Plant invasion imposes significant threats to biodiversity and ecosystem function. Thus, monitoring the spatial pattern of invasive plants is vital for effective ecosystem management. Spartina alterniflora (S. alterniflora) has been one of the most prevalent invasive plants along the China coast, and its spread has had severe ecological consequences. Here, we provide new observation from Landsat operational land imager (OLI) images. Specifically, 43 Landsat-8 OLI images from 2014 to 2016, a combination of object-based image analysis (OBIA) and support vector machine (SVM) methods, and field surveys covering the whole coast were used to construct an up-to-date dataset for 2015 and investigate the spatial variability of S. alterniflora in the coastal zone of mainland China. The classification results achieved good estimation, with a kappa coefficient of 0.86 and 96% overall accuracy. Our results revealed that there was approximately 545.80 km² of S. alterniflora distributed in the coastal zone of mainland China in 2015, from Hebei to Guangxi provinces. Nearly 92% of the total area of S. alterniflora was distributed within four provinces: Jiangsu, Shanghai, Zhejiang, and Fujian. Seven national nature reserves invaded by S. alterniflora encompassed approximately one-third (174.35 km²) of the total area of S. alterniflora over mainland China. The Yancheng National Nature Reserve exhibited the largest area of S. alterniflora (115.62 km²) among the reserves. Given the rapid and extensive expansion of S. alterniflora in the 40 years since its introduction and its various ecological effects, geospatially varied responding decisions are needed to promote sustainable coastal ecosystems.

Given the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series images from 1990 to 2015 were used to establish multi-temporal datasets for documenting the temporal dynamics of S. alterniflora invasion. Our observations revealed that S. alterniflora had a continuous expansion with the area increasing by 50,204 ha during the considered 25 years. The largest expansion was identified in Jiangsu Province during the period of 1990–2000, and in Zhejiang Province during the periods 2000–2010 and 2010–2015. Three noticeable hotspots for S. alterniflora invasion were Yancheng of Jiangsu, Chongming of Shanghai, and Ningbo of Zhejiang, and each had a net area increase larger than 5000 ha. Moreover, an obvious shrinkage of S. alterniflora was identified in three coastal cities including the city of Cangzhou of Hebei, Dongguan, and Jiangmen of Guangdong. S. alterniflora invaded mostly into mudflats (>93%) and shrank primarily due to aquaculture (55.5%). This study sheds light on the historical spatial patterns in S. alterniflora distribution and thus is helpful for understanding its invasion mechanism and invasive species management.

Accurate and effective mapping of forest aboveground biomass (AGB) in heterogeneous mountainous regions is a huge challenge but an urgent demand for resource managements and carbon storage monitoring. Conventional studies have related the plot-measured or LiDAR-based biomass to remote sensing data using pixel-based approaches. The object-based relationship between AGB and multi-source data from LiDAR, multi-frequency radar, and optical sensors were insufficiently studied. It deserves the further exploration that maps forest AGB using the object-based approach and combines LiDAR data with multi-sensor images, which has the smaller uncertainty of positional discrepancy and local heterogeneity, in heterogeneous mountainous regions. To address the improvement of mapping accuracy, satellite LiDAR data from GEDI and ICEsat-2, and images of ALOS-2 yearly mosaic L band SAR (Synthetic Aperture Radar), Sentinel-1 C band SAR, Sentinel-2 MSI, and ALOS-1 DSM were combined for pixel- and object-based forest AGB mapping in a vital heterogeneous mountainous forest. For the object-based approach, optimized objects during a multiresolution segmentation were acquired by the ESP (Estimation of the Scale Parameter) tool, and suitable predictors were selected using an algorithm named VSURF (Variable Selection Using Random Forests). The LiDAR variables at the footprint-level were extracted to connect field plots to the multi-sensor objects as a linear bridge. It was shown that forests’ AGB values varied by elevations with a mean value of 142.58 Mg/ha, ranging from 12.61 to 514.28 Mg/ha. The north slope with the lowest elevation (<1100 m) had the largest mean AGB, while the smallest mean AGB was located in the south slope with the altitude above 2000 m. Using independent validation samples, it was indicated by the accuracy comparison that the object-based approach performed better on the precision with relative improvement based on root-mean-square errors (RI_RMSE) of 4.46%. The object-based approach also selected more optimized predictors and markedly decreased the prediction time than the pixel-based analysis. Canopy cover and height explained forest AGB with their effects on biomass varying according to the elevation. The elevation from DSM and variables involved in red-edge bands from MSI were the most contributive predictors in heterogeneous temperate forests. This study is a pioneering exploration of object-based AGB mapping by combining satellite data from LiDAR, MSI, and SAR, which offers an improved methodology for regional carbon mapping in the heterogeneous mountainous forests.

Coastal wetland soil organic carbon (CW-SOC) is crucial for both “blue carbon” and carbon sequestration. It is of great significance to understand the content of soil organic carbon (SOC) in soil resource management. A total of 133 soil samples were evaluated using an indoor spectral curve and were categorized into silty soil and sandy soil. The prediction model of CW-SOC was established using optimized support vector machine regression (OSVR) and optimized random forest regression (ORFR). The Leave-One-Out Cross-Validation (LOO-CV) method was used to verify the model, and the performance of the two prediction models, as well as the models’ stability and uncertainty, was examined. The results show that (1) The SOC content of different coastal wetlands is significantly different, and the SOC content of silty soils is about 1.8 times that of sandy soils. Moreover, the characteristic wavelengths associated with SOC in silty soils are mainly concentrated in the spectral range of 500–1000 nm and 1900–2400 nm, while the spectral range of sandy soils is concentrated in the spectral range of 600–1400 nm and 1700–2400 nm. (2) The organic carbon prediction model of silty soil based on the OSVR method under the first-order differential of reflectance (R′) is the best, with the Adjusted-R² value as high as 0.78, the RPD value is much greater than 2.0 and 5.07, and the RMSE value as low as 0.07. (3) The performance of the OSVR model is about 15~30% higher than that of the support vector machine regression (SVR) model, and the performance of the ORFR model is about 3~5% higher than that of the random forest regression (RFR) model. OSVR and ORFR are better methods of accurately predicting the CW-SOC content and provide data support for the carbon cycle, soil conservation, plant growth, and environmental protection of coastal wetlands.

Mangrove forests are important coastal ecosystems and are crucial for the equilibrium of the global carbon cycle. Monitoring and mapping of mangrove forests are essential for framing knowledge-based conservation policies and funding decisions by governments and managers. The purpose of this study was to monitor mangrove forest dynamics in the Quanzhou Bay Estuary Wetland Nature Reserve. To achieve this goal, we compared and analyzed the spectral discrimination among mangrove forests, mudflats and Spartina using multi-seasonal Landsat images from 1990, 1997, 2005, 2010, and 2017. We identified the spatio-temporal distribution of mangrove forests by combining an optimal segmentation scale model based on object-oriented classification, decision tree and visual interpretation. In addition, mangrove forest dynamics were determined by combining the annual land change area, centroid migration and overlay analysis. The results showed that there were advantages in the approaches used in this study for monitoring mangrove forests. From 1990 to 2017, the extent of mangrove forests increased by 2.48 km², which was mostly converted from mudflats and Spartina. Environmental threats including climate change and sea-level rise, aquaculture development and Spartina invasion, pose potential and direct threats to the existence and expansion of mangrove forests. However, the implementation of reforestation projects and Spartina control plays a substantial role in the expansion of mangrove forests. It has been demonstrated that conservation activities can be beneficial for the restoration and succession of mangrove forests. This study provides an example of how the application of an optimal segmentation scale model and multi-seasonal images to mangrove forest monitoring can facilitate government policies that ensure the effective protection of mangrove forests.

Soil total nitrogen (STN) is a crucial component of the ecosystem’s nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to predict the STN content and map its spatial distribution in the northeastern coastal region of Hebei Province, China, employing the random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods. The purpose was to compare the ability of remote sensing images (Landsat-8, Sentinel-1, and Sentinel-2) with different machine learning methods for predicting STN content. The research results show the following: (1) The three machine learning methods accurately predicted the STN content and the optimal model provided by the XGBoost method, with an R² of 0.627, RMSE of 0.127 g·kg⁻¹, and MAE of 0.092 g·kg⁻¹. (2) The combination of optical and synthetic aperture radar (SAR) images improved prediction accuracy, with the R² improving by 45.5%. (3) The importance of optical images is higher than that of SAR images in the RF, GBM, and XGBoost methods, with optical images accounting for 87%, 76%, and 77% importance, respectively. (4) The spatial distribution of STN content predicted by the three methods is similar. Higher STN contents are distributed in the northern part of the study area, while lower STN contents are distributed in coastal areas. The results of this study can be very useful for inventories of soil nitrogen and provide data support and method references for revealing nitrogen cycling.