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Vegetation classification has always been the focus of remote sensing applications, especially for tropical regions with fragmented terrain, cloudy and rainy climates, and dense vegetation. How to effectively classify vegetation in tropical regions by using multi-spectral remote sensing with high resolution and red-edge spectrum needs to be further verified. Based on the experiment in Wenchang, Hainan, China, which is located in the tropical monsoon region, and combined with the ZY-1 02D 2.5 m fused images in January, March, July, and August, this paper discusses whether NDVI and four red-edge vegetation indices (VIs), CIre, NDVIre, MCARI, and TCARI, can promote vegetation classification and reduce the saturation. The results show that the schemes with the highest classification accuracies in all phases are those in which the red-edge VIs are involved, which suggests that the red-edge VIs can effectively contribute to the classification of vegetation. The maximum accuracy of the single phase is 86%, and the combined accuracy of the four phases can be improved to 92%. It has also been found that CIre and NDVIre do not reach saturation as easily as NDVI and MCARI in July and August, and their ability to enhance the separability between different vegetation types is superior to that of TCARI. In general, red-edge VIs can effectively promote vegetation classification in tropical monsoon regions, and red-edge VIs, such as CIre and NDVIre, have an anti-saturation performance, which can slow down the confusion between different vegetation types due to saturation. [ABSTRACT FROM AUTHOR]

Context: Vegetation mapping is subject to a diversity of approaches and lack of coordination, leading to low repeatability and predictive power in the species-rich flora of the Southwest Australian Floristic Region. Yet it has potential as a tool of use in fire management. Aims: This project, extending over five decades, aimed to develop an authoritative vegetation classification and map plant fire responses at Two Peoples Bay Nature Reserve. Methods: Using Muir's classification approach, field surveys were conducted with aerial photography in hand. Thirty-three vegetation units were identified, described, mapped, and photographed. Defining attributes and taxa were identified for each unit. Key results: Map, descriptions, and photographs detail forest, woodlands, mallee, scrub thickets, heath, wetlands, and granite communities on the Reserve. The forest, woodland, and shrublands were adequately classified and mapped. However, granite complex and mallee were least satisfactory, oversimplifying a rich diversity of vegetation types and habitats. Conclusions: The Reserve may be divided for management into the central third of heath, shrublands, and low woodlands largely across the isthmus, the dunes and wetlands of the west with a greater diversity of vegetation types, and the eastern granite inselberg attaining 408 m with the most diverse vegetation types. The latter inselberg needs continued protection from fire and other disturbances. Greatest change in vegetation is seen in lowland landscapes where fire activity has also been pronounced. Implications: Vegetation mapping has been a valuable aid for managers and fire planning, and for active comanagement with appropriate Aboriginal families. This project, extending over five decades, developed an authoritative vegetation classification and mapped plant fire responses at Two Peoples Bay Nature Reserve. Using Muir's classification approach, field surveys were conducted with aerial photography in hand. Thirty-three vegetation units were identified, described, mapped, and photographed. Defining attributes and taxa were identified for each unit. Vegetation mapping has been a valuable aid for phytogeographical placement of the Reserve, for managers and for fire planning. This article belongs to the Collection The Natural History of Two Peoples Bay Nature Reserve, Western Australia. [ABSTRACT FROM AUTHOR]

Interactions between carbon (C), nitrogen (N), and phosphorus (P), the vital indicators of ecological restoration, play an important role in signaling the health of ecosystems. Rapidly and accurately mapping foliar C, N, and P is essential for interpreting community structure, nutrient limitation, and primary production during ecosystem recovery. However, research on how to rapidly map C, N, and P in restored areas with mixed plant communities is limited. This study employed laser imaging, detection, and ranging (LiDAR) and hyperspectral data to extract spectral, textural, and height features of vegetation as well as vegetation indices and structural parameters. Causal band, multiple linear regression, and random forest models were developed and tested in a restored area in northern China. Important parameters were identified including (1), for C, red-edge bands, canopy height, and vegetation structure; for N, textural features, height percentile of 40–95%, and vegetation structure; for P, spectral features, height percentile of 80%, and 1 m foliage height diversity. (2) R2 was used to compare the accuracy of the three models as follows: R2 values for C were 0.07, 0.42, and 0.56, for N they were 0.20, 0.48, and 0.53, and for P they were 0.32, 0.39, and 0.44; the random forest model demonstrated the highest accuracy. (3) The accuracy of the concentration estimates could be ranked as C > N > P. (4) The inclusion of LiDAR features significantly improved the accuracy of the C concentration estimation, with increases of 22.20% and 47.30% in the multiple linear regression and random forest models, respectively, although the inclusion of LiDAR features did not notably enhance the accuracy of the N and P concentration estimates. Therefore, LiDAR and hyperspectral data can be used to effectively map C, N, and P concentrations in a mixed plant community in a restored area, revealing their heterogeneity in terms of species and spatial distribution. Future efforts should involve the use of hyperspectral data with additional bands and a more detailed classification of plant communities. The application of this information will be useful for analyzing C, N, and P limitations, and for planning for the maintenance of restored plant communities. [ABSTRACT FROM AUTHOR]

Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification. [ABSTRACT FROM AUTHOR]

Aims: Developing a hierarchical classification system for classes, orders and alliances of the diverse dry grasslands of the Central and Eastern Balkan Peninsula and translating this into an electronic expert system (ES) for the automatic assignment of plots. Location: Serbia, Kosovo, North Macedonia, Bulgaria and northern Greece. Methods: We extracted 5734 plots from the Balkan Dry Grassland Database corresponding to eight classes of dry grasslands reported from the region, using the EuroVegChecklist ES. This data set and later the plots within each derived subunit were subjected to a new numerical approach: starting with an initial partitioning (expert‐interpreted TWINSPAN classification), diagnostic species were determined based on their phi‐values for the target vegetation type and the differences in phi‐values to the next similar types. These diagnostic species were fed into an ES to create a new partitioning, a procedure which was iterated until diagnostic species and species of the ES converged. Then the same approach was applied within each of the derived units to define the units of the next‐lower level. Results: The iterative cluster optimisation (ICO) converged in all cases. The resulting hierarchical expert system (HES) classified 95% of all plots to alliances. We distinguished four classes with eight orders and 12 alliances: (1) Tuberarietea guttatae (Romuleion); (2) Stipo‐Brachypodietea distachyi (Clinopodio alpini‐Thymion striati); (3) Festuco‐Brometea with Brachypodietalia pinnati (Chrysopogono‐Danthonion calycinae and Cirsio‐Brachypodion pinnati), Festucetalia valesiacae (Festucion valesiacae), an unnamed order of rocky steppes (with Pimpinello‐Thymion zygioidis) and Koelerietalia splendentis (Centaureo‐Bromion fibrosi, Saturejion montanae and Diantho haematocalycis‐Festucion hirtovaginatae); (four) Koelerio‐Corynephoretea with Sedo acris‐Festucetalia (Festucion vaginatae) and Trifolio arvensis‐Festucetalia ovinae (Armerio rumelicae‐Potentillion and Minuartio montanae‐Poion molinerii all. nov.). Conclusions: We created a unified hierarchical classification with an electronic ES using diagnostic species defined by phi‐values. Our new approach (ICO‐HES: iterative cluster optimisation for hierarchical expert systems) allows dividing large data sets into meaningful units at several hierarchical levels, and thus has high potential for complex classifications. Importantly, it overcomes the divide between ES species and diagnostic species and re‐unites them into one concept. [ABSTRACT FROM AUTHOR]

The classification of vegetation types worldwide plays a significant role in studies involving remote sensing. This method, used notably in agriculture, aids producers in devising more efficient agricultural management models. It relies on satellite and aircraft technologies to analyze agricultural lands. Nevertheless, the recent emergence of unmanned aerial vehicles (UAVs) has introduced faster and more cost-effective alternatives to traditional satellite and aircraft systems. These UAVs provide higher resolution images, leading to a shift in remote sensing practices. For deep learning in UAV-based image classification, convolutional neural network (CNN) techniques are commonly employed due to their advantageous features and exceptional extraction capabilities. This study proposes a hybrid approach based on CNN, combining 2D depthwise separable convolution (DSC) with a conventional 2D CNN and a Squeeze-and-Excitation network (SENet). The inclusion of SENet aims to boost classification performance without significantly increasing the overall parameter count. By integrating 2D DSC, computational costs and the number of trainable parameters are notably reduced. The multipath network structure's core purpose is to amplify the extracted features from UAV-derived images. The effectiveness of this multipath hybrid approach was evaluated using an orthophoto from Harran University's campus captured by a UAV. The primary goal was to distinguish between mature and immature lavender plants. The results indicate a high accuracy, with immature lavender plants classified at 99.77% accuracy and mature lavender plants at 95.15% accuracy. These findings from experimental studies demonstrate the high effectiveness of our hybrid method in identifying immature lavender plants. [ABSTRACT FROM AUTHOR]

The identification, classification and mapping of different plant communities and habitats is of fundamental importance for defining biodiversity monitoring and conservation strategies. Today, the availability of high temporal, spatial and spectral data from remote sensing platforms provides dense time series over different spectral bands. In the case of supervised mapping, time series based on classical vegetation indices (e.g., NDVI, GNDVI, ...) are usually input characteristics, but the selection of the best index or set of indices (which guarantees the best performance) is still based on human experience and is also influenced by the study area. In this work, several different time series, based on Sentinel-2 images, were created exploring new combinations of bands that extend the classic basic formulas as the normalized difference index. Multivariate Functional Principal Component Analysis (MFPCA) was used to contemporarily decompose the multiple time series. The principal multivariate seasonal spectral variations identified (MFPCA scores) were classified by using a Random Forest (RF) model. The MFPCA and RF classifications were nested into a forward selection strategy to identify the proper and minimum set of indices' (dense) time series that produced the most accurate supervised classification of plant communities and habitat. The results we obtained can be summarized as follows: (i) the selection of the best set of time series is specific to the study area and the habitats involved; (ii) well-known and widely used indices such as the NDVI are not selected as the indices with the best performance; instead, time series based on original indices (in terms of formula or combination of bands) or underused indices (such as those derivable with the visible bands) are selected; (iii) MFPCA efficiently reduces the dimensionality of the data (multiple dense time series) providing ecologically interpretable results representing an important tool for habitat modelling outperforming conventional approaches that consider only discrete time series. [ABSTRACT FROM AUTHOR]

The identification of wetland vegetation is essential for environmental protection and management as well as for monitoring wetlands' health and assessing ecosystem services. However, some limitations on vegetation classification may be related to remote sensing technology, confusion between plant species, and challenges related to inadequate data accuracy. In this paper, vegetation classification in the Yancheng Coastal Wetlands is studied and evaluated from Sentinel-2 images based on a random forest algorithm. Based on consistent time series from remote sensing observations, the characteristic patterns of the Yancheng Coastal Wetlands were better captured. Firstly, the spectral features, vegetation indices, and phenological characteristics were extracted from remote sensing images, and classification products were obtained by constructing a dense time series using a dataset based on Sentinel-2 images in Google Earth Engine (GEE). Then, remote sensing classification products based on the random forest machine learning algorithm were obtained, with an overall accuracy of 95.64% and kappa coefficient of 0.94. Four indicators (POP, SOS, NDVIre, and B12) were the main contributors to the importance of the weight analysis for all features. Comparative experiments were conducted with different classification features. The results show that the method proposed in this paper has better classification. [ABSTRACT FROM AUTHOR]

Approaches to the classification of thermophilous fringe and tall-herb vegetation in Central Europe vary in many respects. In this study, we provide new vegetation-plot data from South Moravia (Czech Republic), develop a new classification based on differences in overall species composition and compare it with recently used classifications. A dataset of 169 relevés fulfilling the condition of at least 50% cover of tall herbs or lower broad-leaved herbs characteristic of fringe vegetation was classified into six informal vegetation types, four of which may be considered equivalent to phytosociological associations. The most mesophilous and species-rich type Potentilla alba-Laserpitium latifolium is distributed on deep loamy soils developed over the Carpathian flysh, mainly in the White Carpathians. Potentilla argentea-Geranium sanguineum is the most heliophilous type, including acidophilous species and occurring on siliceous rock outcrops along the eastern margin of the Bohemian Massif. Type Vincetoxicum hirundinaria-Origanum vulgare is confined to hard base-rich rocks and occurs mainly in the limestone areas of the Moravian Karst and the Pavlov Hills. Type Geranium sanguineum-Peucedanum cervaria occurs mainly on deep calcareous soils developed over loess and Tertiary claystones in the central part of the South Moravian forest-steppe. The floristic classification of the thermophilous fringe and tall-herb vegetation in South Moravia yielded ecologically and geographically well characterized units that simultaneously differ from dry grasslands by the dominance of herbs, mainly tall herbs. It may serve as a model for future revisions of the national syntaxonomic classification scheme of fringe and tall-herb vegetation in the Czech Republic. [ABSTRACT FROM AUTHOR]

The performance of three-dimensional (3D) point cloud reconstruction is affected by dynamic features such as vegetation. Vegetation can be detected by near-infrared (NIR)-based indices; however, the sensors providing multispectral data are resource intensive. To address this issue, this study proposes a two-stage framework to firstly improve the performance of the 3D point cloud generation of buildings with a two-view SfM algorithm, and secondly, reduce noise caused by vegetation. The proposed framework can also overcome the lack of near-infrared data when identifying vegetation areas for reducing interferences in the SfM process. The first stage includes cross-sensor training, model selection and the evaluation of image-to-image RGB to color infrared (CIR) translation with Generative Adversarial Networks (GANs). The second stage includes feature detection with multiple feature detector operators, feature removal with respect to the NDVI-based vegetation classification, masking, matching, pose estimation and triangulation to generate sparse 3D point clouds. The materials utilized in both stages are a publicly available RGB-NIR dataset, and satellite and UAV imagery. The experimental results indicate that the cross-sensor and category-wise validation achieves an accuracy of 0.9466 and 0.9024, with a kappa coefficient of 0.8932 and 0.9110, respectively. The histogram-based evaluation demonstrates that the predicted NIR band is consistent with the original NIR data of the satellite test dataset. Finally, the test on the UAV RGB and artificially generated NIR with a segmentation-driven two-view SfM proves that the proposed framework can effectively translate RGB to CIR for NDVI calculation. Further, the artificially generated NDVI is able to segment and classify vegetation. As a result, the generated point cloud is less noisy, and the 3D model is enhanced. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describe the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model's RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Ecological garden is one of the important links of urban ecosystem. Accurate and effective analysis of temporal and spatial changes of garden vegetation is conducive to the sustainable development of urban resources and environment. However, the elements of garden vegetation are close to each other, and the current analysis methods can't support the remote sensing image analysis in the ecological garden scene. To solve this problem, this paper introduces a new method for recognition and classification based on deep learning networks. Firstly, radiometric correction and geometric correction are used to preprocess the data collected by unmanned aerial vehicle (UAV); the ecological garden remote sensing image analysis network is constructed based on three-dimensional convolutional neural network (3D-CNN), which adopts double convolution pool structure, To address the issue of pixel misclassification due to similar features in landscape vegetation classification, a deep learning network can be used for high-dimensional feature extraction. On this basis, the multiple label conditional random field (MLCRF) algorithm can be applied for global optimization, effectively solving the misclassification problem caused by similar features of vegetation. This method not only improves classification accuracy but also is suitable for large-scale image classification tasks in complex scenes. The simulation results show that the overall recognition accuracy of the proposed 3D-CNN-MLCRF identification method is 97.83% and the Kappa coefficient is 97.18%, which can accurately and effectively realize the task of ecological garden scene classification. [ABSTRACT FROM AUTHOR]

Land cover data is very significant for designing the earth system, managing the natural resources, and also for performing conservation planning. Time-series data are captured with their dynamic vegetation behavior using remote sensing technology, which is broadly utilized in land cover mapping. Most of the Vegetation Index (VI) such as the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) comprises commonly employed features that are obtained from the time-series spectral data. But, these VIs are not validated as the optimal techniques for generating the temporal profiles. Recent researches highly depend on optical satellite imagery for performing these above-mentioned tasks. Dynamic Time Warping (DTW) is said to be an effective optimal solution for solving the existing challenges, especially the improved version of DTW named Time-Weighted Dynamic Time Warping (TWDTW) is used for time-series analysis regarding the time-series vegetation classification. Yet, the TWDTW efficiency is not shown with other comparative machine learning approaches owing to the classification of vegetation type in the mountain areas. The major goal of this paper is to research and create a novel approach for distinguishing the kind of vegetation in a farm region near Ujani Dam in Solapur District, Maharashtra using time-series analysis. For time-series analysis employing satellite images, the suggested model offers a unique Adaptive Time-Weighted Dynamic Time Warping (ATWDTW). The farm's satellite images are first pre-processed before being sent to ATWDTW for examination. The TWDTW idea is optimized for classification performance using a new hybrid metaheuristic technique named Adaptive Coyote Crow Search Optimization (ACCSO). From the experimental results, the performance of the suggested ACCSO-ATWDTW correspondingly provides superior performance to the traditional approaches, where the designed model using ACCSO-ATWDTW provides 7.2%, 5.2%, 9.9%, 4.55%, and 2.33% higher MCC than the MFO-ATWDTW, BSA-ATWDTW, MF-BSA-ATWDTW, CSA-ATWDTW, and COA-ATWDTW at the maximum iteration of 200. This proved the robustness and less sensitivity to training samples of the TWDTW method when applied to mountain vegetation-type classifications. [ABSTRACT FROM AUTHOR]

Since the early 1950s, the development of human settlements and over-exploitation of agriculture in the China side of the Amur River Basin (CARB) have had a major impact on the water environment of the surrounding lakes, resulting in a decrease of aquatic vegetation. According to the United Nations Sustainable Development Goals, a comprehensive understanding of the extent and variability of aquatic vegetation is crucial for preserving the structure and functionality of stable aquatic ecosystems. Currently, there is a deficiency in the CARB long-sequence dataset of aquatic vegetation distribution in China. This shortage hampers effective support for actual management. Therefore, the development of a fast, robust, and automatic method for accurate extraction of aquatic vegetation becomes crucial for large-scale applications. Our objective is to gather information on the spatial and temporal distribution as well as changes in aquatic vegetation within the CARB. Utilizing a hybrid approach that combines the maximum spectral index composite and Otsu algorithm, along with the integration of convolutional neural networks (CNN) and random forest, we applied this methodology to obtain an annual dataset of aquatic vegetation spanning from 1985 to 2020 using Landsat series imagery. The accuracy of this method was validated through both field investigations and Google Images. Upon assessing the confusion matrix spanning from 1985 to 2020, the producer accuracy for aquatic vegetation classification consistently exceeded 87%. Further quantitative analysis unveiled a discernible decreasing trend in both the water and vegetation areas of lakes larger than 20 km2 within the CARB over the past 36 years. Specifically, the total water area decreased from 3575 km2 to 3412 km2, while the vegetation area decreased from 745 km2 to 687 km2. These changes may be attributed to a combination of climate change and human activities. These quantitative data hold significant practical implications for establishing a scientific restoration path for lake aquatic vegetation. They are particularly valuable for constructing the historical background and reference indices of aquatic vegetation. [ABSTRACT FROM AUTHOR]

Vegetation classification, biomass assessment, and wildfire dynamics are interconnected wildfire-ecosystem components. The Chongli District, located in Zhangjiakou City, was the venue for skiing at the 2022 Winter Olympics. Its high mountains and dense forests create a unique environment. The establishment of alpine ski resorts highlighted the importance of comprehensive forest surveys. Understanding vegetation types and their biomass is critical to assessing the distribution of local forest resources and predicting the likelihood of forest fires. This study used satellite multispectral data from the Sentinel-2B satellite to classify the surface vegetation in the Chongli District through K-means clustering. By combining this classification with a biomass inversion model, the total biomass of the survey area can be calculated. The biomass inversion equation established based on multispectral remote sensing data and terrain information in the Chongli area have a strong correlation (shrub forest R2 = 0.811, broadleaf forest R2 = 0.356, coniferous forest R2 = 0.223). These correlation coefficients are key indicators for our understanding of the relationship between remote sensing data and actual vegetation biomass, reflecting the performance of the biomass inversion model. Taking shrubland as an example, a correlation coefficient as high as 0.811 shows the model's ability to accurately predict the biomass of this type of vegetation. In addition, through multiple linear regression, the optimal shrub, broadleaf, and coniferous forest biomass models were obtained, with the overall accuracy reaching 93.58%, 89.56%, and 97.53%, respectively, meeting the strict requirements for survey accuracy. This study successfully conducted vegetation classification and biomass inversion in the Chongli District using remote sensing data. The research results have important implications for the prevention and control of forest fires. [ABSTRACT FROM AUTHOR]

The EuroVegChecklist (EVC) is currently considered to be the basis for the phytosociological classification of the European vegetation, and it is therefore important that it is continually updated, improved and refined. This article revises the nomenclature of three Mediterranean grassland-related classes and some of their contents down to the rank of association, namely Helianthemetea guttati, Poetea bulbosae and Stipo giganteae-Agrostietea castellanae, on the basis of the fourth edition of the International Code of Phytosociological Nomenclature (ICPN). The results show that the correct author citations for the first two classes are as follows: Tuberarietea guttatae Braun-Blanquet 1973, and Poetea bulbosae Rivas Goday et Rivas-Martínez ex Navarro Andrés et Valle Gutiérrez 1984. Instead, the name Stipo giganteae-Agrostietea castellanae is invalid and it is replaced by the new class name Agrostietea castellanae. In addition, we designate the neotype of the association Corynephoro articulati-Helianthemetum guttati (Corynephoro divaricati-Tuberarietum guttatae nom. corr. nov. et mut. nov.), the lectotype of the Poo bulbosae-Trifolietum subterranei, and mutate the names Helianthemetalia guttati and Helianthemion guttati to Tuberarietalia guttatae and Tuberarion guttatae, respectively, as well as two names of associations with a name-giving taxon belonging to the genus Tuberaria. [ABSTRACT FROM AUTHOR]

Vegetation classifications are a valuable tool for guiding management decisions. Vegetation can be classified at different levels of resolution for different purposes. Aggregating finer‐resolution classification units to simplify planning and decision‐making is becoming more common in land management. However, little is known about which resolution or scale of aggregation is appropriate for specific land management practices and the factors to consider when aggregating vegetation classification units. There is a risk that aggregated classification units may overlook vital floristic and structural differences between communities and not accurately represent the patterns of variation in vegetation. This may lead to unanticipated adverse outcomes from management actions. We compared three classification systems to test how well each represented patterns of variation in the vegetation of a semi‐arid region in Victoria, Australia: (a) a finer‐level classification system; (b) a coarser‐resolution system that aggregated finer compositional resolution units for fire management purposes; and (c) a coarser‐resolution classification based on Climate Zones. We conducted floristic and vegetation structure surveys at 249 sites stratified across a climate gradient. Sites were assigned to vegetation classifications based on mapped data layers and checked by ground truthing prior to inclusion. The classification based on a finer compositional resolution best represented variation in both the floristic and structural patterns of the vegetation across this region. This was not unexpected, because accuracy is traded‐off against simplicity when aggregating vegetation classification units. However, the aggregated classification also performed poorly in representing patterns of vegetation when compared with a simple scheme based on Climate Zones. Assessing the adequacy of aggregated vegetation classification units is important, yet often overlooked. To be effective, aggregated vegetation classes must reflect the underlying factors that drive patterns of variation in the vegetation to ensure they support intended management outcomes. [ABSTRACT FROM AUTHOR]

The surveying of forestry resources has recently shifted toward precision and real-time monitoring. This study utilized the BlendMask algorithm for accurately outlining tree crowns and introduced a Bayesian neural network to create a model linking individual tree crown size with diameter at breast height (DBH). BlendMask accurately outlines tree crown shapes and contours, outperforming traditional watershed algorithms in segmentation accuracy while preserving edge details across different scales. Subsequently, the Bayesian neural network constructs a model predicting DBH from the measured crown area, providing essential data for managing forest resources and conducting biodiversity research. Evaluation metrics like precision rate, recall rate, F1-score, and mAP index comprehensively assess the method's performance regarding tree density. BlendMask demonstrated higher accuracy at 0.893 compared to the traditional watershed algorithm's 0.721 accuracy based on experimental results. Importantly, BlendMask effectively handles over-segmentation problems while preserving edge details across different scales. Moreover, adjusting parameters during execution allows for flexibility in achieving diverse image segmentation effects. This study addresses image segmentation challenges and builds a model linking crown area to DBH using the BlendMask algorithm and a Bayesian neural network. The average discrepancies between calculated and measured DBH for Ginkgo biloba, Pinus tabuliformis, and Populus nigra varitalica were 0.15 cm, 0.29 cm, and 0.49cm, respectively, all within the acceptable forestry error margin of 1 cm. BlendMask, besides its effectiveness in crown segmentation, proves useful for various vegetation classification tasks like broad-leaved forests, coniferous forests, and grasslands. With abundant training data and ongoing parameter adjustments, BlendMask attains improved classification accuracy. This new approach shows great potential for real-world use, offering crucial data for managing forest resources, biodiversity research, and related fields, aiding decision-making processes. [ABSTRACT FROM AUTHOR]

With the development of satellite remote sensing technology, a substantial quantity of remote sensing data can be obtained every day, but the ability to extract information from these data remains poor, especially regarding intelligent extraction models for vegetation information in mountainous areas. Because the features of remote sensing images (such as spectral, textural and geometric features) change with changes in illumination, viewing angle, scale and spectrum, it is difficult for a remote sensing intelligent interpretation model with a single data source as input to meet the requirements of engineering or large-scale vegetation information extraction and updating. The effective use multi-source, multi-resolution and multi-type data for remote sensing classification is still a challenge. The objective of this study is to develop a highly intelligent and generalizable classification model of mountain vegetation utilizing multi-source remote sensing data to achieve accurate vegetation extraction. Therefore, a multi-channel semantic segmentation model based on deep learning, FCN-ResNet, is proposed to integrate the features and textures of multi-source, multi-resolution and multi-temporal remote sensing data, thereby enhancing the differentiation of different mountain vegetation types by capturing their characteristics and dynamic changes. In addition, several sets of ablation experiments are designed to investigate the effectiveness of the model. The method is validated on Mt. Taibai (part of the Qinling-Daba Mountains), and the pixel accuracy (PA) of vegetation classification reaches 85.8%. The results show that the proposed multi-channel semantic segmentation model can effectively discriminate different vegetation types and has good intelligence and generalization ability in different mountainous areas with similar vegetation distributions. The multi-channel semantic segmentation model can be used for the rapid updating of vegetation type maps in mountainous areas. [ABSTRACT FROM AUTHOR]

The first comprehensive phytosociological classification of all vegetation types in Europe (EuroVegChecklist; Applied Vegetation Science, 2016, 19, 3–264) contained brief descriptions of each type. However, these descriptions were not standardized and mentioned only the most distinct features of each vegetation type. The practical application of the vegetation classification system could be enhanced if users had the option to select sets of vegetation types based on various combinations of structural, ecological, and biogeographical attributes. Based on a literature review and expert knowledge, we created a new database that assigns standardized categorical attributes of 12 variables to each of the 1106 alliances dominated by vascular plants defined in EuroVegChecklist. These variables include dominant life form, phenological optimum, substrate moisture, substrate reaction, salinity, nutrient status, soil organic matter, vegetation region, elevational vegetation belt, azonality, successional status and naturalness. The new database has the potential to enhance the usefulness of phytosociological classification for researchers and practitioners and to help understand this classification to non‐specialists. [ABSTRACT FROM AUTHOR]

Questions: The classification of plant communities is a well‐established practice in ecology. However, the factors that determine the distribution of common species and the extent of their dominance and overlap are still being debated. Large‐scale studies based on field data are of great interest in the face of biodiversity changes and climate change impacts. This research was designed to answer the following two questions: What are the dominant plant communities in the Patagonian steppes and how do they relate to environmental features, as measured by latitude, longitude, altitude, mean annual precipitation and mean temperature? What have been the recent changes in plant communities in terms of expansion and contraction of different types of communities? Location: Patagonian steppes (South America). Methods: We used a large database of species‐by‐species vegetation cover from 426 monitoring plots in the South American Patagonian steppes, surveyed between 2007 and 2019, with two different assessment dates at least 5 years apart. We applied archetypal analysis to identify different vegetation compositions at a regional scale (called floristic archetypes) and to track their changes over time. In addition, we analyzed the relationship between floristic archetypes and spatial and meteorological variables to assess the association between floristic composition and the environmental context. Results: Seven floristic archetypes were identified in the Patagonian steppes. The dominant and most widespread floristic archetype showed an expanding pattern in floristic trajectories and was positively associated with mean annual temperature. This floristic archetype was characterized by both drought‐ and grazing‐tolerant species. Two floristic archetypes located at the longitudinal extremes showed decreasing relative participation in the vegetation configurations, and the archetype located towards the west showed a positive relationship with annual precipitation, suggesting a threatened scenario in the face of drier conditions. Southern floristic archetypes either showed a slight decrease or remained stable. Conclusions: Patagonian steppes are dominated by both drought‐ and grazing‐tolerant species communities and their spatial distribution is expanding across different biomes, whereas the more forage‐preferred species were associated with a decreasing floristic archetype. A projected drier and hotter climate scenario may potentiate this pattern. The floristic archetypes framework has potential as a monitoring tool for tracking changes in vegetation composition at a mesoscale and for identifying hotspots of vulnerable vegetation in the face of environmental changes. [ABSTRACT FROM AUTHOR]

This work presents a methodology for the hydrological characterization of natural and urban landscapes, focusing on accurate estimations of infiltration capacity and runoff characteristics. By combining existing methods from the literature, we created a systemic process that integrates satellite-based vegetation maps, topography, and soil permeability data. This process generates a detailed vegetation classification and slope-corrected composite curve number (CNcα) map using information at the subpixel level, which is crucial for estimating excess runoff during intense precipitation events. The algorithm designed with this methodology is automated and utilizes freely accessible multispectral imagery. Leveraging the vegetation–impervious–soil (V-I-S) model, it is assumed that land cover comprises V-I-S components at each pixel. Automated Music and spectral Separability-based Endmember Selection is employed on a generic spectral library to obtain the most relevant V-I-S endmember spectra for a particular image, which is then employed in multiple endmember spectral mixture analysis to obtain V-I-S fraction maps. The derived fractions are utilized in combination with the Normalized Difference Vegetation Index and the Modified Normalized Difference Water Index to adapt the CNcα map to different seasons and climatic conditions. The methodology was applied to Esch-sur-Alzette, Luxembourg, over a four-year period to validate the methodology and quantify the increase in the impervious surface area in the commune and the relationship with the runoff dynamics. This approach provides valuable insights into infiltration and runoff dynamics across diverse temporal and geographic ranges. [ABSTRACT FROM AUTHOR]

The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a very important variable in the global water cycle, carbon cycle, and energy cycle. However, there are still some uncertainties in existing ET products. Therefore, this paper evaluates the uncertainty of three widely used global ET products (ERA5-Land, GLDAS-Noah, and MERRA-2) based on the three-cornered hat (TCH) method, and generates a new ET product based on this. The new product is a long-series global monthly ET dataset with a spatial resolution of 0.25° × 0.25° and a time span of 21 years. The results show that ERA5-Land (8.46 mm/month) has the lowest uncertainty among the three ET products, followed by GLDAS-Noah (8.81 mm/month) and MERRA-2 (11.78 mm/month). The new product (TCH) captures ET trends in different regions as well as validating against in situ flux observations, and it exhibits better performance than the re-analysis dataset (ERA5-Land) in vegetation classifications such as evergreen needle-leaf forest, grassland, open shrubland, savanna, and woody savanna. The linear trend analysis of the new product shows a significant decreasing trend in south-eastern South America and southwestern parts of Africa, and an increasing trend in almost all other regions, including eastern North America, north-eastern South America, western Europe, north-central Africa, southern Asia, and south-eastern Oceania. [ABSTRACT FROM AUTHOR]

Fire spread in the Wildland–Urban Interface (WUI) can occur due to direct flame contact, convection, radiation, firebrand attack, or their combinations. Out of them, firebrand attack significantly contributes to damaging structures. To improve the resistance of buildings in wildfire-prone areas, the Australian Standards AS3959 provides construction requirements introducing Bushfire Attack Levels (BAL) based on quantified radiation heat flux. However, quantifying firebrand attack presents challenges, and the standard does not provide specific recommendations in this regard. This study aims to address this research gap by quantifying firebrand flux on houses according to the BALs in Mallee/Mulga-dominated vegetation using physics-based modelling. The study follows the AS3959 vegetation classifications and fire-weather conditions. The study considers Fire Danger Indices (FDI) of 100, 80, and 50 and identifies the housing components most susceptible to firebrand attack and radiant heat flux. The findings reveal an increasing firebrand flux with higher BAL values across all FDIs, with a greater percentage difference observed between FDIs 50 and 80 compared to FDIs 80 and 100. Furthermore, an exponential relationship is found between radiative heat flux and firebrand flux. This research contributes the development of effective strategies to mitigate the firebrand danger and enhance the resilience of structures to enhance AS3959. [ABSTRACT FROM AUTHOR]

Communities with constant presence of two insular endemic species of genus Brassica (B. cazzae Ginzb. et Teyber, B. mollis Vis.) were studied on the small southeastern Adriatic islands and islets (South Croatia), following the Braun-Blanquet approach. From a total of 51 phytosociological relevés, mostly collected in the period from 2020 and 2022, eight associations and three subassociations belonging to seven alliances and six classes were identified. Brassica cazzae and B. mollis occur in a variety of habitat types (i) in bare or very shallow soils on which halophilous and halotolerant communities of the Limonion anfracti-cancellati and Anthyllidion barbae-jovis mostly thrive, (ii) on rocky crevices and cliffs with rupicolous chasmophytic vegetation of the Centaureo cuspidatae-Portenschlagiellion ramosissimae, (iii) within woody vegetation of the Pistacio lentisci-Pinion halepensis, maquis of the Oleo-Ceratonion siliquae and garrigues of the Cisto cretici-Ericion manipuliflorae, and (iv) grasslands of the Chrysopogono grylli-Koelerion splendentis. Two associations (Plantagino holostei-Limonietum cazzae, Brassico mollis-Anthyllidetum barbae-jovis) and one subassociation (Centaureetum ragusinae limonietosum cazzae) are described for the first time. [ABSTRACT FROM AUTHOR]

Clustering is indispensable in the quest for robust vegetation classification schemes that aim to partition, summarise and communicate patterns. However, clustering solutions are sensitive to methods and data and are therefore unstable, a feature that is usually attributed to noise. Viewed through a central‐tendency lens, noise is defined as the degree of departure from type, which is problematic since vegetation types are abstractions of continua, and so noise can only be quantified relative to the particular solution at hand. Graph theory models the structure of vegetation data based on the interconnectivity of samples. Through a graph‐theoretic lens, the causes of instability can be quantified in absolute terms via the degree of connectivity among objects. We simulated incremental increases in sampling intensity in a dataset over five iterations and assessed classification stability across successive solutions derived using algorithms implementing, respectively, models of central‐tendency and interconnectivity. We used logistic regression to model the likelihood of a sample changing groups between iterations as a function of distance to the centroid and degree of interconnectivity. Our results show that the degree to which samples are interconnected is a more powerful predictor of instability than the degree to which they deviate from their nearest centroid. The removal of weakly interconnected samples resulted in more stable classifications, although solutions with many clusters were apparently inherently less stable than those with few clusters, and improvements in stability flowing from the removal of outliers declined as the number of clusters increased. Our results reinforce the fact that clusters abstracted from continuous data are inherently unstable and that the quest for stable, fine‐scale classifications from large regional datasets is illusory. Nevertheless, our results show that using models better suited to the analysis of continuous data may yield more stable classifications of the available data. [ABSTRACT FROM AUTHOR]

Identifying vertical characteristics of mountainous vegetation distribution is necessary for studying the ecological environment quality and biodiversity and for evaluating its responses to climate change. However, producing fine vegetation distribution in a complex mountainous area remains a huge challenge. This study developed a framework based on multi-source high-resolution satellite images to strengthen the understanding of vertical features of vegetation distribution. We fused GaoFen-6 and Sentinel-2 data to produce 2 m multispectral data, combined with ALOS PALSAR digital elevation model (DEM) data, and used an object-based method to extract variables for establishing a classification model. The spatial distribution of vegetation types in Wuyishan National Park (WNP) was then obtained using a hierarchical random forest classifier. The characteristics of different vegetation types along the elevation gradient and their distribution patterns under different human protection levels were finally examined. The results show that (1) An overall accuracy of 87.11% and a Kappa coefficient of 0.85 for vegetation classification was achieved. (2) WNP exhibits obviously vertical differentiation of vegetation types, showing four compound dominant zone groups and five dominant belts. (3) The composition of vegetation types in the scenic area differs significantly from other regions. The proportions of Masson pine and Chinese fir exhibit a noticeably decreasing trend as the distance increases away from roads, while the changes in broadleaf forest and bamboo forest are less pronounced. [ABSTRACT FROM AUTHOR]

Summary: The distribution of roots throughout the soil drives depth‐dependent plant–soil interactions and ecosystem processes, particularly in arctic tundra where plant biomass, is predominantly belowground. Vegetation is usually classified from aboveground, but it is unclear whether such classifications are suitable to estimate belowground attributes and their consequences, such as rooting depth distribution and its influence on carbon cycling.We performed a meta‐analysis of 55 published arctic rooting depth profiles, testing for differences both between distributions based on aboveground vegetation types (Graminoid, Wetland, Erect‐shrub, and Prostrate‐shrub tundra) and between 'Root Profile Types' for which we defined three representative and contrasting clusters. We further analyzed potential impacts of these different rooting depth distributions on rhizosphere priming‐induced carbon losses from tundra soils.Rooting depth distribution hardly differed between aboveground vegetation types but varied between Root Profile Types. Accordingly, modelled priming‐induced carbon emissions were similar between aboveground vegetation types when they were applied to the entire tundra, but ranged from 7.2 to 17.6 Pg C cumulative emissions until 2100 between individual Root Profile Types.Variations in rooting depth distribution are important for the circumpolar tundra carbon‐climate feedback but can currently not be inferred adequately from aboveground vegetation type classifications. See also the Commentary on this article by Weber & Iversen, 240: 457–460. [ABSTRACT FROM AUTHOR]

The lack of labeled training samples restricts the improvement of Hyperspectral Remote Sensing Image (HRSI) classification accuracy based on deep learning methods. In order to improve the HRSI classification accuracy when there are few training samples, a Lightweight 3D Dense Autoencoder Network (L3DDAN) is proposed. Structurally, the L3DDAN is designed as a stacked autoencoder which consists of an encoder and a decoder. The encoder is a hybrid combination of 3D convolutional operations and 3D dense block for extracting deep features from raw data. The decoder composed of 3D deconvolution operations is designed to reconstruct data. The L3DDAN is trained by unsupervised learning without labeled samples and supervised learning with a small number of labeled samples, successively. The network composed of the fine-tuned encoder and trained classifier is used for classification tasks. The extensive comparative experiments on three benchmark HRSI datasets demonstrate that the proposed framework with fewer trainable parameters can maintain superior performance to the other eight state-of-the-art algorithms when there are only a few training samples. The proposed L3DDAN can be applied to HRSI classification tasks, such as vegetation classification. Future work mainly focuses on training time reduction and applications on more real-world datasets. [ABSTRACT FROM AUTHOR]

Biodiversity Stewardship Programme (BSP) recognises private and communal landowners as stewards of biodiversity within their properties. Participation in this programme by landowners is voluntary. Participating properties may be formally declared under different BSP agreements to support conservation and sustainable resource use. These properties may be in areas that are of significant biodiversity value, high irreplaceability and endangered ecosystems. Reedsdell farm was identified based on its strategic location within the North-Eastern Cape Grasslands Priority areas. These are underrepresented ecosystems in South Africa and categorized as critical biodiversity areas due to their importance as ecological corridors. This study was aimed at classifying and describing the plant communities in Reedsdell farm and to determine their veld condition. The Braun-Blanquet method was followed in the classification of the different plant communities and as a result, two major grassland communities were classified represented by three subcommunities and four variants. A total of 125 different plant species were recorded dominated by 47% of forbs, followed by grasses at 28%, and sedges and shrubs at 11% and 10% respectively. Tress species only represented by 4%. The Shannon Diversity Index results indicate a high diversity of species (H' = 2.280) with complete evenness (EH = 0.987), implying that the area is relatively homogenous. The step-point method was used to collect data that was processed using the Ecological Index Method (EIM). A veld condition score of 48% was calculated, indicating a veld that is in a moderate condition. Increaser II species are dominant (45%) and represented by genus, Eragrostis. These are climax stage grasses associated with a sweetveld occurring in a veld that is generally overutilized and prone to overgrazing. The findings of this study contribute to the understanding of plant communities, veld condition and biodiversity value within the North-Eastern Cape Grassland. This information can inform conservation strategies, land management practices, prioritization of areas for protection and restoration. Ultimately contributing to the long-term conservation of the Grassland Biome in the region. [ABSTRACT FROM AUTHOR]

• Braun-Blanquet procedures and juice software successfully used for data collection and analysis. • Two major communities consisting of three subcommunities and four variants were described. • High species richness and diversity is highlighted by 156 plant species recorded from 59 plots. • The research underscores the role of BSP in effectively managing and conserving threatened ecosystems. • The result of ecological index method indicates a moderate (49%) veld condition. • Valuable findings for comparing with broader vegetation patterns and classification. Privately owned properties declared as protected areas through Biodiversity Stewardship Programme (BSP) increase South Africa's priority biodiversity areas. The North-Eastern Cape grasslands, recognised as high biodiversity areas, are crucial for conserving the Grassland Biome. BSP plays a key role in effectively managing these vital vegetation types, securing and preserving threatened species. This study aimed to describe the vegetation and to determine the condition of Glencoe farm, located within the Eastern Cape Drakensberg Strategic Water Source Area and planning domain for the Eastern Cape Protected Area Expansion Strategy priority areas. The Braun-Blanquet method was employed to classify the plant communities. Two major communities consisting of three subcommunities and four variants were classified. Fifty-nine sample plots were surveyed and 156 plant species dominated by herbaceous species (137), and only 19 woody species were recorded. The area has high species diversity (Shannon Diversity Index: H' = 2.272) that is evenly distributed (E H = 0.987). The Ecological Index Method (EIM) results indicated a moderate veld condition score of 49%. The area is dominated by Increaser II species (52%) followed by Increaser I (16%) species. These findings have broader implications for stakeholders managing privately owned properties designated as protected areas through the BSP in South Africa. The North-Eastern Cape grasslands, recognized for their significant biodiversity, require safeguarding to preserve the Grassland Biome. Effective management of these critical vegetation types is essential for securing and conserving threatened ecosystems, thereby protecting South Africa's priority biodiversity areas. [ABSTRACT FROM AUTHOR]

Motivation: The goal of the Russian Arctic Vegetation Archive (AVA‐RU) is to unite and harmonize data of plot‐based plant species and their abundance, vegetation structure and environmental variables from the Russian Arctic. This database can be used to assess the status of the Russian Arctic vegetation and as a baseline to document biodiversity changes in the future. The archive can be used for scientific studies as well as to inform nature protection and restoration efforts. Main types of variables contained: The archive contains 2873 open‐access geobotanical plots. The data include the full species. Most plots include information on the horizontal (cover per species and morphological group) and vertical (average height per morphological group) structure of vegetation, site and soil descriptions and data quality estimations. In addition to the open‐access data, the AVA‐RU website contains 1912 restricted‐access plots. Spatial location and grain: The plots of 1–100 m2 size were sampled in Arctic Russia and Scandinavia. Plots in Russia covered areas from the West to the East, including the European Russian Arctic (Kola Peninsula, Nenets Autonomous district), Western Siberia (Northern Urals, Yamal, Taza and Gydan peninsulas), Central Siberia (Taymyr peninsula, Bolshevik island), Eastern Siberia (Indigirka basin) and the Far East (Wrangel island). About 72% of the samples are georeferenced. Time period and grain: The data were collected once at each location between 1927 and 2022. Major taxa and level of measurement: Plots include observations of >1770 vascular plant and cryptogam species and subspecies. Software format: CSV files (1 file with species list and abundance, 1 file with environmental variables and vegetation structure) are stored at the AVA‐RU website (https://avarus.space/), and are continuously updated with new datasets. The open‐access data are available on Dryad and all the datasets have a backup on the server of the University of Zurich. The data processing R script is available on Dryad. [ABSTRACT FROM AUTHOR]

Changes in Vegetation Indices (VIs) over time can describe vegetation phenology; however, it is not known which phenological features contribute most to land cover classification. Feature selection could potentially solve this problem. In this study, phenological feature importance and selection were evaluated by using two-year Sentinel-2 (S-2) data and single-date PlanetScope (PS) to classify a 50 km2 native podocarp forest in New Zealand. The study area was classified into nine classes. Single-date PS and S-2 data were fused to a base image with the same spatial resolution as PS and 8 bands containing spectral data from S-2; this image was used to produce 30 Vegetation Indices (VIs). Phenological features – amplitude (AMP) and phase (PH) were extracted from these VIs using time-series S-2 only, and harmonic analysis in Google Earth Engine. For accurately classifying forests and identifying the most important features, three classification scenarios (fused bands & VIs, fused bands & phenological features, fused bands & VIs & phenological features) were developed using a Neural Network. Variable Selection Using Random Forest (VSURF) was applied on these scenarios to evaluate the impact of feature selection. Results indicate that VSURF could reduce the time needed for the classification while maintaining a comparable level of accuracy. Phenological features improved accuracy from 90% to 94%, driven mostly by Red-Edge Triangulated Vegetation Index-AMP&PH, Normalised Near-Infrared-PH, Greenness Index-PH, Water Body Index-PH, Normalised Difference Vegetation Index-PH, Normalised Green-PH, Red-Edge Normalised Difference Vegetation Index-PH, Leaf Chlorophyll Content-AMP, and Simple Near-Infrared and Blue Ratio-PH. These features reflect changes in the structure, biochemical, and physiological characteristics of the canopy. A lack of ground-based measurements precluded an evaluation of the accuracy of these phenological aspects and an explanation of their distinctive contribution to the model. Overall, the findings show that specific phenological features can improve the classification of New Zealand's indigenous podocarp forests. [ABSTRACT FROM AUTHOR]

Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai'i Island using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, the support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied Gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m × 2 m M. polymorpha presence dataset and a 30 m × 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy. [ABSTRACT FROM AUTHOR]

Taking Harbin city as the research object, the paper extracted the vegetation cover based on Landsat TM/ OLI remote sensing images in 2004, 2010, 2015 and 2020, which combined the normalized vegetation index and dimidiate pixel model. Then, we integrated the transfer matrix to analyze the spatial and temporal dynamic changes of vegetation coverage. Based on the results of vegetation coverage classification, four landscape pattern indices namely patch area, number of patches, contagion index, and Shannon's diversity index, were calculated by Fragstats4. 2 software to study the spatial and temporal evolution characteristics of the landscape pattern in Harbin. The results showed that the sum of areas with high vegetation coverage and medium-high vegetation cover accounted for 77. 81%, 72. 54%, 73. 01% and 83. 31% of the total area in 2004—2020, respectively, and the vegetation coverage exhibited an overall increasing trend in Harbin City. The landscape percentage of high vegetation coverage was the highest from 2004 to 2020, followed by medium-high vegetation coverage, and the number of patches exhibited a first increasing and then decreasing trend for the most vegetation coverage region. At the landscape level, the aggregation of patches decreased and the landscape diversity increased slightly. This study explores the dynamic changes of vegetation coverage and landscape patterns in Harbin city, which can provide guidance and reference for the planning design and landscape pattern optimization of the city. [ABSTRACT FROM AUTHOR]

Combining machine learning algorithms with multi-temporal remote sensing data for fine classification of wetland vegetation has received wide attention from researchers. However, wetland vegetation has different physiological characteristics and phenological information in different growth periods, so it is worth exploring how to use different growth period characteristics to achieve fine classification of vegetation communities. To resolve these issues, we developed an ensemble learning model by stacking Random Forest (RF), CatBoost, and XGBoost algorithms for karst wetland vegetation community mapping and evaluated its classification performance using three growth periods of UAV images. We constructed six classification scenarios to quantitatively evaluate the effects of combining multi-growth periods UAV images on identifying vegetation communities in the Huixian Karst Wetland of International Importance. Finally, we clarified the influence and contribution of different feature bands on vegetation communities' classification from local and global perspectives based on the SHAP (Shapley Additive explanations) method. The results indicated that (1) the overall accuracies of the four algorithms ranged from 82.03% to 93.37%, and the classification performance was Stacking > CatBoost > RF > XGBoost in order. (2) The Stacking algorithm significantly improved the classification results of vegetation communities, especially Huakolasa, Reed-Imperate, Linden-Camphora, and Cephalanthus tetrandrus-Paliurus ramosissimus. Stacking had better classification performance and generalization ability than the other three machine learning algorithms. (3) Our study confirmed that the combination of spring, summer, and autumn growth periods of UAV images produced the highest classification accuracy (OA, 93.37%). In three growth periods, summer-based UAVs achieved the highest classification accuracy (OA, 85.94%), followed by spring (OA, 85.32%) and autumn (OA, 84.47%) growth period images. (4) The interpretation of black-box stacking model outputs found that vegetation indexes and texture features provided more significant contributions to classifying karst wetland vegetation communities than the original spectral bands, geometry features, and position features. The vegetation indexes (COM and NGBDI) and texture features (Homogeneity and Standard Deviation) were very sensitive when distinguishing Bermudagrass, Bamboo, and Linden-Camphora. These research findings provide a scientific basis for the protection, restoration, and sustainable development of karst wetlands. [ABSTRACT FROM AUTHOR]

Vegetation mapping from remote sensing data has proven useful for monitoring ecosystems at local, regional and global scales. Generally based on supervised classification methods, ecosystem mapping needs representative and consistent labelling. Such completeness is often difficult to achieve and requires the exclusion of minority species poorly represented in the studied scene in the training base. This exclusion leads to wrong predictions in the resulting map. In this study, the use of a posteriori classification rejection methods to limit the errors associated with minority species was evaluated in three different mapping scenarios: classification according to vegetation layers, prediction of genera from various vegetation types from low vegetation to trees and mapping of habitat (assemblages of species). For this purpose, several supervised classification methods based on Support Vector Machines (SVM), Random Forests (RF) and Regularized Logistic Regression (RLR) algorithms were first applied to hyperspectral images covering the reflective domain. On these classifications, the usual evaluation methods (confusion matrix and its derivatives calculated on an independent test set composed of the majority species) showed performances similar to those of the state-of-the-art. However, the introduction of a new score taking into account minority species demonstrated the need to include them in the evaluation process to provide robust performance quantification representing map effectiveness. Three a posteriori rejection methods, based on simple thresholding, K-means and SVM algorithms, were well suited to monitor minority species. The performance gain depended on the mapping scenario, the machine learning model and the rejection method. An increase in performance with the inclusion of minority species of up to 12% could be observed through the new score. These methods also detected a similar proportion of prediction errors associated with predominant species [ABSTRACT FROM AUTHOR]

The global seasonal change and continued rapid growth have maximized the need to assess the urban dwellers' depend on vegetation for their lives, and also in the urban ecosystem resources. The conventional outcomes devoted to cropland mapping, with the help of high-quality remote sensing data's. This paper is to investigate and develop a new methodology that pertains to time series analysis for classifying the type of vegetation in a farm area of Ujani Dam located in Solapur District, Maharastra. The proposed model develops a novel adaptive time-weighted dynamic time warping (ATWDTW) for the time series analysis using the satellite images. The gathered satellite images from the farm are processed initially and subjected to analysis by ATWDTW. The TWDTW concept is optimally tuned by the new hybrid meta-heuristic algorithm termed moth flame-based bird swarm optimization (MF-BSA) for enhancing the classification performance. Regarding the false omission rate of the proposed MF-BSA-ATWDTW model attains 5.56% and 29.9% lower than SVM and K-means respectively. From the analysis, it is possible to get a deep insight into the vegetation to be done in each year, and the comparative analysis proves that the proposed model is further adaptable for experimental use in relating and explaining environmental and ecological time-series information. [ABSTRACT FROM AUTHOR]

Studies of the Bulgarian vegetation have started a century ago, but for many reasons for a long time they were defined by the dominant approach. Within the last two decades, many syntaxa occurring in the country have been identified in the result of intensive field work and development of national and international databases, as well as after adoption of the standards of the European vegetation classification. The first complete checklist of the Bulgarian syntaxa under the Braun-Blanquet approach was compiled more than a decade ago and has been in need of updating in compliance with the latest national and international scientific achievements. The current paper aims at compiling an updated checklist of the Bulgarian high-rank syntaxa dominated by vascular plants. According to the results, Bulgarian vegetation features 68 classes, 135 orders and 259 alliances. These syntaxa account for 65% of the European classes, 45% of the orders and 23% of the alliances. A number of uncertain and insufficiently researched units illustrate the knowledge gaps and a need for further studies. [ABSTRACT FROM AUTHOR]

Yellow Water-Lily (Nuphar lutea) is a well-known aquatic plant for the flora of Bulgaria. However, presently it has very limited distribution. Many former localities have disappeared during the last century. This paper provides current data on some important populations of the species in Northwest Bulgaria, the municipalities of Byala Slatina and Boychinovtsi, and presents information on its distribution and syntaxonomy in the country. [ABSTRACT FROM AUTHOR]

The expansion of construction zones, transportation, and utilities for industry and high-tech areas due to human activities has caused the deterioration of the natural ecological environment. As cities face problems related to the surface urban heat island (SUHI) effect and environmental pollution, there is an urgent need to develop new methods for the ecological–microclimatic assessment and structural–functional planning of urban areas. The main goal of this study was to demonstrate the evolution of the surface urban heat island (SUHI) effect in Moscow over a long period and to determine the interaction between SUHIs and urban pollution islands (UPIs) using a geospatial analysis platform while optimizing vegetation classification with machine learning. Additionally, we are creating a digital database for modeling the sustainability of cities on the GEE platform using cloud computing. This study used cloud computing and remote sensing image analysis platforms for a 17-year temporal-series ecological–microclimatic assessment, which provided a sequence of values describing the ongoing process of changes in the ecological conditions of Moscow over time. Combining machine learning with the random forest algorithm (RF) improved vegetation classification accuracy while reducing computation time. The study findings demonstrated how the SUHI affected Moscow's territory and showed the urban areas significantly impacted by this phenomenon. The locations of surface urban heat islands in Moscow and areas affected by SUHI and UPI were identified using numerical modeling of the urban thermal field variance index (UTFVI). From the findings, we identified the need to develop a new method for obtaining geospatial data for assessing the interaction between UPIs and SUHIs using cloud computing and mathematical data models. [ABSTRACT FROM AUTHOR]

Arguably the most popular remote-sensing products are classified images. However, there are no definitive procedures to assess classification accuracy that simultaneously consider resources available and field efforts. The explosive usage of unmanned aerial systems (UAS) in land surveys adds new challenges to classification assessment, as orthorectified images usually contain significant artifacts. This study aims to identify the optimal ratio between training and validation sample size within a supervised classification approach applied to UAS orthophotos. As a case study, we used a wetland area west of Portland, OR, USA, treated with various glyphosate formulations to control Phalaris arundinacea, commonly known as reed canary grass. A completely randomized design with five replications and six glyphosate formulations was used to assess P. arundinacea vigor following repeated herbicide applications. The change in P. arundinacea vitality was monitored with high-resolution four-band imagery acquired with a SlantRange 3PX camera installed on a DJI Matrice 210. The orthophotos created from images were produced with Pix4D, which was subsequently preprocessed with ERDAS Imagine 2020 to reduce the noise, shadows, and artifacts. All images were classified with the maximum likelihood classification algorithm. Simple random and stratified random sampling methods were applied to collect training and validation samples, evaluating eight ratios of training to validation samples to assess their classification accuracy. We found that increasing the training-to-validation sample size ratio enhances accuracy, with the 3:1 ratio being the most reliable in classifying P. arundinacea vigor. Our study provides evidence that image preprocessing and enhancement are essential for UAS-based imagery. [ABSTRACT FROM AUTHOR]

Distance/correlation metrics have emerged as a robust and simplified tool for assessing the spectral characteristics of hyperspectral image pixels and effectively categorizing vegetation within a specific study area. Correlation methods provide a readily deployable and computationally efficient approach, rendering them particularly advantageous for applications in developing nations or regions with limited resources. This article presents a comparative investigation of correlation/distance metrics for the identification of vegetation pixels in hyperspectral imagery. The study facilitates a comprehensive evaluation of five distance and/or correlation metrics, namely, direct correlation, cosine similarity, normalized Euclidean distance, Bray–Curtis distance, and Pearson correlation. Direct correlation and Pearson correlation emerged as the two metrics that demonstrated the highest accuracy in vegetation pixel identification. Using the selected methodologies, a vegetation detection algorithm was implemented and validated using a hyperspectral image of the Manga neighborhood in Cartagena de Indias, Colombia. The spectral library facilitated image processing, while the mathematical calculation of correlations was performed using the numpy and scipy libraries in the Python programming language. Both the approach adopted in this study and the implemented algorithm aim to serve as a point of reference for conducting detection studies on diverse material types in hyperspectral imagery using open-access programming platforms. [ABSTRACT FROM AUTHOR]

Studying the variation in vegetation types within the source region of the Yellow River (SRYR) is of great significance for understanding the response of vegetation to climate change and human activities on the Qinghai-Tibet Plateau (QTP) permafrost. In order to understand the characteristics of the variation in vegetation associations in the SRYR under the influence of climate and human activities, two hyperspectral remote sensing images from HJ-1A in 2013 and OHS-3C in 2020 were used to extract the vegetation types located in the area south of Ngoring Lake, covering 437.11 km2 in Maduo County, from the perspective of vegetation associations. Here, the hybrid spectral CNN (HybridSN) model, which is dependent on both spatial and spectral information, was used for vegetation association classifications. On this basis, the variations in vegetation associations from 2013 to 2020 were studied using the transition matrix, and the variation in noxious weeds across different altitude and slope gradients was analyzed. As an example, Thermopsis lanceolata's spatial distribution pattern and diffusion mechanism were analyzed. The results showed that (1) in addition to noxious weeds, herbage such as Poa poophagorum, Stipa purpurea, Kobresia humilis, and Carex moorcroftii increased, indicating that the overall ecological environment tended to improve, which may be attributed mainly to the development of a warm and humid climate. (2) Most of the noxious weeds were located at low altitudes with an area increase in the 4250–4400 m altitude range and a decrease in the 4400–4500 m altitude range. More attention should be given to the fact that the noxious weeds area increased from 2.88 km2 to 9.02 km2 between 2013 and 2020, which was much faster than that of herbage and may threaten local livestock development. (3) The Thermopsis lanceolate association characterized by an aggregated distribution tended to spread along roads, herdsmen sites, and degraded swamps, which were mainly affected by human activities and swamp degradation. [ABSTRACT FROM AUTHOR]

Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development. [ABSTRACT FROM AUTHOR]

One of the ideas behind vegetation monitoring is the ability to identify different vegetation units, such as species, communities, habitats, or vegetation types. Remote sensing data allow for obtaining such information remotely, which is especially valuable in areas that are difficult to explore (such as mountains or wetlands). At the same time, such techniques allow for limiting field research, which is particularly important in this context. Remote sensing has been utilized for vegetation inventories for many decades, using airborne and spaceborne platforms. Developing newer tools, algorithms and sensors is conducive to more new applications in the vegetation identification field. The Special Issue "Remote Sensing Applications in Vegetation Classification" is an overview of the applications of remote sensing data with different resolutions for the identification of vegetation at different levels of detail. In 14 research papers, the most frequent different types of crops were analysed. In three cases, the authors recognised different types of grasslands, whereas trees were the object of the studies in two papers. The most commonly used sensors were Copernicus Sentinel-1 and Sentinel-2; however, to a lesser extent, MODIS, airborne hyperspectral and multispectral data, as well as LiDAR products, were also utilised. There were articles that tested and compared different combinations of datasets, different terms of data acquisition, or different classifiers in order to achieve the highest classification accuracy. These accuracies were assessed quite satisfactorily in each publication; the overall accuracy (OA) for the best result varied from 72% to 98%. In all of the research papers, at least one of the two commonly used machine learning algorithms, random forest (RF) and support vector machines (SVM), was applied. Additionally, one paper presented software ARTMO's machine-learning classification algorithms toolbox, which allows for the testing of 13 different classifiers. The studies published in this Special Issue can be used by the vegetation research teams and practitioners to conduct deeper analysis via the utilization of the proposed solutions. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing technology has been widely used in vegetation type mapping, and the integrated use of spectral and texture features of remote sensing data has become an effective way to improve the accuracy of vegetation classification, but the Hughes effect generated by the high-dimensional feature space composed of multi-scale texture features and hyperspectral features reduced the image classification accuracy, and the randomness of feature optimization algorithms such as genetic algorithm (GA) can also cause uncertainty in image classification accuracy. In order to solve those problems, a high-dimensional spectral-texture feature space was established by incorporating spectral features of ZY1 02D Advanced HyperSpectral Imager(AHSI) hyperspectral data and multi-scale texture features extracted from ZY1 02D Visible and Near Infrared Camera(VNIC) data at various window sizes and movement directions, covering the vicinity of Zongjia Town in Dulan County, Qinghai Province, China. Jeffries-Matusita(J-M) distance is employed to assess the sample separability between vegetation types of texture features derived from varying window sizes and movement directions. Based on a combination of feature optimization algorithms (GA algorithm, particle swarm optimization (PSO), generalized normal distribution optimization (GNDO), atom search algorithm (ASO) and marine predators algorithm (MPA)) and classification algorithm (random forest (RF)), GA-RF, PSO-RF, GNDO-RF, ASO-RF, and MPA-RF algorithms were proposed and applied to the vegetation type classification of the high-dimensional spectral-texture feature space. The results show that the texture features extracted from different window sizes and window movement directions showed maximum J-M distance between distinct vegetations. For instance, the J-M distance between wolfberry (old) and wolfberry (new) is highest in the 3×3 window size texture images, while the J-M distance between wolfberry (old) and haloxylon is highest in the 11×11 window size texture images. In the 0° window movement direction texture images, poplar and haloxylon had the best separability to other vegetation types with J-M distances greater than 1.87 and 1.86, respectively. Grass and other vegetation types achieved the best separability with J-M distances greater than 1.18 in the 90° window movement direction texture images. The inclusion of multiscale texture features improved the overall classification accuracy (OA) by 8.02 percentage. The proposed GA-RF, PSO-RF, GNDO-RF, ASO-RF, and MPA-RF algorithms, when compared to the conventional random forest algorithm, led to an improvement in OA of vegetation classification by 1.32 to 2.41 percentage. Among them, the MPA-RF method exhibited the highest accuracy, achieving an OA and Kappa coefficient of 88.92% and 0.86, respectively. The results of this study demonstrate that texture images extracted from different window sizes and window movement directions are useful in distinguishing between different types of vegetation. The accuracy of vegetation recognition is significantly increased by adding multi-scale texture features to the spectral feature set. The combination of feature optimization algorithm and image classification algorithm by iterative optimization alleviates the randomness of the optimization algorithm results, overcomes the Hughes effect of high-dimensional features, and successfully improves the vegetation classification accuracy. This study proposes innovative ideas for feature extraction, feature optimization, and classification algorithm selection for hyperspectral vegetation classification studies. [ABSTRACT FROM AUTHOR]