Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.

The overwhelming spread of Spartina alterniflora (S. alterniflora) (smooth cordgrass) over recent decades has put many native plant communities and coastal environments at risk. Therefore, long-term monitoring of S. alterniflora dynamics is necessary to better understand and manage the invasion of the species. However, it is difficult to map Spartina saltmarshes in China on an annual or multiyear epoch basis. To address this issue, we developed a classification approach integrating Google Earth Engine (GEE) and object-based hierarchical random forest (RF) classification, and we applied this approach to quantify the expansion and dieback of S. alterniflora at Dafeng Milu National Nature Reserve, Jiangsu, China during 1993–2020. Results showed that the area of S. alterniflora expanded from 24.48 ha in 1993 to 1564.96 ha in 2010. However, after ecological hydrological engineering and an increase in Elaphures davidianus (Père David's deer) numbers in 2011, the S. alterniflora area decreased significantly to 944.28 ha in 2020. During 2011–2020, the S. alterniflora area decreased substantially at a rate of 67 ha per year and by 86% in one area studied in Dafeng Milu National Nature Reserve. In 2020, the 944.28 ha of S. alterniflora in the reserve was mainly distributed in mudflats by the sea. Overall, these results show that it is feasible to identify S. alterniflora using the GEE platform and object-based hierarchical RF classification; moreover, this approach could improve understanding and management of this invasion species.

Algal bloom is a serious global issue for inland waters, posing poses a serious threat to aquatic ecosystems. The timely and accurate detection of algal blooms is critical for their control, management and forecasting. Optical satellite imagery with short revisit times has been widely used to monitor algal blooms in marine and large inland waters. However, such images typically are of coarse resolution, limiting their utility to map algal blooms in small inland waters. We developed a new method to map the spatial extent of algal blooms using sentinel-2 multispectral instrument (MSI) and Landsat operational land imager (OLI) images with higher spatial resolution but lower temporal resolution based on the concept of local indicator of spatial association. The mapping results was applied to measure the duration and frequency of algal blooms in Lake Taihu from 2017 to 2020. Our results show that the developed methodology is able to extract the spatial distribution of moderate algal blooms using near-infrared and red-edge bands (bands 6, 7, 8, and 8a of sentinel-2 MSI images or band 5 of Landsat OLI images) by comparison with MODIS FAI data ( R 2 = 0.888 for sentinel-2 MSI and R 2 = 0.85 for Landsat OLI, P < 0.05). However, the temporal resolution of combined Landsat OLI and sentinel-2 MSI images (i.e., up to 2–3 days) is insufficient to monitor algal blooms during the summer time in Lake Taihu due to cloud effects and rapid algal change. Our research has benefits for the management of small inland waters with complex water conditions.

Saltmarsh carbon storage contributes significantly to combating global climate change and achieving regional carbon neutrality. Yet saltmarsh carbon stocks have shown a trend of decline in recent years. Therefore, long-term monitoring and analyzing of saltmarshes for their carbon storage is imperative to better protect and manage this pool of carbon. This study investigated the spatiotemporal dynamics in saltmarsh carbon storage during 1987–2020, by using the Google Earth Engine (GEE) platform and applying the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and analyzed the driving factors of carbon storage in saltmarshes. The key results are as follows. Firstly, carbon density values in saltmarshes ranged more than 14-fold, from 7.24 to 104.99 Mg·hm^-2, and the total carbon storage showed a decreasing trend. Secondly, reduced carbon storage was concentrated in inshore saltmarshes adjacent to reclamation sites, especially in Shandong, whereas augmented carbon storage characterized the offshore saltmarshes dominated by Spartina alterniflora, especially in Shanghai and Jiangsu. Overall, the carbon stocks of saltmarshes have fallen by 10.44 Tg; the decrease in carbon storage caused by Suaeda salsa, Phragmites australis, and mudflats exceeded the increase in carbon storage caused by Spartina alterniflora and Scirpus mariqueter. Further, we found that reclamation was the most dominant driver of carbon storage reductions, except for sea level rise and hurricane disturbances that can also negatively impact carbon storage, while greater carbon storage was closely related to the invasion of Spartina alterniflora. This study’s findings facilitate the development of a carbon storage management strategy for saltmarsh ecosystems to address global climate change and contribute to attaining carbon neutrality.