The chlorophyll content of leaves is an important indicator of plant environmental stress, photosynthetic capacity, and is widely used to diagnose the growth and health status of vegetation. Traditional chlorophyll content inversion is based on the vegetation index under pure species, which rarely considers the impact of interspecific competition and species mixture on the inversion accuracy. To solve these limitations, the harmonic analysis (HA) and the Hilbert–Huang transform (HHT) were introduced to obtain the frequency index, which were combined with spectral index as the input parameters to estimate chlorophyll content based on the unmanned aerial vehicle (UAV) image. The research results indicated that: (1) Based on a comparison of the model accuracy for three different types of indices in the same period, the estimation accuracy of the pure spectral index was the lowest, followed by that of the frequency index, whereas the mixed index estimation effect was the best. (2) The estimation accuracy in November was lower than that in other months; the pure spectral index coefficient of determination (R²) was only 0.5208, and the root–mean–square error (RMSE) was 4.2144. The estimation effect in September was the best. The model R² under the mixed index reached 0.8283, and the RMSE was 2.0907. (3) The canopy chlorophyll content (CCC) estimation under the frequency domain index was generally better than that of the pure spectral index, indicating that the frequency information was more sensitive to subtle differences in the spectrum of mixed vegetation. These research results show that the combination of spectral and frequency information can effectively improve the mapping accuracy of the chlorophyll content, and provid a theoretical basis and technology for monitoring the chlorophyll content of mixed vegetation in wetlands.

Urban forests are highly heterogeneous; information about the combined effect of forest classification scale and algorithm selection on the estimation accuracy for urban forests remains unclear. In this study, we chose Chongming eco-island in the mega-city of Shanghai, a national experimental carbon neutral construction plot in China, as the study object. Remote sensing estimation models (simple regression models vs. machine learning models) of forest carbon density were constructed across different classification scales (all forests, different forest types, and dominant tree species) based on high-resolution aerial photographs and Sentinel-2A remote sensing images, and a large number of field surveys and optimal models were screened by ten-fold cross-validation. The results showed that (1) in early 2020, the total forest area and carbon storage of Chongming eco-island were 307.8 km² and 573,123.6 t, respectively, among which the areal ratios and total carbon storage ratios of evergreen broad-leaved forest, deciduous broad-leaved forest, and warm coniferous forest were 51.4% and 53.3%, 33.5% and 32.8%, and 15.1% and 13.9%, respectively. (2) The average forest carbon density of Chongming eco-island was 18.6 t/ha, among which no differences were detected among the three forest types (i.e., 17.2–19.2 t/ha), opposite to what was observed among the dominant tree species (i.e., 14.6–23.7 t/ha). (3) Compared to simple regression models, machine learning models showed an improvement in accuracy performance across all three classification scales, with average rRMSE and rBias values decreasing by 29.4% and 53.1%, respectively; compared to the all-forests classification scale, the average rRMSE and rBias across the algorithms decreased by 25.0% and 45.2% at the forest-type classification scale and by 28.6% and 44.3% at the tree species classification scale, respectively. We concluded that refining the forest classification, combined with advanced prediction procedures, could improve the accuracy of carbon storage estimates for urban forests.

Coastal wetlands play an important carbon sequestration role in China’s “carbon peaking” and “carbon neutrality” goals. Monitoring aboveground biomass (AGB) is crucial for wetland management. Satellite remote sensing enables efficient retrieval of AGB. However, a variety of statistical models can be used for biomass inversion, depending on factors such as the vegetation type and inversion method. In this study, Landsat 8 Operational Land Imager (OLI) images were preprocessed in the study area through radiation calibration and atmospheric correction for modeling. In terms of model selection, 13 different models, including the univariate regression model, multiple regression model, and machine learning regression model, were compared in terms of their accuracy in estimating the biomass of various wetland vegetation types under their respective optimal parameters. The findings revealed that: (1) the regression models varied across vegetation types, with the accuracy of the biomass estimates decreasing in the order of Scirpus spp. > Spartina alterniflora > Phragmites australis; (2) overall modeling, without distinguishing vegetation types, addressed the challenges of limited samples availability and sampling difficulty. Among them, the random forest regression model outperformed the others in estimating wet and dry AGB with R² values of 0.806 and 0.839, respectively. (3) Comparatively, individual modeling of vegetation types can better reflect the biomass of each wetland vegetation type, especially the dry AGB of Scirpus spp., whose R² and RMSE values increased by 0.248 and 11.470 g/m², respectively. This study evaluates the impact of coastal saltmarsh vegetation types on biomass estimation, providing insights into biomass dynamics and valuable support for wetland conservation and restoration, with potential contributions to global habitat assessment models and international policies like the 30x30 Conservation Agenda.

Revealing the coordination relationship between land use/land cover (LULC) and carbon storage (CS) under diverse climate scenarios is crucial for climate change adaptation in topographically complex regions. This study developed an integrated framework combining the System Dynamics (SD) model, Patch-generating Land Use Simulation (PLUS) model, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, enabling a closed-loop analysis of driving forces, spatial simulation, and ecological feedback. This study systematically assessed LULC evolution and ecosystem CS along China’s National Highway 318 (G318) from 2000 to 2020, and projected LULC and CS under three SSP-RCP scenarios (SSP1-1.9, SSP2-4.5, SSP5-8.5) for 2030. Results show the following: (1) Historical LULC change was dominated by rapid urban expansion, cropland loss, and nonlinear grassland fluctuation, exerting strong impacts on ecosystem dynamics. Future scenario simulations revealed distinct thresholds of ecological pressure. (2) Regional CS exhibited a decline–recovery pattern during 2000–2020, with all 2030 scenarios projecting CS reduction, although ecological-priority pathways could mitigate losses. (3) Coordination between land-use intensity and CS improved gradually, with SSP2-4.5 emerging as the optimal strategy for balancing development and ecological sustainability. Overall, the coupled SD-PLUS-InVEST framework provides a practical tool for policymakers to optimize land use patterns and enhance CS in complex terrains.

Mangroves are critical components of coastal wetland ecosystems, providing essential services such as high-capacity carbon sequestration, biodiversity conservation, and shoreline protection. They play an irreplaceable role in mitigating climate change and maintaining coastal ecological balance. However, the effects of S. alterniflora management on mangrove carbon sinks in coastal wetlands remain poorly quantified, particularly with respect to canopy structural constraints. To address this issue, this study took the Zhangjiang Estuary Mangrove National Nature Reserve in Fujian Province as the study area and constructed a retrieval framework for mangrove above-ground vegetation carbon stock integrating Sentinel-1/2 multi-source remote sensing and ICESat-2 spaceborne LiDAR data. Based on the 10-year time series data from 2016 to 2025, we analyzed the spatiotemporal evolution patterns of mangroves and S. alterniflora. Mangrove area was calculated by counting valid mangrove pixels from 10-m annual land-cover maps generated by the accuracy-validated Random Forest classifier, with pixel area used for areal conversion. We further selected three key years (2021, 2023, and 2025) to explore the phased-variation characteristics of mangrove canopy height and above-ground vegetation carbon stock. The results showed that: (1) the mangrove area in Zhangjiang Estuary increased significantly from 2016 to 2025, with two phases, namely stable at 75.78–81.34 ha (2016–2022) and rapid growth to 183.49 ha by 2025, which is consistent with the expected outcomes of local conservation and restoration practices; (2) mangrove canopy height was retrieved using the random forest model, with a coefficient of determination R² of 0.75 and a root mean square error (RMSE) of 0.92 m, which reliably characterized the spatial distribution patterns of mangrove canopy height in the study area; and (3) from 2021 to 2025, S. alterniflora coverage dropped by more than 98%, while mangrove carbon storage rose 118.3% and vegetation expanded outward to tidal flats. The enhancement of mangrove carbon sink function was highly consistent with the implementation progress of S. alterniflora governance and associated mangrove restoration practices. The results of this study provide a scientific basis for evaluating invasive alien species management outcomes, guiding mangrove ecological restoration, and optimizing blue carbon resource management in coastal wetland ecosystems.

Comprehending the spatiotemporal dynamics, spatial heterogeneity, and pattern-process relationships of small and micro wetlands (SMWs) holds paramount significance for their conservation, restoration, and sustainable management. We integrated Landsat imagery, land-use, topographic, climatic, socioeconomic, and statistical data for 1990–2020 in Anhui Province. Random forest classification, kernel density estimation, standard deviation ellipses, centroid migration, landscape metrics, land-use transition analysis, ecosystem service value (ESV) assessment, and Geodetector were used to characterize SMW change. Total SMW area increased from 53,843.67 ha in 1990 to 71,637.39 ha in 2020, while patch number increased from 104,011 to 125,971; patches smaller than 3 hm² remained dominant. High-density areas were concentrated in the Jianghuai Plain, Yangtze River plain, Chaohu Lake region, and agricultural drainage networks, with a persistent northeast–southwest distribution orientation. Land-use transitions were dominated by exchanges among cropland, water bodies, and SMWs. At constant 2020 grain prices, total ESV increased from approximately CNY 3.668 billion in 1990 to CNY 6.840 billion in 2020, with hydrological regulation remaining among the largest service components. Geodetector results showed temporally varying single-factor spatial associations, while interaction patterns suggested potential joint spatial associations among natural and anthropogenic factors. These results support spatially targeted SMW conservation and management in Anhui Province.