Vegetation Mapping Research Articles

Multispectral analysis-ready satellite data for three East African mountain ecosystems

The East African mountain ecosystems are facing increasing threats due to global change, putting their unique socio-ecological systems at risk. To monitor and understand these changes, researchers and stakeholders require accessible analysis-ready remote sensing data. Although satellite data is available for many applications, it often lacks accurate geometric orientation and has extensive cloud cover. This can generate misleading results and make it unreliable for time-series analysis. Therefore, it needs comprehensive processing before usage, which encompasses multi-step operations, requiring large computational and storage capacities, as well as expert knowledge. Here, we provide high-quality, atmospherically corrected, and cloud-free analysis-ready Sentinel-2 imagery for the Bale Mountains (Ethiopia), Mounts Kilimanjaro and Meru (Tanzania) ecosystems in East Africa. Our dataset ranges from 2017 to 2021 and is provided as monthly and annual aggregated products together with 24 spectral indices. Our dataset enables researchers and stakeholders to conduct immediate and impactful analyses. These applications can include vegetation mapping, wildlife habitat assessment, land cover change detection, ecosystem monitoring, and climate change research.

Aquatic vegetation mapping with UAS-cameras considering phenotypes

Aquatic vegetation species at the genus level in an oxbow lake were identified in Hungary based on a multispectral Uncrewed Aerial System (UAS) survey within an elongated oxbow lake area of the Tisza River under continental climate. Seven and 13 classes were discriminated using three different classification methods (Support Vector Machine [SVM], Random Forest [RF], and Multivariate Adaptive Regression Splines [MARS]) using different input data in ten combinations: original spectral bands, spectral indices, Digital Surface Model (DSM), and Haralick texture indices. We achieved a high (97.1%) overall accuracies (OAs) by applying the SVM classifier, but the RF performed only <1% worse, as it was represented in the first places of the classification rank before the MARS. The highest classification accuracies (>84% OA) were obtained using the most important variables derived by the Recursive Feature Elimination (RFE) method. The best classification required DSM as an input variable. The poorest classification performance belonged to the model that used only texture indices or spectral indices. On the class level, Stratiotes aloides exhibit the lowest degree of separability compared to the other classes. Accordingly, we recommend using supplementary input data for the classifications besides the original spectral bands, for example, DSM, spectral, and texture indices, as these variables significantly improve the classification accuracies in the proper combinations of the input variables.

Multidecadal mangrove forest change in Macajalar Bay, Northern Mindanao, Philippines (1950-2020) using remote sensing and geographic information systems.

The mangrove forest in Macajalar Bay is regarded as an important coastal ecosystem since it provides numerous ecosystem services. Despite their importance, the clearing of mangroves has been rampant and has reached critical rates. Addressing this problem and further advancing its conservation require accurate mangrove mapping. However, current spatial information related to mangroves is sparse and insufficient to understand the historical change dynamics. In this study, the synergy of 1950 vegetation maps and Landsat images was explored to provide multidecadal monitoring of mangrove forest change dynamics in Macajalar Bay, Philippines. Vegetation maps containing the 1950 mangrove extent and Landsat images were used as input data to monitor the rates of loss over 70years. In 2020, the mangrove forest cover was estimated to be 201.73ha, equivalent to only 61.99% of the 325.43ha that was estimated in 1950. Between 1950 and 2020, net mangrove loss in Macajalar Bay totaled 324.29ha. The highest clearing rates occurred between 1950 and 1990 when it recorded a total of 258.51ha, averaging 6.46ha/year. The original mangrove forest that existed in 1950 only represents 8.56% of the 2020 extent, suggesting that much of the old-growth mangrove had been cleared before 2000 and the existing mangrove forest is mainly composed of secondary mangrove forest stands. Across Macajalar Bay, intensified clearing that happened between 1950 and 1990 has been driven by large-scale aquaculture developments. Mangrove gains on the other hand were evident and have increased the total extent by 79.84ha since 2000 as a result of several afforestation programs. However, approximately half of these gains that were observed since 2010 exhibited low canopy cover. As of writing, approximately 85% of the 2020 mangrove forest stands fall outside the 1950 original mangrove extent. Examining the viability of the original mangrove forest for mangrove reforestation together with promoting site-species matching, and biophysical assessment are necessary undertakings to advance current mangrove conservation initiatives in Macajalar Bay.

A novel transformer‐based graph generation model for vectorized road design

AbstractRoad network design, as an important part of landscape modeling, shows a great significance in automatic driving, video game development, and disaster simulation. To date, this task remains labor‐intensive, tedious and time‐consuming. Many improved techniques have been proposed during the last two decades. Nevertheless, most of the state‐of‐the‐art methods still encounter problems of intuitiveness, usefulness and/or interactivity. As a rapid deviation from the conventional road design, this paper advocates an improved road modeling framework for automatic and interactive road production driven by geographical maps (including elevation, water, vegetation maps). Our method integrates the capability of flexible image generation models with powerful transformer architecture to afford a vectorized road network. We firstly construct a dataset that includes road graphs, density map and their corresponding geographical maps. Secondly, we develop a density map generation network based on image translation model with an attention mechanism to predict a road density map. The usage of density map facilitates faster convergence and better performance, which also serves as the input for road graph generation. Thirdly, we employ the transformer architecture to evolve density maps to road graphs. Our comprehensive experimental results have verified the efficiency, robustness and applicability of our newly‐proposed framework for road design.

Tracking Gross Primary Productivity Using Satellite Solar Induced Fluorescence: Insights Across Agricultural Ecosystems of India

AbstractAccurately estimating gross primary productivity (GPP) in heterogeneous cropping systems from satellite data poses a challenge. In this study, for the first time, we examined how satellite solar‐induced chlorophyll fluorescence (SIF) tracks GPP in India's diverse cropping system. A significant correlation (r = 0.70–0.90, p ≤ 0.05) at 0.0833° spatial resolution is observed between 8daily and monthly FLUXCOM GPP and Global Ozone Monitoring Experiment‐2 (GOME‐2) SIF. Compared to GOME‐2 SIF the finer resolution, TROPOspheric Monitoring Instrument (TROPOMI) SIF showed greater potential (r = 0.80–0.95, p ≤ 0.05) in estimating GPP and crop productivity, except for Southern and Northeastern parts of India. These regions of India have heterogeneous cropland and low crop area fraction as identified from normalized difference vegetation index (NDVI), near‐infrared reflectance of vegetation and local moran's index map of NDVI. This heterogeneity leads to differences in the timing of growth stages across the annual cycle, such that the SIF signal is characterized by low seasonal variability across the cycle, leading to low correlation between GPP and SIF. Further, in addition to physiological control the intra‐annual variability of GPP‐SIF relationship (GPP/SIF ratio) is influenced by temperature, radiation, and moisture availability during peak/late growing seasons. However, in the early growing season, it primarily relies on soil moisture. Our findings should help improve GPP estimation using SIF across the agricultural ecosystems of India.

Vegetation Analysis in the Archaeological Area of Pasargadae WHS (Iran) Enhancing the Naturalistic Value of the Site within the Occurring Environmental Changes

This study provides pioneering research on the vegetation of archaeological areas in Iran to enhance its naturalistic and bioindication values by selecting the Pasargadae World Heritage Site (WHS). Vegetation surveys were carried out in different homogeneous habitats, analyzing the plant communities through statistical elaboration, syntaxonomic role, mapping, and enhancement of plants with conservation interest. In an ecological approach, the study included an analysis of the recent climate changes and human interventions influencing the water resources. Results revealed seven main vegetation types reflecting ecological gradients shaped by environmental, edaphic, and anthropogenic factors. The syntaxonomic analysis showed a primary subdivision in semi-natural grasslands and synanthropic vegetation. Several key species were identified as bioindicators of multiple factors, such as: Launaea acanthodes, Stipa barbata, Alhagi maurorum, Bellevalia saviczii, Glycyrrhiza glabra, Convolvulus arvensis, and Hordeum murinum. The vegetation map showed how the hilly grassland communities hosted the highest number of species with conservation interest and their need to be better protected. Bio-climatic data, such as the construction of dams and the exploitation for irrigation purposes, pointed to the increasing xeric conditions, which make urging conservation efforts for the site’s historical and naturalistic values. The study underscores the importance of preserving places with high plant diversity for effective site management, and enhances the intricate relationship between vegetation and natural features in the occurring environmental changes.

Combining Multitemporal Optical and Radar Satellite Data for Mapping the Tatra Mountains Non-Forest Plant Communities

Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83–0.96), SVM (0.87–0.93), and lower results for XGBoost (0.69–0.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73–0.97 (RF) and 0.66–0.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one–four percent points and Sentinel-1 data by 1%–9%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area.

DETERMINING THE SPECIES COMPOSITION OF FOREST VEGETATION IN THE KOSTANAY REGION USING REMOTE SENSING DATA

During the scientific investigation, woody species of forest vegetation were identified and a map of forest vegetation in the Kostanay region was produced using various data sources: field materials, Earth remote sensing data, and ArcGIS10.9 software. An algorithm was developed to detect tree species based on Landsat 9 satellite imagery, characterized by high spatial resolution. Recognition of dominant tree species was performed using various combinations of spectral bands from Landsat 9 imagery, analysis of vegetation indices (NDVI, EVI) across different seasons, and supervised local adaptive classification. The obtained data were validated against field research materials (August-September 2023) and forest management records. The chosen algorithm implements contemporary approaches to acquiring and processing necessary data from satellite remote sensing imagery. Further differentiation and creation of the forest vegetation map of the Kostanay region were based on the established map of tree species, digital elevation model, geological-geomorphological features, field research, thematic maps, and physical geography of the region. As a result of the conducted research, six classes of forest stands were delineated in the Kostanay region, including light-coniferous and deciduous tree species such as pine, birch, aspen, larch, shrubbery, and meadow vegetation. These research findings and the algorithm developed can be applied to other study areas and hold practical significance.

Forest Community Spatial Modeling Using Machine Learning and Remote Sensing Data

This study examines the application of unsupervised classification techniques in the mapping of forest vegetation, aiming to align vegetation cover with the Braun-Blanquet classification system through remote sensing. By leveraging Landsat 8 and 9 satellite imagery and advanced clustering algorithms, specifically the Weka X-Means, this research addresses the challenge of minimizing researcher subjectivity in vegetation mapping. The methodology incorporates a two-step clustering approach to accurately classify forest communities, utilizing a comprehensive set of vegetation indices to distinguish between different types of forest ecosystems. The validation of the classification model relied on a detailed analysis of over 17,000 relevés from the “Flora” database, ensuring a high degree of accuracy in matching satellite-derived vegetation classes with field observations. The study’s findings reveal the successful identification of 44 forest community types that was aggregated into seven classes of Braun-Blanquet classification system, demonstrating the efficacy of unsupervised classification in generating reliable vegetation maps. This work not only contributes to the advancement of remote sensing applications in ecological research, but also provides a valuable tool for natural resource management and conservation planning. The integration of unsupervised classification with the Braun-Blanquet system presents a novel approach to vegetation mapping, offering insights into ecological characteristics, and can be good starter point for sequestration potential of forest communities’ assessment in the Republic of Tatarstan.

ОСОБО ОХРАНЯЕМЫЕ ПРИРОДНЫЕ ТЕРРИТОРИИ В КОНТЕКСТЕ ГОРОДСКОГО ПЛАНИРОВАНИЯ ГОРОДА ПЕРМИ: ФУНКЦИИ И ПРОБЛЕМЫ

This article is a study using GIS technologies aimed at assessing and analyzing changes in the areas of types of plant communities under the influence of oil and gas industry facilities on the island. Sakhalin.&#x0D; Purpose (Object): The purpose of this study is to assess the degree of change in the areas of types of plant communities under the influence of oil and gas indus-try facilities on the island. Sakhalin.&#x0D; Methods: Methodology for assessing changes in the areas of plant communi-ties under the influence of oil and gas industry facilities on the island. Sakhalin in-cludes 7 stages, consisting of spatial reference of a raster image (vegetation map of the island) to a base map, as well as the subsequent creation of polygonal objects based on the received data and conducting spatial analysis in ArcGIS software. The final stage involves determining the degree of change using specified criteria.&#x0D; Findings: Most of the types of plant communities on Sakhalin Island belong to the conditionally unmodified class with the percentage of the territory occupied by oil and gas industry facilities less than 0.5%. We assigned the following types of plant communities to this class: white birch-larch grass-shrub forests in place of dark coniferous forests (0.00%); reed grass communities in place of green moss and grassy dark coniferous forests (0.17%); etc. The weakly modified class in-cludes 4 types of communities: larch mid-taiga lichen forests with dwarf cedar (0.75%); thickets of wild rosemary in place of larch green moss-ledum forests (0.74%); larch mid-taiga green moss-ledum forests (0.69%); thickets of dwarf ce-dar and alder (0.59%). None of the existing types of plant communities on Sakhalin Island belongs to the moderately or highly modified class, since the area of land occupied by oil and gas industry facilities does not exceed 2%.&#x0D; Conclusions: Assessment of the degree of change in areas occupied by various types of plant communities under the influence of oil and gas industry facilities on the island. Sakhalin makes it possible to determine the most disturbed types of communities and classify them according to the degree of change.&#x0D; This information is useful in view of the relevance of the issue of environmental pollution by oil and gas industry facilities, as well as from the point of view of the possibility of developing measures to protect and protect the environment. In addi-tion, based on the data obtained, it is possible to predict further changes in the de-velopment of Sakhalin shelf projects and the construction of new onshore infra-structure.

Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image

The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sensing, offer a novel solution for precise crop identification. In this study, we evaluated a methodology that integrates object-oriented method and random forest (RF) algorithm for crop identification using multispectral UAV images. The process involved a multiscale segmentation algorithm, utilizing the optimal segmentation scale determined by Estimation of Scale Parameter 2 (ESP2). Eight classification schemes (S1–S8) were then developed by incorporating index (INDE), textural (GLCM), and geometric (GEOM) features based on the spectrum (SPEC) features of segmented objects. The best-trained RF model was established through three steps: feature selection, parameter tuning, and model training. Subsequently, we determined the feature importance for different classification schemes and generated a prediction map of vegetation for the entire study area based on the best-trained RF model. Our results revealed that S5 (SPEC + GLCM + INDE) outperformed others, achieving an impressive overall accuracy (OA) and kappa coefficient of 92.76% and 0.92, respectively, whereas S4 (SPEC + GEOM) exhibited the lowest performance. Notably, geometric features negatively impacted classification accuracy, while the other three feature types positively contributed. The accuracy of ginger, luffa, and sweet potato was consistently lower across most schemes, likely due to their unique colors and shapes, posing challenges for effective discrimination based solely on spectrum, index, and texture features. Furthermore, our findings highlighted that the most crucial feature was the INDE feature, followed by SPEC and GLCM, with GEOM being the least significant. For the optimal scheme (S5), the top 20 most important features comprised 10 SPEC, 7 INDE, and 3 GLCM features. In summary, our proposed method, combining object-oriented and RF algorithms based on multispectral UAV images, demonstrated high classification accuracy for crops. This research provides valuable insights for the accurate identification of various crops, serving as a reference for future advancements in agricultural technology and crop management strategies.

К анализу флоры сосудистых растений Севера Дальнего Востока России

The analysis of the vascular flora list for the North of RussiaТs Far East, with regard to the species systematics, morphology, ecology, and distribution, was carried out in connection with the project of grid mapping of the flora and vegetation. Characteristics of phytochorological division floras are given in accordance with the scheme of the regional phytochorological division based on the generalized large-scale vegetation map.

Long-term changes in vegetation and land use in mountainous areas with heavy snowfalls in northern Japan: an 80-year comparison of vegetation maps

Comparison of old and new vegetation maps is an effective way to detect vegetation dynamics. Recent developments in computer technology have made it possible to accurately compare old paper vegetation maps with current digitized vegetation maps to reveal long-term vegetation dynamics. Recently, a 1:50,000 scale vegetation map of the Hakkoda Mountains in northern Japan, located in the ecotone of cool temperate and subalpine forests in northern Japan under an East Asian monsoon climate, from 1930 was discovered. We compared the 1930s vegetation map with the most recent 2010 vegetation map to test the following hypotheses: 1) the occurrence of upward expansion of the upper limit of cool-temperate deciduous forests, and 2) whether designation as a national park in 1936 would have reduced forestry and land use, expanded beech forests, and cool-temperate deciduous forests. To compare vegetation changes, 67 types of vegetation legends for the 1930 and 2010 maps were unified to 21 based on plant species composition. Consequently, vegetation has changed substantially over the past 80 years. 1) In the subalpine zone above 1,000 m, the coniferous forest area decreased by half. In the cool temperate zone below 1,000 m, the area of beech forests increased 1.48 times, and some of them could be shifted upwards, replacing subalpine fir forests in the lower part of the subalpine zone. 2) In areas below 700 m, deciduous oak forests once used as thickets were almost halved. Instead, climax and beech forests expanded. However, we also found that even after the area was declared a national park, oak forests were cleared and converted to commercial forests such as cedar plantations, cattle ranches, and horse pastures in some areas. These results will be useful for future ecosystem and biodiversity research/conservation and will provide baseline information for climate change adaptation policies.

Enhancing vegetation formation classification: Integrating coarse-scale traditional mapping knowledge and advanced machine learning

Mapping vegetation formation types in large areas is crucial for ecological and environmental studies. However, this is still challenging to distinguish similar vegetation formation types using existing predictive vegetation mapping methods, based on commonly used environmental variables and remote sensing spectral data, especially when there are not enough training samples. To solve this issue, we proposed a predictive vegetation mapping method by integrating an advanced machine learning algorithm and knowledge in an early coarse-scale vegetation map (VMK). First, we implemented classification using the random forest algorithm by integrating the early vegetation map as an auxiliary feature (VMF). Then, we determined the rationality of classified vegetation types and distinguished the confusing types, respectively, based on the knowledge of the spatial distributions and hierarchies of vegetation. Finally, we replaced each recognized unreasonable vegetation type with its corresponding reasonable vegetation type. We implemented the new method in upstream of the Yellow River based on GaoFen-1 satellite images and other environmental variables (i.e., topographical and climate variables). Results showed that the overall accuracy using the VMK method ranged from 67.7 % to 76.8 %, which was 10.9 % to 13.4 % and 3.2 % to 6.6 %, respectively, higher than that of the method without the early vegetation map (NVM) and the VMF method, based on cross-validation with 20 % to 60 % random training samples. The spatial details of the vegetation map using the VMK method were also more reasonable compared to the NVM and VMF methods. These results indicated that the VMK method can distinctly improve the mapping accuracy at the vegetation formation level by integrating knowledge of existing vegetation maps. The proposed method can largely reduce the requirements on the number of field samples, which is especially important for alpine mountains and arctic region, where collecting training samples is more difficult due to the harsh natural environment.

Kilometre-scale simulations over Fennoscandia reveal a large loss of tundra due to climate warming

Abstract. The Fennoscandian boreal and mountain regions harbour a wide range of vegetation types, from boreal forest to high alpine tundra and barren soils. The area is facing a rise in air temperature above the global average and changes in temperature and precipitation patterns. This is expected to alter the Fennoscandian vegetation composition and change the conditions for areal land use such as forestry, tourism and reindeer husbandry. In this study we used a unique high-resolution (3 km) climate scenario with considerable warming resulting from strongly increasing carbon dioxide emissions to investigate how climate change can alter the vegetation composition, biodiversity and availability of suitable reindeer forage. Using a dynamical vegetation model, including a new implementation of potential reindeer grazing, resulted in simulated vegetation maps of unprecedented high resolution for such a long time period and spatial extent. The results were evaluated at the local scale using vegetation inventories and for the whole area against satellite-based vegetation maps. A deeper analysis of vegetation shifts related to statistics of threatened species was performed in six “hotspot” areas containing records of rare and threatened species. In this high-emission scenario, the simulations show dramatic shifts in the vegetation composition, accelerating at the end of the century. Alarmingly, the results suggest the southern mountain alpine region in Sweden will be completely covered by forests at the end of the 21st century, making preservation of many rare and threatened species impossible. In the northern alpine regions, most vegetation types will persist but shift to higher elevations with reduced areal extent, endangering vulnerable species. Simulated potential for reindeer grazing indicates latitudinal differences, with higher potential in the south in the current climate. In the future these differences will diminish, as the potentials will increase in the north, especially for the summer grazing grounds. These combined results suggest significant shifts in vegetation composition over the present century for this scenario, with large implications for nature conservation, reindeer husbandry and forestry.

A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring

Aquatic vegetation is an essential component of lake ecosystems, used as a biological indicator for in situ monitoring within the Water Framework Directive. We developed a hierarchical object-based image classification model with multi-seasonal Sentinel-2 imagery and suitable spectral indices in order to map the aquatic vegetation in a Mediterranean oligotrophic/mesotrophic deep lake; we then applied the model to another lake with similar abiotic and biotic characteristics. Field data from a survey of aquatic macrophytes, undertaken on the same dates as EO data, were used within the accuracy assessment. The aquatic vegetation was discerned into three classes: emergent, floating, and submerged aquatic vegetation. Geographic object-based image analysis (GEOBIA) proved to be effective in discriminating the three classes in both study areas. Results showed high effectiveness of the classification model in terms of overall accuracy, particularly for the emergent and floating classes. In the case of submerged aquatic vegetation, challenges in their classification prompted us to establish specific criteria for their accurate detection. Overall results showed that GEOBIA based on spectral indices was suitable for mapping aquatic vegetation in oligotrophic/mesotrophic deep lakes. EO data can contribute to large-scale coverage and high-frequency monitoring requirements, being a complementary tool to in situ monitoring.

Remote sensing and spectroscopy of lichens.

Lichens are combinations of two symbiotic organisms, a green alga or cyanobacterium and a fungus. They grow in nearly all terrestrial ecosystems and survive in habitats, which are very dry or cold, or too poor in nutrients to maintain vegetation growth. Because lichens grow on visible surfaces and exhibit spectral properties, which are clearly different from, for example, vegetation, it is possible to distinguish them in remote sensing data. In this first systematic review article on remote sensing of lichens, we analyze and summarize which lichen species or genera, and in which habitats and geographical regions, have been remotely sensed, and which remote sensing or spectroscopic technologies have been used. We found that laboratory or insitu measured spectra of over 70 lichen species have been reported to date. We show that studies on remote sensing of lichens fall under seven broad themes: (1) collection of lichen spectra for quantification of lichen species or characteristics, (2) pollution monitoring with lichens as ecological indicators, (3) geological and lithological mapping, (4) desert and dryland monitoring, (5) animal habitat monitoring, (6) land cover or vegetation mapping, and (7) surface energy budget modeling.

Deep-Learning-Based Automatic Extraction of Aquatic Vegetation from Sentinel-2 Images—A Case Study of Lake Honghu

Aquatic vegetation is an important component of aquatic ecosystems; therefore, the classification and mapping of aquatic vegetation is an important aspect of lake management. Currently, the decision tree (DT) classification method based on spectral indices has been widely used in the extraction of aquatic vegetation data, but the disadvantage of this method is that it is difficult to fix the threshold value, which, in turn, affects the automatic classification effect. In this study, Sentinel-2 MSI data were used to produce a sample set (about 930 samples) of aquatic vegetation in four inland lakes (Lake Taihu, Lake Caohai, Lake Honghu, and Lake Dongtinghu) using the visual interpretation method, including emergent, floating-leaved, and submerged vegetation. Based on this sample set, a DL model (Res-U-Net) was used to train an automatic aquatic vegetation extraction model. The DL model achieved a higher overall accuracy, relevant error, and kappa coefficient (90%, 8.18%, and 0.86, respectively) compared to the DT method (79%, 23.07%, and 0.77) and random forest (78%,10.62% and 0.77) when utilizing visual interpretation results as the ground truth. When utilizing measured point data as the ground truth, the DL model exhibited accuracies of 59%, 78%, and 91% for submerged, floating-leaved, and emergent vegetation, respectively. In addition, the model still maintains good recognition in the presence of clouds with the influence of water bloom. When applying the model to Lake Honghu from January 2017 to October 2023, the obtained temporal variation patterns in the aquatic vegetation were consistent with other studies. The study in this paper shows that the proposed DL model has good application potential for extracting aquatic vegetation data.

A hierarchical, multi‐sensor framework for peatland sub‐class and vegetation mapping throughout the Canadian boreal forest

AbstractPeatlands in the Canadian boreal forest are being negatively impacted by anthropogenic climate change, the effects of which are expected to worsen. Peatland types and sub‐classes vary in their ecohydrological characteristics and are expected to have different responses to climate change. Large‐scale modelling frameworks such as the Canadian Model for Peatlands, the Canadian Fire Behaviour Prediction System and the Canadian Land Data Assimilation System require peatland maps including information on sub‐types and vegetation as critical inputs. Additionally, peatland class and vegetation height are critical variables for wildlife habitat management and are related to the carbon cycle and wildfire fuel loading. This research aimed to create a map of peatland sub‐classes (bog, poor fen, rich fen permafrost peat complex) for the Canadian boreal forest and create an inventory of peatland vegetation height characteristics using ICESat‐2. A three‐stage hierarchical classification framework was developed to map peatland sub‐classes within the Canadian boreal forest circa 2020. Training and validation data consisted of peatland locations derived from various sources (field data, aerial photo interpretation, measurements documented in literature). A combination of multispectral data, L‐band SAR backscatter and C‐Band interferometric SAR coherence, forest structure and ancillary variables was used as model predictors. Ancillary data were used to mask agricultural areas and urban regions and account for regions that may exhibit permafrost. In the first stage of the classification, wetlands, uplands and water were classified with 86.5% accuracy. In the second stage, within the wetland areas only, peatland and mineral wetlands were differentiated with 93.3% accuracy. In the third stage, constrained to only the peatland areas, bogs, rich fens, poor fens and permafrost peat complexes were classified with 71.5% accuracy. Then, ICESat‐2 ATL08 spaceborne lidar data were used to describe regional variations in peatland vegetation height characteristics and regional and class‐wise variations based on a boreal forest wide sample. This research introduced a comprehensive large‐scale peatland sub‐class mapping framework for the Canadian boreal forest, presenting the first moderate resolution map of its kind.

Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.