Monitoring Invasion Process of Spartina alterniflora by Seasonal Sentinel-2 Imagery and an Object-Based Random Forest Classification

Trends in the application of remote sensing in blue carbon science

Blue carbon ecosystems (BCEs), such as mangroves, saltmarshes, and seagrasses, are increasingly recognized as natural climate solutions. Evaluating the current extent, losses, and gains of BCEs is crucial to estimating greenhouse gas emissions and supporting policymaking. Remote sensing approaches are uniquely suited to assess the factors driving BCEs dynamics and their impacts at various spatial and temporal scales. Here, we explored trends in the application of remote sensing in blue carbon science. We used bibliometric analysis to assess 2193 published papers for changes in research focus over time (1990 - June 2022). Over the past three decades, publications have steadily increased, with an annual growth rate of 16.9%. Most publications focused on mangrove ecosystems and used the optical spaceborne Landsat mission, presumably due to its long-term, open-access archives. Recent technologies such as LiDAR, UAVs, and acoustic sensors have enabled fine-scale mapping and monitoring of BCEs. Dominant research topics were related to mapping and monitoring natural and human impacts on BCEs, estimating vegetation and biophysical parameters, machine and deep learning algorithms, management (including conservation and restoration), and climate research. Based on corresponding author affiliations, 80 countries contributed to the field, with United States (27.2%), China (15.0%), Australia (7.5%), and India (6.0%) holding leading positions. Overall, our results reveal the need to increase research efforts for seagrasses, saltmarshes, and macroalgae, integrate technologies, increase the use of remote sensing to support carbon accounting methodologies and crediting schemes, and strengthen collaboration and resource sharing among countries. Rapid advances in remote sensing technology and decreased image acquisition and processing costs will likely enhance research and management efforts focused on BCEs.

Invasion patterns of Spartina alterniflora: Response of clones and seedlings to flooding and salinity-A case study in the Yellow River Delta, China

The invasion of Spartina alterniflora has caused severe damage to the coastal wetland ecosystem of the Yellow River Delta, China. Flooding and salinity are key factors influencing the growth and reproduction of S. alterniflora. However, the differences in response of S. alterniflora seedlings and clonal ramets to these factors remain unclear, and it is not known how these differences affect invasion patterns. In this paper, clonal ramets and seedlings were studied separately. Through literature data integration analysis, field investigation, greenhouse experiments, and situational simulation, we demonstrated significant differences in the responses of clonal ramets and seedlings to flooding and salinity changes. Clonal ramets have no theoretical inundation duration threshold with a salinity threshold of 57 ppt (part per thousand); Seedlings have an inundation duration threshold of about 11 h/day and a salinity threshold of 43 ppt. The sensitivity of belowground indicators of two propagules-types to flooding and salinity changes was stronger than that of aboveground indicators, and it is significant for clones (P < 0.05). Clonal ramets have a larger potentially invadable area than seedlings in the Yellow River Delta. However, the actual invasion area of S. alterniflora is often limited by the responses of seedlings to flooding and salinity. In a future sea-level rise scenario, the difference in responses to flooding and salinity will cause S. alterniflora to further compress native species habitats. Our research findings can improve the efficiency and accuracy of S. alterniflora control. Management of hydrological connectivity and strict restrictions on nitrogen input to wetlands, for example, are potential new initiatives to control S. alterniflora invasion.

Comparison of multi-class and fusion of multiple single-class SegNet model for mapping karst wetland vegetation using UAV images

Wetland vegetation classification using deep learning algorithm and unmanned aerial vehicle (UAV) images have attracted increased attentions. However, there exist several challenges in mapping karst wetland vegetation due to its fragmentation, intersection, and high heterogeneity of vegetation patches. This study proposed a novel approach to classify karst vegetation in Huixian National Wetland Park, the largest karst wetland in China by fusing single-class SegNet classification using the maximum probability algorithm. A new optimized post-classification algorithm was developed to eliminate the stitching traces caused by SegNet model prediction. This paper evaluated the effect of multi-class and fusion of multiple single-class SegNet models with different EPOCH values on mapping karst vegetation using UAV images. Finally, this paper carried out a comparison of classification accuracies between object-based Random Forest (RF) and fusion of single-class SegNet models. The specific conclusions of this paper include the followings: (1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall accuracy of 87.34%; (2) the optimized post-classification algorithm improved classification accuracy of SegNet model by eliminating splicing traces; (3) classification performance of single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy (F1-Score) ranging from 10 to 25%; (4) Fusion of single-class SegNet models and object-based RF classifier both produced good classifications for karst wetland vegetation, and achieved over 87% overall accuracy.

Genus-Level Mapping of Invasive Floating Aquatic Vegetation Using Sentinel-2 Satellite Remote Sensing

Invasive floating aquatic vegetation negatively impacts wetland ecosystems and mapping this vegetation through space and time can aid in designing and assessing effective control strategies. Current remote sensing methods for mapping floating aquatic vegetation at the genus level relies on airborne imaging spectroscopy, resulting in temporal gaps because routine hyperspectral satellite coverage is not yet available. Here we achieved genus level and species level discrimination between two invasive aquatic vegetation species using Sentinel 2 multispectral satellite data and machine-learning classifiers in summer and fall. The species of concern were water hyacinth (Eichornia crassipes) and water primrose (Ludwigia spp.). Our classifiers also identified submerged and emergent aquatic vegetation at the community level. Random forest models using Sentinel-2 data achieved an average overall accuracy of 90%, and class accuracies of 79–91% and 85–95% for water hyacinth and water primrose, respectively. To our knowledge, this is the first study that has mapped water primrose to the genus level using satellite remote sensing. Sentinel-2 derived maps compared well to those derived from airborne imaging spectroscopy and we also identified misclassifications that can be attributed to the coarser Sentinel-2 spectral and spatial resolutions. Our results demonstrate that the intra-annual temporal gaps between airborne imaging spectroscopy observations can be supplemented with Sentinel-2 satellite data and thus, rapidly growing/expanding vegetation can be tracked in real time. Such improvements have potential management benefits by improving the understanding of the phenology, spread, competitive advantages, and vulnerabilities of these aquatic plants.

From Remote Sensing to Species Distribution Modelling: An Integrated Workflow to Monitor Spreading Species in Key Grassland Habitats

Remote sensing (RS) has been widely adopted as a tool to investigate several biotic and abiotic factors, directly and indirectly, related to biodiversity conservation. European grasslands are one of the most biodiverse habitats in Europe. Most of these habitats are subject to priority conservation measure, and several human-induced processes threaten them. The broad expansions of few dominant species are usually reported as drivers of biodiversity loss. In this context, using Sentinel-2 (S2) images, we investigate the distribution of one of the most spreading species in the Central Apennine: Brachypodium genuense. We performed a binary Random Forest (RF) classification of B. genuense using RS images and field-sampled presence/absence data. Then, we integrate the occurrences obtained from RS classification into species distribution models to identify the topographic drivers of B. genuense distribution in the study area. Lastly, the impact of B. genuense distribution in the Natura 2000 (N2k) habitats (Annex I of the European Habitat Directive) was assessed by overlay analysis. The RF classification process detected cover of B. genuense with an overall accuracy of 94.79%. The topographic species distribution model shows that the most relevant topographic variables that influence the distribution of B. genuense are slope, elevation, solar radiation, and topographic wet index (TWI) in order of importance. The overlay analysis shows that 74.04% of the B. genuense identified in the study area falls on the semi-natural dry grasslands. The study highlights the RS classification and the topographic species distribution model’s importance as an integrated workflow for mapping a broad-expansion species such as B. genuense. The coupled techniques presented in this work should apply to other plant communities with remotely recognizable characteristics for more effective management of N2k habitats.

Mapping an Invasive Plant Spartina alterniflora by Combining an Ensemble One-Class Classification Algorithm with a Phenological NDVI Time-Series Analysis Approach in Middle Coast of Jiangsu, China

Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost-effective management strategies for conserving native biodiversity. Traditional remote-sensing-based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co-dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one-class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time-series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e., single-scene analysis (SSA) and NDVI-TSA in the core zones of Yancheng National Natural Reserve (YNNR). Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI-TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI-TSA, the EOCC algorithm presented more balanced sensitivity–specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e., senescence stage of November). The achieved results (overall accuracy (OA), Kappa, and true skill statistic (TSS) were 92.92%, 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94%, 0.815, and 0.825 for transferability to the non-training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection.

Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data

Understanding urban spatial pattern of land use is of great significance to urban land management and resource allocation. Urban space has strong heterogeneity, and thus there were many researches focusing on the identification of urban land use. The emergence of multiple new types of geospatial data provide an opportunity to investigate the methods of mapping essential urban land use. The popularization of street view images represented by Baidu Maps is benificial to the rapid acquisition of high-precision street view data, which has attracted the attention of scholars in the field of urban research. In this study, OpenStreetMap (OSM) was used to delineate parcels which were recognized as basic mapping units. A semantic segmentation of street view images was combined to enrich the multi-dimensional description of urban parcels, together with point of interest (POI), Sentinel-2A, and Luojia-1 nighttime light data. Furthermore, random forest (RF) was applied to determine the urban land use categories. The results show that street view elements are related to urban land use in the perspective of spatial distribution. It is reasonable and feasible to describe urban parcels according to the characteristics of street view elements. Due to the participation of street view, the overall accuracy reaches 79.13%. The contribution of street view features to the optimal classification model reached 20.6%, which is more stable than POI features.