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The aboveground biomass (AGB) of wetland vegetation is a crucial indicator for assessing the health of wetland ecosystems. In the context of global biodiversity threats, biodiversity has become a focal point in ecological and remote sensing research. This study focuses on the Beiliuyao area on Chongming Island, and ground-based biomass data and unmanned aerial vehicle (UAV) hyperspectral imagery are employed for the regional-scale estimation of the AGB of wetland vegetation. Considering the significant differences in AGB between Spartina alterniflora and Phragmites australis during different phenological periods, and AGB retrieval models are constructed based on vegetation classification. Multivariate stepwise regression (MSR), BP neural network (BP), and random forest regression (RFR) models are used to estimate both the dry and wet weights of AGB at the species level. The research results are as follows: (1) Compared with the other three estimation models for the same period, the RFR model yields the highest accuracy, with an R2 reaching 0.82 and an RMSE of 116.14 g/m2. (2) The accuracy of the estimations in November is lower under the same model conditions than that in other months, with the lowest R2 of 0.57 and an RMSE of 228.42 g/m2. (3) The weight of the AGB gradually decreases from August to November, and the wet AGB density ranges from 6000 to 7000 g/m2 with an account of 4.2 % of the wet ABG falling within this range in August. The results of this study demonstrate that UAV hyperspectral imagery and the RFR model can be used to effectively estimate the biomass of dominant species in wetlands. This approach provides a theoretical basis for the large-scale, efficient and dynamic monitoring of the AGB of wetland vegetation.

The traditional PV potential calculation based on PV available land only considers a single land use type and lacks a comprehensive assessment of all land types, so the PV potential has not been fully exploited. Considering southern Hebei as the study area, this study quantified the utilisation rate of land that was available for photovoltaic (PV) power by GIS and established a PV potential assessment model that considered various land types. Simultaneously, the regional power consumption was spatialised, and the electricity generation and consumption were compared to categorise areas into four types. The results show that the rooftops in southern Hebei had the largest PV potential, with a theoretical power generation capacity of 64,032 GWh, followed by water bodies, roads, and unused land. These areas had a total power generation capacity of 131,306 GWh. Furthermore, power consumption is spatialised due to its uneven spatial distribution. The spatialisation results of power consumption showed that it was relatively concentrated in cities and their surrounding areas. Therefore, rooftops and roads in urban areas are suitable for priority development. When comparing power consumption and generation, it was found that most of the central urban areas and some economically developed counties belonged to the L–H (Low power generation and high power consumption) category and should be developed first.

Abstract Background A stronger spatial clustering of cancer burden indicates stronger environmental and human behavioral effects. However, which common cancers in China have stronger spatial clustering and knowledge gaps regarding the environmental and human behavioral effects have yet to be investigated. This study aimed to compare the spatial clustering degree and hotspot patterns of 11 common cancers in mainland China and discuss the potential environmental and behavioral risks underlying the patterns. Methods Cancer incidence data recorded at 339 registries in 2014 was obtained from the “China Cancer Registry Annual Report 2017”. We calculated the spatial clustering degree of the common cancers using the global Moran’s Index and identified the hotspot patterns using the hotspot analysis. Results We found that esophagus, stomach and liver cancer have a significantly higher spatial clustering degree ( $$p

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.

Driven by global change and population pressure, droughts and floods have been two of the most serious natural hazards, leading to crop losses and economic havoc in many areas and ultimately affecting more people globally than any other natural hazard [...]

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.

The invasive species Spartina alterniflora show a significant coexistence zonation pattern with local Phragmites australis in different mixture ratio, increasing the difficulty to monitor their distribution directly by remote sensing. Canopy chlorophyll content (CCC) is an important indicator to monitor the growth and physiological status. The objective of this study was to estimate CCC under different mixture ratio. Five spectral indices were selected and combined via back propagation (BP) neural network model for estimating CCC. Combining multi-indices yielded better results (R² = 0.7729, RMSE = 53.01 ug.cm-2) on average than the best single spectral index (R² = 0.7190, RMSE = 63.53 ug.cm-2) without distinguishing interspecies competition, with a total increase of 7.5% in the R² and a decrease of 16.56% in the RMSE. Meanwhile, when considering interspecies competition, the estimating results obtained by the BP neural network model achieved a further improvement of the R² value, ranging from 3.57% to 20.37%, while the prediction error reduced at varying degrees (maximum reduction of 23.78%). The results indicate that combining multi-indices by BP neural network model can alleviate the influence of interspecies competition and achieve higher estimating accuracy. [ABSTRACT FROM AUTHOR]