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Background: Lentil (Lens culinaris Medik.) is a globally-significant agricultural crop used to feed millions of people. Lentils have been cultivated in the Australian states of Victoria and South Australia for several decades, but efforts are now being made to expand their cultivation into Western Australia and New South Wales. Plant architecture plays a pivotal role in adaptation, leading to improved and stable yields especially in new expansion regions. Image-based high-throughput phenomics technologies provide opportunities for an improved understanding of plant development, architecture, and trait genetics. This paper describes a novel method for mapping and quantifying individual branch structures on immature glasshouse-grown lentil plants grown using a LemnaTec Scanalyser 3D high-throughput phenomics platform, which collected side-view RGB images at regular intervals under controlled photographic conditions throughout the experiment. A queue and distance-based algorithm that analysed morphological skeletons generated from images of lentil plants was developed in Python. This code was incorporated into an image analysis pipeline using open-source software (PlantCV) to measure the number, angle, and length of individual branches on lentil plants. Results: Branching structures could be accurately identified and quantified in immature plants, which is sufficient for calculating early vigour traits, however the accuracy declined as the plants matured. Absolute accuracy for branch counts was 77.9% for plants at 22 days after sowing (DAS), 57.9% at 29 DAS and 51.9% at 36 DAS. Allowing for an error of ± 1 branch, the associated accuracies for the same time periods were 97.6%, 90.8% and 79.2% respectively. Occlusion in more mature plants made the mapping of branches less accurate, but the information collected could still be useful for trait estimation. For branch length calculations, the amount of variance explained by linear mixed-effects models was 82% for geodesic length and 87% for Euclidean branch lengths. Within these models, both the mean geodesic and Euclidean distance measurements of branches were found to be significantly affected by genotype, DAS and their interaction. Two informative metrices were derived from the calculations of branch angle; 'splay' is a measure of how far a branch angle deviates from being fully upright whilst 'angle-difference' is the difference between the smallest and largest recorded branch angle on each plant. The amount of variance explained by linear mixed-effects models was 38% for splay and 50% for angle difference. These lower R2 values are likely due to the inherent difficulties in measuring these parameters, nevertheless both splay and angle difference were found to be significantly affected by cultivar, DAS and their interaction. When 276 diverse lentil genotypes with varying degrees of salt tolerance were grown in a glasshouse-based experiment where a portion were subjected to a salt treatment, the branching algorithm was able to distinguish between salt-treated and untreated lentil lines based on differences in branch counts. Likewise, the mean geodesic and Euclidean distance measurements of branches were both found to be significantly affected by cultivar, DAS and salt treatment. The amount of variance explained by the linear mixed-effects models was 57.8% for geodesic branch length and 46.5% for Euclidean branch length. Conclusion: The methodology enabled the accurate quantification of the number, angle, and length of individual branches on glasshouse-grown lentil plants. This methodology could be applied to other dicotyledonous species. [ABSTRACT FROM AUTHOR]

There is an increasing need for ecosystem‐level distribution models (EDMs) and a better understanding of which factors affect their quality. We investigated how the performance and transferability of EDMs are influenced by 1) the choice of predictors and 2) model complexity. We modelled the distribution of 15 pre‐classified ecosystem types in Norway using 252 predictors gridded to 100 × 100 m resolution. The ecosystem types are major types in the ‘Nature in Norway' system mainly defined by rule‐based criteria such as whether soil or specific functional groups (e.g. trees) are present. The predictors were categorised into four groups, of which three represented proxies for natural, anthropogenic, or terrain processes (‘ecological predictors') and one represented spectral and structural characteristics of the surface observable from above (‘surface predictors'). Models were generated for five levels of model complexity. Model performance and transferability were evaluated with data collected independently of the training data. We found that 1) models trained with surface predictors only performed considerably better and were more transferable than models trained with ecological predictors, and 2) model performance increased with model complexity, levelling off from approximately 10 parameters and reaching a peak at approximately 20 parameters, while model transferability decreased with model complexity. Our findings suggest that surface predictors enhance EDM performance and transferability, most likely because they represent discernible surface characteristics of the ecosystem types. A poor match between the rule‐based criteria that define the ecosystem types and the ecological predictors, which represent ecological processes, is a plausible explanation for why surface predictors better predict the distribution of ecosystem types. Our results indicate that, in most cases, the same models are not well suited for contrasting purposes, such as predicting where ecosystems are and explaining why they are there. [ABSTRACT FROM AUTHOR]

Residues mapping can provide essential information about soil chemical elements' behaviors and contribute to possible interferences in coffee tree development. Thus, the research objective was to monitor plant residue burning effects by analyzing the chemical elements in ash, using soil analysis, and applying vegetative indices obtained by RPA images. The samples were submitted for conventional soil analysis and atomic emission spectrometry (pure ash). The RPA multispectral images were used to form thirty-one vegetative indices. Thus, at the soil and ash collection points, the index performance was evaluated for six months and divided into three collection times. Then, the data were statistically analyzed to evaluate which index best separated the plants in regions with ash and ash-free soil. The pure ash deposits revealed expressive presences of K, Ca, Mg, and Al in addition to pH elevation. In areas with ash, the high temperature at the burning time may have caused elemental chemical transformations in the Al composition, making this element unavailable in soil analysis. The vegetative indices showed a significant difference only in coffee four months after planting. Among the thirty-one evaluated indices, only twenty were satisfactory for ash analysis. The burning of plant residues promoted the neutralization of Al. In addition, ash deposits in the soil added some essential elements for plant development. Negatively, they raised the PH and made micronutrients unavailable. The best vegetative indices for ash monitoring were the Normalized Near Infrared Index (NNIRI) and Normalized Green Index (NGI). Prior ash mapping can contribute to localized application in macro, such as K and limestone, reusing the number of elements already deposited by burning vegetables. [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]

The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species. [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]

Information on boll distribution within a cotton plant is critical to evaluate the adaptation and response of cotton plants to environmental and biotic stress in cotton production. Cotton researchers have applied available conventional fiber measurements, such as the high volume instrument (HVI) and advanced fiber information system (AFIS), to map the location and the timing of boll development and distribution within plants and further to determine within-plant variability of cotton fiber properties. Both HVI and AFIS require numerous cotton bolls combined for the measurement. As an alternative approach, attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy was proposed to measure fiber maturity (MIR) and crystallinity (CIIR) of a sample as little as 0.5 mg lint. Extending fiber maturity and crystallinity measurement into a single boll for node-by-node mapping, FT-IR method might be advantageous due to less sampling amount compared with HVI and AFIS methods. Results showed that FT-IR technique enabled the evaluation of fiber MIR and CIIR at a boll level, which resulted in average MIR and CIIR values highly correlated with HVI micronaire (MIC) and AFIS maturity ratio (M). Hence, FT-IR technique possesses a good potential for a rapid and non-destructive node-by-node mapping of cotton boll maturity and crystallinity distribution. [ABSTRACT FROM AUTHOR]

This perspective addresses the nature of the past and current relationships between the ecological and biogeographical regionalization of plants and plant communities. It uses four examples (two related to continental scale and two related to regional/local scale) to document the cases of cross‐pollination between both approaches in the past and the need for concerted use of both ecological and distributional data in formulating robust regional spatial classification systems of biotic assemblages. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

There is an urgent need for novel protection strategies to sustainably secure crop production under changing climates. Studying microbial effectors, defined as microbe‐derived proteins that alter signalling inside plant cells, has advanced our understanding of plant immunity and microbial plant colonisation strategies. Our understanding of effectors in the establishment and beneficial outcome of plant symbioses is less well known. Combining functional and comparative interaction assays uncovered specific symbiont effector targets in highly interconnected plant signalling networks and revealed the potential of effectors in beneficially modulating plant traits. The diverse functionality of symbiont effectors differs from the paradigmatic immuno‐suppressive function of pathogen effectors. These effectors provide solutions for improving crop resilience against climate stress by their evolution‐driven specification in host protein targeting and modulation. Symbiont effectors represent stringent tools not only to identify genetic targets for crop breeding, but to serve as applicable agents in crop management strategies under changing environments. [ABSTRACT FROM AUTHOR]

Trait-based approaches are of increasing concern in predicting vegetation changes and linking ecosystem structures to functions at large scales. However, a critical challenge for such approaches is acquiring spatially continuous plant functional trait maps. Here, six key plant functional traits were selected as they can reflect plant resource acquisition strategies and ecosystem functions, including specific leaf area (SLA), leaf dry matter content (LDMC), leaf N concentration (LNC), leaf P concentration (LPC), leaf area (LA) and wood density (WD). A total of 34 589 in situ trait measurements of 3447 seed plant species were collected from 1430 sampling sites in China and were used to generate spatial plant functional trait maps (∼1 km), together with environmental variables and vegetation indices based on two machine learning models (random forest and boosted regression trees). To obtain the optimal estimates, a weighted average algorithm was further applied to merge the predictions of the two models to derive the final spatial plant functional trait maps. The models showed good accuracy in estimating WD, LPC and SLA, with average R2 values ranging from 0.48 to 0.68. In contrast, both the models had weak performance in estimating LDMC, with average R2 values less than 0.30. Meanwhile, LA showed considerable differences between the two models in some regions. Climatic effects were more important than those of edaphic factors in predicting the spatial distributions of plant functional traits. Estimates of plant functional traits in northeastern China and the Qinghai–Tibetan Plateau had relatively high uncertainties due to sparse samplings, implying a need for more observations in these regions in the future. Our spatial trait maps could provide critical support for trait-based vegetation models and allow exploration of the relationships between vegetation characteristics and ecosystem functions at large scales. The six plant functional trait maps for China with 1 km spatial resolution are now available at 10.6084/m9.figshare.22351498 (An et al., 2023). [ABSTRACT FROM AUTHOR]

Soil compaction is considered one of the main threats to structural soil degradation, and it promotes increased densification of soil particles, impairs ecosystem services, the plant development, and therefore affects agricultural profitability. In this sense, this study aimed to analyze the feasibility of using a Remotely Piloted Aircraft System (RPAS) by relating parameters derived from aerial images based on Vegetation Indices (VIs) and the Canopy Height Model (CHM) with soil compaction in a coffee plantation area. The study was conducted in a commercial coffee plantation with the cultivar Mundo Novo with 14 years of implantation. Two aerial surveys were carried out, the first to determine the CHM and define the sampling points and the second for radiometric calculations of VIs. In the sampling point were collected data plant height, soil characterization, soil penetration resistance and productivity. Images were processed by Pix4D software, and the data analysis at QGIS and RStudio. As at results, no statistically significant differences were detected between the different plant height zones in the soil chemical analysis; significant statistical differences between plant height zones were detected for penetration resistance, which is correlated to productivity data; and the radiometric data presented a correlation with the penetration resistance data, making it possible to determine VIs (NDRE and MTCI) with correlation to the compaction data allowing the estimation of such variable. In this way, the possibility of monitoring the height variations of the coffee crop using RPAS to demarcate compacted zones was evidenced. [ABSTRACT FROM AUTHOR]

Hailstorms pose a direct threat to agriculture, often causing yield losses and worsening farmers' agricultural activity. Traditional methods of hail damage estimation, conducted by insurance field inspectors, have been questioned due to their complexity, partial subjectivity, and lack of accounting for spatial variability. Therefore, remote sensing integration in the estimation process could provide a valuable aid. The focus of this study was on winter wheat (Triticum aestivum L.) and its response to damage in the near-infrared (NIR) spectral region, with a particular emphasis on the study of brown pigments as a proxy for yield damage estimation and mapping. An experiment was conducted during two cropping seasons (2020–2021 and 2021–2022) at two sites, simulating hail damage at critical flowering and milky stages using a specifically designed prototype machinery with low, medium, and high damage gradients compared to undamaged conditions in plots with a minimum of 400 m2 area. After the damage simulation, hyperspectral visible-NIR reflectance was measured with Unmanned Aerial Vehicle (UAV) flights, and measurements of chlorophyll and of leaf area index (LAI) were contextually taken. Final yield per treatment was recorded using a combine. An increase in absorbance in the NIR region (780–950 nm) was observed and evaluated using a spectral mixture analysis (SMA) after selecting representative damaged and undamaged vegetation spectra to map the damage. The abundance of damaged endmember pixels per treatment resulted in a good relationship with the final yield (R2 = 0.73), identifying the most damaged areas. The absorbance feature was further analysed with a newly designed multispectral index (TAI), which was tested against a selection of indices and resulted in the highest relationship with the final yield (R2 = 0.64). Both approaches were effective in highlighting the absorbance feature over different dates and development stages, defining an effective mean for hailstorm damage mapping in winter wheat. [ABSTRACT FROM AUTHOR]

Crepis guioliana (Asteraceae) had previously been considered a serpentine endemic restricted to a few localities in NW Greece. It is now reported for the first time as a Balkan endemic from the serpentine massif of Lenie-Valamara in SE Albania. The total distribution of the species is mapped and the Albanian plants illustrated by several photographs. Several serpentine species from the Lenie-Valamara massif with southern extensions to NW Greece are listed. [ABSTRACT FROM AUTHOR]

SUMMARY: The cytosol‐facing outer membrane (OM) of organelles communicates with other cellular compartments to exchange proteins, metabolites, and signaling molecules. Cellular surveillance systems also target OM‐resident proteins to control organellar homeostasis and ensure cell survival under stress. However, the OM proximity proteomes have never been mapped in plant cells since using traditional approaches to discover OM proteins and identify their dynamically interacting partners remains challenging. In this study, we developed an OM proximity labeling (OMPL) system using biotin ligase‐mediated proximity biotinylation to identify the proximity proteins of the OMs of mitochondria, chloroplasts, and peroxisomes in living Arabidopsis (Arabidopsis thaliana) cells. Using this approach, we mapped the OM proximity proteome of these three organelles under normal conditions and examined the effects of the ultraviolet‐B (UV‐B) or high light (HL) stress on the abundances of OM proximity proteins. We demonstrate the power of this system with the discovery of cytosolic factors and OM receptor candidates potentially involved in local protein translation and translocation. The candidate proteins that are involved in mitochondrion–peroxisome, mitochondrion–chloroplast, or peroxisome–chloroplast contacts, and in the organellar quality control system are also proposed based on OMPL analysis. OMPL‐generated OM proximity proteomes are valuable sources of candidates for functional validation and suggest directions for further investigation of important questions in cell biology. [ABSTRACT FROM AUTHOR]

Chlorophyll plays a crucial role in the process of photosynthesis and helps to regulate plants' growth and development. Timely and accurate evaluation of leaf chlorophyll content provides valuable information about the health and productivity of plants as well as the effectiveness of agricultural treatments. For non-contact and high-performance chlorophyll content mapping in plants, spectral imaging techniques are the most widely used. Due to agility and rapid random-spectral-access tuning, acousto-optical imagers seem to be very attractive for the detection of vegetation indices and chlorophyll content assessment. This laboratory study demonstrates the capabilities of an acousto-optic imager for evaluation of leaf chlorophyll content in six crops with different biophysical properties: Ribes rubrum, Betula populifolia, Hibiscus rosa-sinensis, Prunus padus, Hordeum vulgare and Triticum aestivum. The experimental protocol includes plant collecting, reference spectrophotometric measurements, hyperspectral imaging data acquisition, processing and analysis and building a multi-crop chlorophyll model. For 90 inspected samples of plant leaves, the optimal vegetation index and model were found. Obtained values of chlorophyll concentrations correlate well with reference values (determination coefficient of 0.89 and relative error of 15%). Applying a multi-crop model to each pixel, we calculated chlorophyll content maps across all plant samples. The results of this study demonstrate that acousto-optic imagery is very promising for fast chlorophyll content assessment and other laboratory spectral-index-based measurements. [ABSTRACT FROM AUTHOR]

Monitoring salt marshes with remote sensing is necessary to evaluate their state and restoration. Determining appropriate techniques for this can be overwhelming. Our study provides insight into whether a pixel- or object-based Random Forest classification approach is best for mapping vegetation in north temperate salt marshes. We used input variables from drone images (raw reflectances, vegetation indices, and textural features) acquired in June, July, and August 2021 of a salt marsh restoration and reference site in Aulac, New Brunswick, Canada. We also investigated the importance of input variables and whether using landcover classes representing areas of change was a practical way to evaluate variation in the monthly images. Our results indicated that (1) the classifiers achieved overall validation accuracies of 91.1–95.2%; (2) pixel-based classifiers outperformed object-based classifiers by 1.3–2.0%; (3) input variables extracted from the August images were more important than those extracted from the June and July images; (4) certain raw reflectances, vegetation indices, and textural features were among the most important variables; and (5) classes that changed temporally were mapped with user's and producer's validation accuracies of 86.7–100.0%. Knowledge gained during this study will inform assessments of salt marsh restoration trajectories spanning multiple years. [ABSTRACT FROM AUTHOR]

The task of accurately mapping species-specific vegetation cover in remote and topographically complex regions like those found in Hawaiʻi presents unique challenges. This study leverages a machine learning approach to accurately classify vegetation into fine species-specific classes across the island of Lāna'i, Hawaii, offering a novel methodology for tackling such challenges. Utilizing high-resolution WordView-2 satellite imagery, a neural network classifier and a custom lidar-based geometric correction, we introduced two new approaches to refine our high-resolution land cover classifications. This included the implementation of prior-based adjustments to class posterior probabilities to enhance land cover classification accuracy. Moreover, we developed mixed hierarchical classification maps that use class posterior probabilities to identify, at the pixel level, the finest land cover class that meets a user-defined confidence threshold. The resulting high-resolution land cover map for Lāna'i captures the rich diversity and distribution of native and invasive plant species with high overall accuracy, generally exceeding 95%, based on independent ground control data. The capacity to produce wall-to-wall species-level vegetation maps provides a new window into monitoring vegetation dynamics on Lāna'i and similarly remote and topographically complex regions, and contributes to our broader understanding of ecosystem responses to invasive species, climatic changes, and land management practices such as erosion and sediment control planning. Our approach offers a blueprint for similar efforts in other complex and remote ecosystems. [ABSTRACT FROM AUTHOR]

Question: Remote sensing is a fundamental tool to monitor biodiversity and spectral diversity may represent a proxy for different biodiversity facets such as taxonomic (TD) and functional diversity (FD). We used fine‐resolution multispectral imagery to explore spectral diversity (SD) patterns across spatial scales (i.e., plot, transect, area), and assess SD relationships with TD and FD along an environmental gradient. Location: Coastal sand dune, Viareggio, Italy ("Migliarino–San Rossore–Massaciuccoli" Regional Park, 43°83′ N, 10°25′ E). Methods: We measured TD as species richness, while SD and FD were computed using probability density functions based on pixel and species position in multivariate spaces. We compared SD and FD patterns in space occupation, and we tested whether these patterns are coordinated along the sea–inland gradient. We also assessed univariate relationships between SD and biodiversity facets, and we tested how these facets were partitioned across scales. Results: We found a strong correspondence between functional and spectral patterns in space occupation and along the environmental gradient, with a lack of significance when considering TD. However, TD and SD showed higher variation at broader scale while most FD variation occurred at plot level. Conclusion: By measuring FD and SD with a common methodological framework, we demonstrate that SD approximates functional patterns in plant communities. Moreover, we show that SD retrieved using high‐resolution images can capture different aspects of FD, and that the occupation of the spectral space is analogous to the occupation of the functional space. [ABSTRACT FROM AUTHOR]

[Display omitted] Efficient and timely mapping and monitoring of marine vegetation are becoming increasingly important as coastal habits have experienced significant declines in spatial extent due to climate change. Recent advances in machine learning and cloud computing, such as Google Earth Engine, have demonstrated that online analysis platforms make global-scale habitat mapping and monitoring possible. However, the mapping and monitoring of marine ecosystems with remote sensing is challenging, and we lack reliable, generalisable and scalable indices such as NDVI to assess spatiotemporal change in marine habitats. Here, we present a novel method for mapping coastal marine habitats using a kernelised aquatic vegetation index (k NDAVI) with spatially balanced in-water ground truthing and compare it to existing indices and mapping approaches. The kernelised vegetation index provides a simple, consistent, scalable and accurate method for mapping shallow marine vegetation across ∼ 400 km of coastline along mid-west Australia (31.58°S − 29.56°S). This region has significant coastal macroalgae cover that provides critical recruitment habitat for many invertebrates, including commercially valuable fisheries, and has experienced significant loss due to climate-induced heatwaves. We extensively validate k NDAVI and satellite-derived covariates for their utility in mapping SAV using three approaches 1] cross-validation, 2] block cross-validation and 3] site validation. Habitat models that included the kernelised vegetation index achieved excellent agreement (Accuracy > 0.90 and Cohen's kappa > 0.80) for classifying submerged vegetation. We demonstrate that the k NDAVI index has considerable potential for large-scale vegetation monitoring and provides an applicable metric to map spatiotemporal dynamics and more effectively manage these changing coastal habitats. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This review provides an overview of the progress made in remote sensing application for soil moisture, vegetation and inundation mapping in wetland environments. The main objective of the paper was to assess the link between soil moisture variations and vegetation characteristics in wetland remote sensing studies. To achieve this objective, relevant literature was gathered from established search engines, e.g. Science Direct and Web of Science, along with specific search strategies and key phrases. Three hundred and ninety-three journal articles on wetland remote sensing published between 1980 and 2023 were collected and subjected to a comprehensive analysis. The findings indicate that remote sensing of wetland moisture, vegetation and inundation has been increasing, from three published work in the 1980s to 22 in the 1990s, to 88 between 2001 and 2010, and to 278 between 2011 and 2023. Results showed that there has been an improvement in the application of remote sensing in mapping of wetland moisture, inundation and vegetation in Africa between 2015 and 2023. Despite wide application of remote sensing to map these aspects, very few studies (2.1%) have focused on establishing the relationship between them. The analysis indicated that the launch of new Sentinel-1 radar and Sentinel-2 optical sensors in addition to the Landsat series, along with a variety of analytical methods, provided a great opportunity for derivation of soil moisture and vegetation data which can be used to establish the soil moisture–vegetation nexus. [ABSTRACT FROM AUTHOR]

A proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (Solidago spp.), characterized by aggressive growth that results in habitat disruption as it outcompetes native plants. This invasiveness also leads to altered soil composition through the release of allelopathic chemicals, complicating control efforts and making it challenging to maintain ecological balance in affected areas. The research goal was to develop methods that allow the analysis of changes in heterogeneous habitats with high accuracy and repeatability. For this reason, we used open source classifiers Support Vector Machine (SVM), Random Forest (RF), and satellite images of Sentinel-2 (free) and PlanetScope (commercial) to assess their potential in goldenrod classification. Due to the fact that invasions begin with invasion footholds, created by small patches of invasive, autochthonous plants and different land cover patterns (asphalt, concrete, buildings) forming heterogeneous areas, we based our studies on field-verified polygons, which allowed the selection of randomized pixels for the training and validation of iterative classifications. The results confirmed that the optimal solution is the use of multitemporal Sentinel-2 images and the RF classifier, as this combination gave F1-score accuracy of 0.92–0.95 for polygons dominated by goldenrod and 0.85–0.89 for heterogeneous areas where goldenrod was in the minority (mix class; smaller share of goldenrod in canopy than autochthonous plants). The mean decrease in the accuracy analysis (MDA), indicating an informativeness of individual spectral bands, showed that Sentinel-2 bands coastal aerosol, NIR, green, SWIR, and red were comparably important, while in the case of PlanetScope data, the NIR and red were definitely the most important, and remaining bands were less informative, and yellow (B5) did not contribute significant information even during the flowering period, when the plant was covered with intensely yellow perianth, and red-edge, coastal aerosol, or green II were much more important. The maximum RF classification values of Sentinel-2 and PlanetScope images for goldenrod are similar (F1-score > 0.9), but the medians are lower for PlanetScope data, especially with the SVM algorithm. [ABSTRACT FROM AUTHOR]

Invasive alien species are among the most pervasive threats to biodiversity. Invasive species can cause catastrophic reductions in populations of native and endemic species and the collapse of ecosystem function. A second major global conservation concern is the extirpation of large‐bodied mobile animals, including long‐distance migrants, which often have keystone ecological roles over extensive spatial extents. Here, we report on a potentially catastrophic synergy between these phenomena that threatens the endemic biota of the Galapagos Archipelago. We used GPS telemetry to track 140 migratory journeys by 25 Western Santa Cruz Island Galapagos tortoises. We plotted the spatial interaction between tortoise migrations and recently established non‐native forest dominated by the invasive tree Cedrela odorata (Cedrela forest). We qualified (a) the proportion of migratory journeys that traversed Cedrela forest, and (b) the probability that this observed pattern occurred by chance. Tortoise migrations were overwhelmingly restricted to small corridors between Cedrela forest blocks, indicating clear avoidance of those blocks. Just eight of 140 migrations traversed extensive Cedrela stands. Tortoises avoid Cedrela forest during their migrations. Further expansion of Cedrela forest threatens long‐distance migration and population viability of critically endangered Galapagos tortoises. Applied research to determine effective management solutions to mitigate Cedrela invasion is a high priority. [ABSTRACT FROM AUTHOR]

This paper primarily focuses on identifying and mapping vegetation stress caused by the impact of coal mining using airborne hyperspectral image (AVIRIS-NG) at a fine scale level and is validated using spectroscopic field data. In this work, we have calculated and tested vegetation stress-affected narrow banded vegetation indices (VIs) based on Separability (S) and Coefficient of discrimination (R2) statistical tests for the identification of suitable vegetation stress indices. We have considered the highest S and R2 values of stress indices for weighted and vegetation combined pixels' analysis. The final weighted combination index has been used for vegetation stress detection and mapping in coal mining sites. The outcome has been validated using field-based healthy and stress-affected plant spectral data and compared to ENVI software's agriculture stress tool (AST) based vegetation stress result. Some indices exhibited higher performance for vegetation stress analysis (NDVI, NSI-2, SR-1, SWST-3, SRWI, NWI-2, CSVI, HMSSI, and ARI-1), according to statistical results (S and R2) of VIs. Based on the vegetation stress index, high-stress zones are located in and around the mines whilelow-stress regions are positioned farther from the mines. The findings additionally demonstrated that the VIs-based stress result (AUC-0.69) fits the data more accurately than the ENVI's AST result (AUC-0.61). The VI's-based stress index had a clearly negative correlation with the distance of vegetation from the mines, which could be verified using the on-field NIR spectral measurements. Therefore, effective vegetation health monitoring and planning in coal mining sites might benefit from these results. [ABSTRACT FROM AUTHOR]

Excessive nitrogen (N) fertilization poses environmental risks at regional and global levels. Satellite remote sensing provides a novel approach for large-scale N monitoring. In this study, we evaluated the performance of different types of spectral bands and indices (SIs) coupled with ensemble learning models (ELMs) at retrieving the plant N concentration (PNC) and plant aboveground biomass (AGB) of potato from Sentinel-2 images. Cloud-free Sentinel-2 imagery was acquired during the tuber-formation to starch-accumulation stages from 2020 to 2021. Fourteen optimal SIs were selected using the successive projections algorithm (SPA) and principal component analysis (PCA). The PNC and AGB estimation models were then built using an ELMs. The results showed that the SIs based on chlorophyll absorption bands were strongly related to potato PNC and AGB. Also, the N-correlated bands were mainly concentrated in the red-edge (705 nm) and short-wave infrared (1610 and 2190 nm) regions. The ELMs successfully predicted PNC and AGB (R2PNC = 0.74; R2AGB = 0.82). Compared with the other five base models (k-nearest neighbor (KNN), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR)), the ELMs provided higher PNC and AGB estimation accuracy and effectively reduced overfitting to training data. This study demonstrated that the promising solution of using SPA-PCA coupled with an ensemble learning model improves the estimation accuracy of potato PNC and AGB based on Sentinel-2 imagery data. [ABSTRACT FROM AUTHOR]

Erosion is the main problem that affects soil health related to agricultural activities, therefore this study aims to determine the level of erosion hazard in the Jenelata Sub Watershed. RUSLE is used to calculate erosion prediction using rainfall erosion information, soil erodibility value, topographic value and using maps for vegetation and conservation practices, so that erosion values are obtained for Buakkang, Bissoloro, Bontomanai, Jenebatu, Sapaya Village, Paranglompoa, Pattalikang, Tassese, Mangempeng, Paladigan, and Ronaloe. Each was divided into very low classes with land loss of less than 15 t/ha/year with a land area of 7812.38 ha. The low class was land loss of 15 to 60 t/ha/year with a land area of 3263.04 ha. The medium class was land loss of 60 to 180 t/ha/year with a land area of 694.76 ha. The high class was land loss of 180 to 480 t/ha/year with a land area of 3234.03 ha, and the very high class was land loss that is greater than 480 t/ha/year with a land area of 5272.67 ha. This study showed high and very high erosion with a land area of 3234.03 ha and 5272.67 ha and very low erosion with a land area of 7812.38 ha. [ABSTRACT FROM AUTHOR]

Droughts have been implicated as the main driver behind recent vegetation die‐off and are projected to drive greater mortality under future climate change. Understanding the coupling relationship between vegetation and drought has been of great global interest. Currently, the coupling relationship between vegetation and drought is mainly evaluated by correlation coefficients or regression slopes. However, the optimal drought timescale of vegetation response to drought, as a key indicator reflecting vegetation sensitivity to drought, has largely been ignored. Here, we apply the optimal drought timescale identification method to examine the change in coupling between vegetation and drought over the past three decades (1982–2015) with long‐term satellite‐derived Normalized Difference Vegetation Index and Standardized Precipitation‐Evapotranspiration Index data. We find substantial increasing response of vegetation to drought timescales globally, and the correlation coefficient between vegetation and drought under optimal drought timescale overall declines between 1982 and 2015. This decrease in vegetation–drought coupling is mainly observed in regions with water deficit, although its initial correlation is relatively high. However, vegetation in water‐surplus regions, with low coupling in earlier stages, is prone to show an increasing trend. The observed changes may be driven by the increasing trend of atmospheric CO2. Our findings highlight more pressing drought risk in water‐surplus regions than in water‐deficit regions, which advances our understanding of the long‐term vegetation–drought relationship and provides essential insights for mapping future vegetation sensitivity to drought under changing climate conditions. [ABSTRACT FROM AUTHOR]

The correlation between transcription levels (abundance of individual mRNAs) and translation (level of corresponding proteins in the proteome) is modest. This confirms that gene expression regulation at the translation stage is crucial. Given the significance of investigations into translationally active mRNA as an indicator of translation effectiveness, researchers require reliable and structured functional sources of information on translation, such as plant translatomic maps that resembling transcription maps. The establishment of a standardized experimental and computational approach is required to generate translatomic maps. In this article, we explore possibilities for researchers to generate translatomic maps for plants. We present a comprehensive overview of experimental methods used to obtain translationally active mRNA fractions, taking into account their advantages and limitations. In addition, we discuss methodological approaches that enable obtaining a complete mRNA pool, sequencing methods for qualitative and quantitative evaluation of various mRNA fractions, as well as bioinformatic methods for multivariate analysis and indicators for assessing mRNA translational activity. This manuscript calls for the scientific community to develop translatomic maps of plant objects as a means to increase the utilization of translatomic data in both fundamental plant biology and practical applications. [ABSTRACT FROM AUTHOR]

SUMMARY: Auxins and cytokinins are two major families of phytohormones that control most aspects of plant growth, development and plasticity. Their distribution in plants has been described, but the importance of cell‐ and subcellular‐type specific phytohormone homeostasis remains undefined. Herein, we revealed auxin and cytokinin distribution maps showing their different organelle‐specific allocations within the Arabidopsis plant cell. To do so, we have developed Fluorescence‐Activated multi‐Organelle Sorting (FAmOS), an innovative subcellular fractionation technique based on flow cytometric principles. FAmOS allows the simultaneous sorting of four differently labelled organelles based on their individual light scatter and fluorescence parameters while ensuring hormone metabolic stability. Our data showed different subcellular distribution of auxin and cytokinins, revealing the formation of phytohormone gradients that have been suggested by the subcellular localization of auxin and cytokinin transporters, receptors and metabolic enzymes. Both hormones showed enrichment in vacuoles, while cytokinins were also accumulated in the endoplasmic reticulum. [ABSTRACT FROM AUTHOR]

Multiscale thematic maps of vegetation cover of the eastern Bolshezemelskaya tundra model area have been analyzed in this work. The primary mapping data has been obtained by processing satellite (Quickbird (Qb), Landsat TM5 (L5)) and aerial (DJI Phantom 2 (unmanned aerial vehicle (UAV)) images. Same imaging dates, survey conditions, and the spectral channel ranges of satellite radiometers have determined the identity of the vegetation cover characteristics on the satellite images. Homogeneous areas have been used for spectral signatures calculation of classes (Qb and L5 classifications) and have been obtained based on UAV imagery. A comparison of aerial and satellite images of the model area have showed that the bulk of the Qb image contained mixed pixels with a composition of the dominant class below 50%. Only 14.6% of the pixels had a share of the dominant class exceeding 80%. The majority (53.8%) of such homogeneous image elements included water surface classes (39.2%) and willow (24.6%). The number of homogeneous pixels of L5 (composition of more than 50% of the surface belongs to the same Qb class) did not exceed 14.1%. The spectral brightness coefficients for homogeneous pixels had high convergence between Qb and L5. Mixed pixels have been able to form spectral signatures with new values and sometimes with classes often missing inside. Overlapping the land cover and water surface class spectral features in mixed pixels formed spectra of eroded peatlands and bare soil. With reduction of resolution, an increase in the presence of an exposed peat class was noted (1.6- to 2.2-fold for transition UAV to Qb and 3.1- to 4.4-fold for Qb to L5, with the highest result being detected during UAV-L5 transition (6.9-fold)). Methods of spectral selection of etalon classes affected the convergence of classification results of spatially different images as well. A weak degree of conjunction was observed between UAV and Qb (30.3% (total) and 20.7% (κ)) and Qb and L5 classifications (44.5 and 30.3%, respectively). This index was negligible for UAV and L5 vegetation maps (28.5 and 15.5%). The main factors influencing the level of convergence and the ratio of class areas on different-scale images were the radiometric features of the class etalons and the spatial homogeneity of the mapped landscapes. [ABSTRACT FROM AUTHOR]

This paper presents an R-based approach to mapping dynamics of the flooded areas in the Inner Niger Delta (IND), Mali, using time series analysis of Landsat 8–9 satellite images. As the largest inland wetland in West Africa, the habitats of IND offers high potential for biodiversity of the flood-dependent eco systems. IND is one of the most productive areas in West Africa. Mapping flooded areas based on satellite images enables to provide strategies for land management and rice planting and modelling vegetation types of IND. Our approach is based on using libraries of R programming language for processing six Landsat images, and each image was taken on November from 2013 to 2022. By capturing spatial and temporal structures of the satellite images on 2013, 2015, 2018, 2020, 2021 and 2022, the remote sensing data are combined to yield estimates of landscape dynamics that is temporally coherent, while helping to analyse fluctuations of spatial extent in fluvial wetlands caused by the hydrological processes of seasonal flooding. Further, by allowing packages of R to support image processing, an approach to mapping vegetation by NDVI, SAVI and EVI indices and visualising changes in distribution of different land cover classes over time is realised. In this context, processing Earth observation data by advanced scripting tools of R language provides new insights into complex interlace of climate-hydrological processes and vegetation responses. Our study contributes to the sustainable management of natural resources and improving knowledge on the functioning of IND ecosystems in Mali, West Africa. [ABSTRACT FROM AUTHOR]

In this article, desertification and dune progression over vegetation was quantified using remote sensing data. However, vegetation buried under sand blowout could not be counted using this method. Therefore, to estimate the extent of buried vegetation, a GPR campaign was conducted over the coastal sand-dune of Tottori Prefecture (Japan) in combination with a high-resolution topographic UAV-based survey of the topography. The results show that buried vegetation exists underneath sand-blowout, especially near the dune ridges, and can extend from 20 to 30 meters further than the estimate based on airborne remote sensing. Furthermore, the presence of palaeo-vegetation in palaeodune layers also provides the information on the long-term evolution of sand dunes, which can be used to reconstruct Quaternary coastal environments. [ABSTRACT FROM AUTHOR]

There is a variety of taxonomic and nomenclatural databases, curated at different intervals and using different criteria to decide which species names are listed as accepted and which are considered synonyms. Botanical collections, such as herbaria or botanical gardens, maintain data that link names to plant material. The choice of the used database affects the naming of the plant specimens. If data from different institutions are to be matched, inconsistencies may arise. A solution that allows the use of different databases side by side would be beneficial in our opinion. The linking of botanical taxonomic and nomenclatural datasets by utilizing standardized Semantic Web technologies allows the coexistence of several lists. In this study, we conducted a mapping of The Plant List, World Flora Online, and the Integrated Taxonomic Information System provided in Darwin Core Archive files to the Simple Knowledge Organization System and created an interlinked version of these datasets. The developed graphical user interface visualized the contents of the included taxonomic databases. The usefulness and usability of the GUI were positively evaluated in interviews with five scientific employees and curators of four different botanical gardens. Citation: Schrumpf A., Killinger M., Schiessle P. & Scherp A. 2024: On the coexistence of taxonomic botanical databases – a user study. – Willdenowia 53: 309–316. https://doi.org/10.3372/wi.53.53308 Version of record first published online on 01 February 2024. [ABSTRACT FROM AUTHOR]

The advent of new spaceborne imaging spectrometers offers new opportunities for ecologists to map vegetation traits at global scales. However, to date most imaging spectroscopy studies exploiting satellite spectrometers have been constrained to the landscape scale. In this paper we present a new method to map vegetation traits at the landscape scale and upscale trait maps to the continental level, using historical spaceborne imaging spectroscopy (Hyperion) to derive estimates of leaf mass per area, nitrogen, and carbon concentrations of forests in Québec, Canada. We compare estimates for each species with reference field values and obtain good agreement both at the landscape and continental scales, with patterns consistent with the leaf economic spectrum. By exploiting the Hyperion satellite archive to map these traits and successfully upscale the estimates to the continental scale, we demonstrate the great potential of recent and upcoming spaceborne spectrometers to benefit plant biodiversity monitoring and conservation efforts. [ABSTRACT FROM AUTHOR]

Understanding the relationship between climate and vegetation requires climate data to be linked with ecological data, including habitat types and vegetation mapping. Our new R package climenv allows researchers to efficiently acquire, extract and visualise data sets that are commonly used by researchers to quantify the climatic envelope of vegetation. climenv offers integrated downloading and processing capabilities for three globally recognised data sets: WorldClim 2, CHELSA and the National Aeronautics and Space Administration's (NASA) Shuttle Radar Topography Mission (SRTM) elevation data. The package allows users to easily download and extract these data sets for single and multi‐geospatial polygon and point data sets, facilitating studies that explore the relationship between vegetation and climate. Furthermore, climenv allows users to plot traditional Holdridge life zone classification, Walter–Lieth climate diagrams and new customised plots, which combine aspects of both these systems with other biologically relevant climate variables. By enhancing the usability and flexibility of these data sets, climenv helps to explore the intricacies of the relationships between climate and vegetation. Our package is accessible from CRAN (https://CRAN.R‐project.org/package=climenv) or GitHub (https://github.com/jamestsakalos/climenv). [ABSTRACT FROM AUTHOR]

Deep learning (DL) models are gaining popularity in forest variable prediction using Earth observation (EO) images. However, in practical forest inventories, reference datasets are often represented by plot- or stand-level measurements, while high-quality representative wall-to-wall reference data for end-to-end training of DL models are rarely available. Transfer learning facilitates expansion of the use of deep learning models into areas with sub-optimal training data by allowing pretraining of the model in areas where high-quality teaching data are available. In this study, we perform a "model transfer" (or domain adaptation) of a pretrained DL model into a target area using plot-level measurements and compare performance versus other machine learning models. We use an earlier developed UNet based model (SeUNet) to demonstrate the approach on two distinct taiga sites with varying forest structure and composition. The examined SeUNet model uses multi-source EO data to predict forest height. Here, EO data are represented by a combination of Copernicus Sentinel-1 C-band SAR and Sentinel-2 multispectral images, ALOS-2 PALSAR-2 SAR mosaics and TanDEM-X bistatic interferometric radar data. The training study site is located in Finnish Lapland, while the target site is located in Southern Finland. By leveraging transfer learning, the SeUNet prediction achieved root mean squared error (RMSE) of 2.70 m and R2 of 0.882, considerably more accurate than traditional benchmark methods. We expect such forest-specific DL model transfer can be suitable also for other forest variables and other EO data sources that are sensitive to forest structure. [ABSTRACT FROM AUTHOR]

The use of unoccupied aerial vehicles (UAVs) for vegetation monitoring is widespread in agriculture and forestry but far less so in ecological restoration where it has tremendous unrealized potential. We tested the ability of multispectral data and a derived vegetation index to classify shrub, herbaceous vegetation, and bare soil cover in a rare alluvial floodplain vegetation community in semiarid Southern California, where shrub cover is manipulated in restoration efforts aimed to provide open habitats required by several threatened and endangered species. Three classifiers and three levels of spatial aggregation were compared for their ability to provide accurate shrub cover estimates at a scale commensurate with the needs of conservation managers. We used object-based image analysis (OBIA) and compared maximum likelihood (ML), support vector machine (SVM), and random forest (RF) classifiers applied to high-spatial resolution (0.14 m) data from a four-band Parrot Sequoia+ multispectral sensor. The SVM and RF classifiers yielded similarly high classification accuracy evaluated using the training data (overall accuracy of 96.4% and 97.6%, respectively), much higher than ML (88%). Aggregating shrub cover data to 25 and 50 m resolutions yielded more accurate and well-calibrated cover estimates (mean absolute error 12% and 11%, respectively, for RF) than 10 m aggregation (MAE 19% for RF). Shrub cover estimated using RF and SVM was able to meet the restoration monitoring needs to distinguish the three phases of shrub habitat characterized by their cover (10–30%, 30–75%, >75%) that differ in habitat quality and restoration prescriptions. [ABSTRACT FROM AUTHOR]

Proxy‐based reconstructions show that the climate during the mid‐Holocene (MH) was warmer and wetter than the present day in most regions of China, characterized by a northwestward extension of the East Asian summer monsoon and an increase in woody cover. However, climate simulations that neglect vegetation shifts yield cooler climates than proxy‐based reconstructions for the MH. Here, we simulate MH vegetation cover, productivity, and terrestrial parameters in China using a state‐of‐the‐art land surface model forced with realistic proxy‐based climate data. Our simulations corroborate MH vegetation cover inferred from 246 pollen sites, and indicate a higher vegetation productivity in China, despite the lower CO2 concentration during the MH compared to the present day. Enhanced vegetation growth during the MH could have affected land surface energy and hydrological budgets through biophysical feedbacks. Our findings highlight the impact of vegetation dynamics on the terrestrial carbon cycle and regional climate in China. Plain Language Summary: Multiple paleoclimate archives illustrate warmer and wetter climate during the mid‐Holocene (MH; ∼6,000 years ago) than at present in most regions of China. However, current model results and proxy‐based reconstructions have some key discrepancies in climate reconstructions, possibly because these climate simulations have overlooked vegetation changes. Therefore, it is essential to develop a vegetation cover map for China to examine the vegetation‐climate feedback during the MH. Here, we present reconstructions of mid‐Holocene vegetation cover, productivity and terrestrial parameters in China at a very high resolution using a land surface model. Simulation results can reproduce MH vegetation cover indicated by paleo‐records and suggest increased tree cover and vegetation productivity in China. Vegetation changes during the MH likely had an important impact on regional climate in China through altering terrestrial parameters. In contrast, multiple‐model ensembles in the latest Paleoclimate Modeling Intercomparison Project significantly underestimated MH vegetation growth primarily because of inherent bias in prescribed vegetation maps. This work reveals the critical impact of MH vegetation cover on terrestrial carbon, energy and hydrological budgets in China, and underlines the need to incorporate vegetation dynamics in terrestrial or climate simulations for the past and future. Key Points: Our work provides a reliable and spatially continuous mid‐Holocene vegetation map for China at a resolution of 0.5°Calculated mid‐Holocene vegetation gross primary productivity of ∼7.5 PgC/yr in China is higher than the present dayVegetation dynamics should be considered in simulating mid‐Holocene terrestrial carbon cycle and climate change for China [ABSTRACT FROM AUTHOR]

Using historical data from 1986 to 2016, this study quantifies the impacts of climate change and measures the effectiveness of the Indonesian government's climate change adaptation policies on rice production in 14 Indonesian provinces. Generally, the Feasible Generalised Least Squares (FGLS) estimation result showed that rises in temperature and precipitation negatively affected rice production, but relative humidity was beneficial for rice production. At the provincial level, the result of the Seemingly Unrelated Regression (SUR) estimation revealed that West Java was worst affected by climate change. Climate change adaptation policies, including a planting calendar map and farmer insurance, showed positive influence in increasing rice production. Further application of the planting calendar map and reinforced agricultural extension advice are advisable. [ABSTRACT FROM AUTHOR]

Questions: Can we map complex habitat mosaics from remote‐sensing data? In doing this, are measures of spectral heterogeneity useful to improve image classification performance? Which measures are the most important? How can multitemporal data be integrated in a robust framework? Location: Classical Karst (NE Italy). Methods: First, a habitat map was produced from field surveys. Then, a collection of 12 monthly Sentinel‐2 images was retrieved. Vegetation and spectral heterogeneity (SH) indices were computed and aggregated in four combinations: (1) monthly layers of vegetation and SH indices; (2) seasonal layers of vegetation and SH indices; (3) yearly layers of SH indices computed across the months; and (4) yearly layers of SH indices computed across the seasons. For each combination, a Random Forest classification was performed, first with the complete set of input layers and then with a subset obtained by recursive feature elimination. Training and validation points were independently extracted from field data. Results: The maximum overall accuracy (0.72) was achieved by using seasonally aggregated vegetation and SH indices, after the number of vegetation types was reduced by aggregation from 26 to 11. The use of SH measures significantly increased the overall accuracy of the classification. The spectral β‐diversity was the most important variable in most cases, while the spectral α‐diversity and Rao's Q had a low relative importance, possibly because some habitat patches were small compared to the window used to compute the indices. Conclusions: The results are promising and suggest that image classification frameworks could benefit from the inclusion of SH measures, rarely included before. Habitat mapping in complex landscapes can thus be improved in a cost‐ and time‐effective way, suitable for monitoring applications. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) hosts a variety of vegetation types ranging from broadleaved and needle-leaved forests at the lower altitudes and mesic areas to alpine grassland at the higher altitudes and xeric areas. Accurate and detailed mapping of the vegetation distribution on TP is essential for an improved understanding of climate change effects on terrestrial ecosystems. Yet, existing land cover datasets of TP are either provided at a low spatial resolution or have insufficient vegetation types to characterize certain unique TP ecosystems, such as the alpine scree. Here, we produced a 10 m resolution TP land cover map with 12 vegetation classes and 3 non-vegetation classes for the year 2022 (referred as TP_LC10-2022) by leveraging state-of-the-art remote sensing approaches including the Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and 4 machine learning models using Google Earth Engine platform. Our dataset TP_LC10-2022 achieved an overall classification accuracy of 86.5% with a Kappa coefficient of 0.854. By comparing with 4 existing global land cover products, TP_LC10-2022 showed significant improvements in terms of reflecting local-scale vertical variations in the southeast TP region. Moreover, we found that alpine scree occupied 13.99% of the TP region which was ignored in existing land cover datasets, and that shrublands occupied 4.63% of the TP region characterized by distinct forms of deciduous shrublands and evergreen shrublands largely determined by topography and missed in existing land cover datasets. Our dataset provides a solid foundation for further analyses which need accurate delineation of these unique vegetation types in TP. The TP_LC10-2022 and the sample dataset are freely available at https://doi.org/10.5281/zenodo.8228112 (Huang et al., 2023b) and https://doi.org/10.5281/zenodo.8227942 (Huang et al., 2023a) respectively. Additionally, the classification map can be viewed through https://cold-classifier.users.earthengine.app/view/tplc10-2022. [ABSTRACT FROM AUTHOR]

[Display omitted] Structural information of grassland changes on the Tibetan Plateau is essential for understanding alterations in critical ecosystem functioning and their underlying drivers that may reflect environmental changes. However, such information at the regional scale is still lacking due to methodological limitations. Beyond remote sensing indicators only recognizing vegetation productivity, we utilized multivariate data fusion and deep learning to characterize formation-based plant community structure in alpine grasslands at the regional scale of the Tibetan Plateau for the first time and compared it with the earlier version of Vegetation Map of China for historical changes. Over the past 40 years, we revealed that (1) the proportion of alpine meadows in alpine grasslands increased from 50% to 69%, well-reflecting the warming and wetting trend; (2) dominances of Kobresia pygmaea and Stipa purpurea formations in alpine meadows and steppes were strengthened to 76% and 92%, respectively; (3) the climate factor mainly drove the distribution of Stipa purpurea formation, but not the recent distribution of Kobresia pygmaea formation that was likely shaped by human activities. Therefore, the underlying mechanisms of grassland changes over the past 40 years were considered to be formation dependent. Overall, the first exploration for structural information of plant community changes in this study not only provides a new perspective to understand drivers of grassland changes and their spatial heterogeneity at the regional scale of the Tibetan Plateau, but also innovates large-scale vegetation study paradigm. [ABSTRACT FROM AUTHOR]

The article focuses on the significance of grasslands, which were once considered low in diversity, as recent research shows their high biodiversity and carbon storing potential, requiring over a century to recover after agricultural plowing; it emphasizes the importance of conserving natural grasslands for their ecosystem services and their role in food security and climate change mitigation.

The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations. [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]

Accurate and timely information on winter wheat spatial distribution is essential for food security and environmental sustainability. However, high-quality nation-wide winter wheat products at high resolutions are still scarce around the world, and the approaches for winter wheat mapping are generally constrained by the lack of sufficient and representative training data. In this study, a knowledge-based approach based on spectral and polarization information from critical stages of winter wheat, was proposed to extract high-quality training data of winter wheat, thereby supporting winter wheat mapping with machine learning classifiers. Additionally, classification model trained by the generated training data was transferred across years to achieve the in-season mapping of winter wheat. Two-year classification scenarios based on the automated training data generation (ATDG) or model transfer (MT) were designed to evaluate the quality of automatically generated training data, the performance of model transfer, the contribution of optical and radar data, and the earliest timing for winter wheat mapping over China. With the ATDG and MT, the first 10-m resolution maps of winter wheat over China (ChinaWheat10) were produced for three consecutive years (2020 & 2021 by ATDG; 2021 & 2022 by MT). For ATDG and MT, the combined features of Sentinel-1 and Sentinel-2 yielded the highest overall accuracies with the random forest classifier. Specifically, winter wheat mapping with the ATDG achieved the highest F1-score of 0.94 for both 2020 and 2021. The MT reached a comparable F1-score of 0.94 and 0.93 for 2021 and 2022, and winter wheat maps with the F1-score of 0.93 and 0.92 could be produced as early as April (two months ahead of harvesting). Besides well-delineated winter wheat parcels, the estimated areas of ChinaWheat10 aligned well with the agricultural census data at the provincial (R2 ≥ 0.95) and municipal (R2 ≥ 0.91) levels. These findings suggest the proposed approaches have a great potential for accurate, cost-effective and high-resolution in-season mapping of winter wheat over large regions. [ABSTRACT FROM AUTHOR]