was developed to identify 21 habitats by combining very-high-resolution satellite images, geographic information system (GIS) layers and knowledge-based modification rules. In stage 1, several classification methods were tested to produce initial results with 11 classes from a WorldView-2/3 image mosaic using a combination of spectral, textural, topographic and geometric variables. In stage 2, modification rules were applied to refine the classification results based on contextual properties and ancillary data layers. Evaluation of the classified maps showed that the highest overall accuracy was obtained from pixel-based random forest classification (84.0%) and the implementation of modification rules led to an average 8.8% increase in the accuracy. In stage 3, the classification scheme was expanded to all 21 habitats through the adoption of additional rules. The resulting habitat map achieved >80% accuracy for most of the evaluated classes and >70% accuracy for the mixed habitats when validated using field-collected points. The proposed mapping framework was able to utilize different information sources in a systematic and controllable workflow. While transitional mixed habitats were mapped using class membership probabilities and a soft classification method, the identification of other habitats benefited from the hybrid use of remote-sensing classification and ancillary data. Adaptive implementation of classification procedures, development of appropriate rules and combination with spatial data are recommended when producing an integrated and accurate map.

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