Mangroves are endemic forest communities in tropical and subtropical coastal zones. China’s Hainan Island is very rich in mangrove plant species. In recent years, due to climate change and human disturbance, the living environment of many mangrove distribution areas has been seriously threatened. This study used land satellite series remote sensing images from 1990 to 2020 to monitor the coverage and area changes of mangroves on Hainan Island. The spatial distribution pattern and change trend of mangroves were explored using the standard deviation ellipse method, and the CA–Markov model was used to predict the possible changes of mangroves in the study area in 2025 and 2030. The development trend of mangroves in the future and the driving factors affecting the evolution of mangroves were also analyzed. Over the past 30 years, the area of mangroves has witnessed fluctuations. It decreased from 4578 hectares in 1990 to its lowest at 3870 hectares in 2005 and then rebounded to 4474 hectares by 2020, indicating an initial decline followed by a gradual increase. Although conservation and restoration efforts have yielded success, specific areas have witnessed a decline in mangrove coverage. From 1990 to 2020, mangrove areas in Huiwen decreased from 1055 hectares to 904 hectares, areas in Guannan decreased from 227 hectares to 167 hectares, areas in Xinyinggang decreased from 328 hectares to 298 hectares, areas in Yangpugang decreased from 747 hectares to 682 hectares, areas in Huachangwan decreased from 355 hectares to 327 hectares, and areas in Puqian decreased from 170 hectares to 141 hectares. In particular, the growth in the Eastern and Mayao port areas is especially significant. Additionally, data analysis has revealed the spatial distribution characteristics of mangroves in different regions, such as the mangrove area in Dongzhaigang, which remained relatively stable from 1990 to 2020, while in other areas like Huiwen and Guannan, the mangrove area decreased during these 20 years. By calculating the standard deviation ellipse, we observed that the overall change of mangroves on Hainan Island from 1990 to 2020 was relatively slow, mainly distributed along the northern coastal area of Hainan Island. Furthermore, the standard deviation ellipse and SDE center point of each mangrove growth area have visualized the growth trends of the mangroves. The Markov chain simulation results show that future changes in mangroves will mainly be concentrated in the marginal areas of the mangroves. These areas may be affected by rising sea levels, climate change, soil salinity, and human activities. In the future, mangrove areas are expected to display a dynamic equilibrium, experiencing periods of expansion and reduction, ultimately moving towards a more consistent state. To protect and restore mangroves, it is necessary to strengthen the monitoring and management of their ecological environment and socio-economic factors and improve their stability and diversity.

In the context of increasing urbanization, understanding the influence of urban spatial layouts on microclimatic conditions has become crucial, particularly in high-density reclaimed areas like those in the Macao Peninsula, where optimizing thermal comfort is essential for enhancing residents’ quality of life and promoting sustainable urban development. For this study, we assessed high-density reclaimed plots on the Macao Peninsula for a detailed case study. We conducted on-site measurements along with simulations using ENVI-met software, comparing datasets to ensure model accuracy and reliability. Through careful adjustment of simulation parameters to accurately reflect real-world conditions, we found that building layout and size have a significant effect on the Physiologically Equivalent Temperature (PET). Notably, in the unique climate of the Lingnan area, courtyard layouts demonstrably block external heat and wind, fostering a significantly more comfortable internal microclimate. This design strategy provides not only generous public space for fostering community activities and stronger social bonds, but also creates a visually appealing and cohesive aesthetic. This research seeks to improve resident comfort, optimize urban energy efficiency, and promote sustainable urban development principles. Accordingly, our findings serve as a valuable adaptation reference for urban environmental planning initiatives under the climate in the Lingnan area.

Mangrove mapping increasingly integrates multisource remote sensing, yet the geographically transferable value of additional sensors remains uncertain. This study evaluated Sentinel-2 multispectral imagery, Sentinel-1 and GF-3 SAR, GF-5 AHSI hyperspectral observations, terrain variables, and sensor-independent coastal-context information across five coastal regions of southern China. Five nested feature configurations (D1–D5) were tested with Random Forest, XGBoost, DeepLabV3, and SegFormer under true leave-one-study-area-out validation on 349,898 common 10 m coordinates; deep models were replicated across three random seeds. Model-averaged F1-scores were 0.9025, 0.9031, 0.9123, 0.9134, and 0.9220 for D1–D5, respectively, but differences were not statistically supported across held-out regions (Friedman χ² = 0.48, p = 0.9754). Sentinel-1, GF-3, and their interaction showed small, geographically inconsistent contrasts. GF-5 effects were model- and preprocessing-dependent, and cross-scene normalization altered performance in contrasting directions. Gain and grouped-permutation analyses indicated model-specific predictor use and sensitivity rather than independent sensor contribution. Overall, multisource benefits depended on sensor configuration, model representation, geographic domain, and preprocessing conditions, supporting context-specific sensor selection and geographically independent validation.

This study reframes street-level sustainable urban renewal as a coordination problem between street vitality and relative low-carbon performance, rather than treating vibrant activity and carbon-pressure reduction as separate planning objectives. Its main contribution is an integrated street-level diagnostic framework that combines multidimensional vitality measurement, township-constrained carbon-emission reference estimation, vitality–carbon mismatch identification, and interpretable nonlinear mechanism analysis within unified street analytical units. Although previous studies have substantially advanced the measurement of street vitality and urban carbon emissions, these two strands of research have often developed separately. As a result, limited evidence is available on whether high-vitality streets also perform well in low-carbon terms, where vitality–carbon mismatches emerge, and which built-environment conditions are associated with more coordinated outcomes. Taking the five central districts of Chengdu, China, as a case, this study integrates multi-source activity, mobility, built-environment, and emission-related data. Street vitality is measured through activity agglomeration, temporal continuity, functional support, and external connectivity, while relative low-carbon performance is derived from the reverse normalization of length-normalized carbon-emission intensity based on a township-constrained street-level emission reference estimate. The results show that street vitality and low-carbon performance are spatially uneven and frequently mismatched, as high activity does not automatically translate into stronger low-carbon performance, and lower-carbon pressure does not necessarily indicate a vibrant urban environment. More coordinated streets are associated with context-specific combinations of functional organization, transport operation, built form, street-interface quality, and ecological background. Nonlinear diagnostic results further suggest that coordination is favored by moderate, balanced, and locally adapted built-environment conditions rather than by the simple maximization of individual indicators. These findings shift the discussion from whether vitality and low-carbon performance are desirable in isolation to how they can be jointly diagnosed and improved in street-level urban renewal.