Search

Aquatic vegetation is crucial for improving water quality, supporting fisheries and preserving biodiversity in lakes. Monitoring the spatiotemporal dynamics of aquatic vegetation is indispensable for the assessment and protection of lake ecosystems. Nevertheless, a comprehensive global assessment of lacustrine aquatic vegetation is lacking. This study introduces an automatic identification algorithm (with a total accuracy of 94.4%) for Sentinel-2 MSI, enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to 2022. Results show that aquatic vegetation occurred in 81,116 lakes across six continents over the past four years, covering a cumulative maximum aquatic vegetation area (MVA) of 16,111.8 km2. The global median aquatic vegetation occurrence (VO, in %) is 3.0%, with notable higher values observed in South America (7.4%) and Africa (4.1%) compared with Asia (2.7%) and North America (2.4%). High VO is also observed in lakes near major rivers such as the Yangtze, Ob, and Paraná Rivers. Integrating historical data with our calculated MVA, the aquatic vegetation changes in 170 lakes worldwide were analyzed. It shows that 72.4% (123/170) of lakes experienced a decline in aquatic vegetation from the early 1980s to 2022, encompassing both submerged and overall aquatic vegetation. The most substantial decrease is observed in Asia and Africa. Our findings suggest that, beyond lake algal blooms and temperature, the physical characteristics of the lakes and their surrounding environments could also influence aquatic vegetation distribution. Our research provides valuable information for the conservation and restoration of lacustrine aquatic vegetation. [ABSTRACT FROM AUTHOR]

In order to ensure food security, it is crucial to collect agricultural information efficiently and accurately. Remote sensing has become increasingly important in obtaining crop distribution information on a large scale. However, current research based on satellite platforms struggles to meet the requirements of high-precision and large-scale crop monitoring simultaneously. To address this challenge, we propose a method for achieving fine-scale crop classification by integrating remote-sensing data from various satellite platforms by constructing temporal-scale crop features within the parcels using Sentinel-2A, Landsat-8, and Gaofen-6. We adopt a feature-matching method to fill in missing values in the time-series feature construction process, to avoid issues with unidentifiable crops. The classification results of the Yellow River basin of the Ningxia region show that our method can achieve a wide range of crop discrimination on a fine scale, with an overall accuracy of 80%. Our proposed method demonstrates the potential of integrating multi-platform remote-sensing data to achieve fine-scale crop classification, which can aid decision-making for farmers, government agencies, and other stakeholders involved in the agricultural sector. [ABSTRACT FROM AUTHOR]

Low carbon management and policies should refer to local long-term inter-annual carbon uptake. However, most previous research has only focused on the quantity and spatial distribution of gross primary product (GPP) for the past 50 years because most satellite launches, the main GPP data source, were no earlier than 1980. We identified a close relationship between the tree-ring index (TRI) and vegetation carbon dioxide uptake (as measured by GPP) and then developed a nested TRI-GPP model to reconstruct spatially explicit GPP values since 1895 from seven tree-ring chronologies. The model performance in both phases was acceptable: We chose general regression neural network regression and random forest regression in Phase 1 (1895–1937) and Phase 2 (1938–1985). With the simulated and real GPP maps, we observed that the GPP for grassland and overall GPP were increasing. The GPP landscape patterns were stable, but in recent years, the GPP's increasing rate surpassed any other period in the past 130 years. The main local climate driver was the Palmer Drought Severity Index (PDSI), and GPP had a significant positive correlation with PDSI in the growing season (June, July, and August). With the GPP maps derived from the nested TRI-GPP model, we can create fine-scale GPP maps to understand vegetation change and carbon uptake over the past century. [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]

Sugarcane plays a pivotal role in the Brazilian economy as a primary crop. This semi-perennial crop allows for multiple harvests throughout its life cycle. Given its longevity, farmers need to be mindful of avoiding gaps in sugarcane fields, as these interruptions in planting lines negatively impact overall crop productivity over the years. Recognizing and mapping planting failures becomes essential for replanting operations and productivity estimation. Due to the scale of sugarcane cultivation, manual identification and mapping prove impractical. Consequently, solutions utilizing drone imagery and computer vision have been developed to cover extensive areas, showing satisfactory effectiveness in identifying gaps. However, recognizing small gaps poses significant challenges, often rendering them unidentifiable. This study addresses this issue by identifying and mapping gaps of any size while allowing users to determine the gap size. Preliminary tests using YOLOv5 and ImageJ 1.53k demonstrated a high success rate, with a 96.1% accuracy in identifying gaps of 50 cm or larger. These results are favorable, especially when compared to previously published works. [ABSTRACT FROM AUTHOR]

AbstractAlgae also positively affect these microorganisms and soil fertility. The composition of potential bioproducts from algae can vary depending on the type of algae cell. The inoculation of seeds/soils with phosphate-solubilizing microorganisms is therefore a promising method to improve food production without causing environmental pollution. For this purpose, increasing doses of algae extract (0, 0.2, 0.4, 0.8, and 1%) and different phosphorus (P) sources were added to clay loam soil at the 0 and 130 mg kg−1 doses. As a result, the effect of algal extract applied in increasing doses on some growth parameters of bean plants was found to be statistically significant for both P sources. The lowest yield components were also obtained in the control treatment (-algae extract; -P). As the applied algae extract doses increased, the yield components for mono-ammonium phosphate (MAP) and dicalcium phosphate (CaHPO4) applications increased. On the other hand, the values obtained from plants treated with MAP as a source of phosphorus were higher than those obtained with CaHPO4. Increasing the level of algae extract across MAP-treated plants resulted in higher values in the plant’s upper parts but lower values in the root. Compared to other treatments, plants treated with CaHPO4 showed higher values when algaeextract was applied at concentrations of 0.8% and 1%. [ABSTRACT FROM AUTHOR]

The global increase in wildfires due to climate change highlights the need for accurate wildfire mapping. This study performs a proof of concept on the usefulness of SuperDove imagery for wildfire mapping. To address this topic, we present an automatic methodology that combines the use of various vegetation indices with clustering algorithms (bisecting k-means and k-means) to analyze images before and after fires, with the aim of improving the precision of the burned area and severity assessments. The results demonstrate the potential of using this PlanetScope sensor, showing that the methodology effectively delineates burned areas and classifies them by severity level, in comparison with data from the Copernicus Emergency Management Service (CEMS). Thus, the potential of the SuperDove satellite sensor constellation for fire monitoring is highlighted, despite its limitations regarding radiometric distortion and the absence of Short-Wave Infrared (SWIR) bands, suggesting that the methodology could contribute to better fire management strategies. [ABSTRACT FROM AUTHOR]

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. Due to the short and sparse features of shrubs, capturing the shrub biomass accurately by satellite observations is challenging. Previous studies mostly used ground samples and satellite observations to estimate shrub biomass by establishing a direct connection, which was often hindered by the limited number of ground samples and spatial scale mismatch between samples and observations. Unmanned aerial vehicles (UAVs) provide opportunities to obtain more samples that are in line with the aspects of satellite observations (i.e., scale) for regional-scale shrub biomass estimations accurately with low costs. However, few studies have been conducted based on the air-space-ground-scale connection assisted by UAVs. Here we developed a framework for estimating 10 m shrub biomass at a regional scale by integrating ground measurements, UAV, Landsat, and Sentinel-1/2 observations. First, the spatial distribution map of shrublands and non-shrublands was generated in 2023 in the Helan Mountains of Ningxia province, China. This map had an F1 score of 0.92. Subsequently, the UAV-based shrub biomass map was estimated using an empirical model between the biomass and the crown area of shrubs, which was aggregated at a 10 m × 10 m grid to match the spatial resolution of Sentinel-1/2 images. Then, a regional-scale estimation model of shrub biomass was developed with a random forest regression (RFR) approach driven by ground biomass measurements, UAV-based biomass, and the optimal satellite metrics. Finally, the developed model was used to produce the biomass map of shrublands over the study area in 2023. The uncertainty of the resultant biomass map was characterized by the pixel-level standard deviation (SD) using the leave-one-out cross-validation (LOOCV) method. The results suggested that the integration of multi-scale observations from the ground, UAVs, and satellites provided a promising approach to obtaining the regional shrub biomass accurately. Our developed model, which integrates satellite spectral bands and vegetation indices (R2 = 0.62), outperformed models driven solely by spectral bands (R2 = 0.33) or vegetation indices (R2 = 0.55). In addition, our estimated biomass has an average uncertainty of less than 4%, with the lowest values (<2%) occurring in regions with high shrub coverage (>30%) and biomass production (>300 g/m2). This study provides a methodology to accurately monitor the shrub biomass from satellite images assisted by near-ground UAV observations as well as ground measurements. [ABSTRACT FROM AUTHOR]

Under the background of global change, the lake water environment is facing a huge threat from eutrophication. The rapid increase in curly-leaf pondweed (Potamogeton crispus L.) in recent years has seriously threatened the ecological balance and the water diversion safety of the eastern route of China's South-to-North Water Diversion Project. The monitoring and control of curly-leaf pondweed is imperative in shallow lakes of northern China. Unmanned Aerial Vehicles (UAVs) have great potential for monitoring aquatic vegetation. However, merely using satellite remote sensing to detect submerged vegetation is not sufficient, and the monitoring of UAVs on aquatic vegetation is rarely systematically evaluated. In this study, taking Nansi Lake as a case, we employed Red–Green–Blue (RGB) UAV and satellite datasets to evaluate the monitoring of RGB Vegetation Indices (VIs) in pondweed and mapped the dynamic patterns of the pondweed Fractional Vegetation Coverage (FVC) in Nansi Lake. The pondweed FVC values were extracted using the RGB VIs and the machine learning method. The extraction of the UAV RGB images was evaluated by correlations, accuracy assessments and separability. The correlation between VIs and FVC was used to invert the pondweed FVC in Nansi Lake. The RGB VIs were also calculated using Gaofen-2 (GF-2) and were compared with UAV and Sentinel-2 data. Our results showed the following: (1) The RGB UAV could effectively monitor the FVC of pondweed, especially when using Support Vector Machine that (SVM) has a high ability to recognize pondweed in UAV RGB images. Two RGB VIs, RCC and RGRI, appeared best suited for monitoring aquatic plants. The correlations between four RGB VIs based on GF-2, i.e., GCC, BRI, VDVI, and RGBVI and FVCSVM calculated by the UAV (p < 0.01) were better than those obtained with other RGB VIs. Thus, the RGB VIs of GF-2 were not as effective as those of the UAV in pondweed monitoring. (2) The binomial estimation model constructed by the Normalized Difference Water Index (NDWI) of Sentinel-2 showed a high accuracy (R2 = 0.7505, RMSE = 0.169) for pondweed FVC and can be used for mapping the FVC of pondweed in Nansi Lake. (3) Combined with the Sentinel-2 time-series data, we mapped the dynamic patterns of pondweed FVC in Nansi Lake. It was determined that the flooding of pondweed in Nansi Lake has been alleviated in recent years, but the rapid increase in pondweed in part of Nansi Lake remains a challenging management issue. This study provides practical tools and methodology for the innovative remote sensing monitoring of submerged vegetation. [ABSTRACT FROM AUTHOR]

Central Asia (CA) is located in the interior of the Eurasian continent and consists of five countries—Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. It contains the largest concentration of temperate deserts and mountains of CA biodiversity hotspots. However, regionwide floristic diversity is sorely lacking, and nationwide floristic diversity is seriously outdated in this region. Using the data collected by the Mapping Asia Plants (MAP) project, we describe and analyze the diverse floristic characteristics of plant diversity in CA at both the regional and national levels, including the dominant families and genera, endemic taxa, and floristic similarity. The results allow the compilation of a new checklist of vascular plants in CA, including 9643 taxa (1198 genera within 139 families) and 3409 endemic taxa (414 genera in 66 families). We confirm that there are 5695, 4036, 4542, 3005, and 4222 species of vascular plants within the CA countries, of which 532, 326, 505, 175, and 301 species are endemic taxa in Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, respectively. The region's biodiversity is notable for its high degree of endemism—up to 35.35%—which contributes to the floristic uniqueness and the irreplaceability of CA. Tajikistan, encompassing the most dominant area of the CA mountains, has the highest species density (3.19/100 km2) and endemism (11.12%) among the five countries. Neighboring countries such as Tajikistan and Uzbekistan, and Kazakhstan and Kyrgyzstan share more species in common, while Turkmenistan has less species overlap with the other four countries. Trends in endemic and total taxa are consistent. This comprehensive inventory is novel, revealing CA's plant diversity in two dimensions and providing a solid foundation for subsequent research that will be beneficial to the transboundary conservation and sustainable use of plant resources in CA. [ABSTRACT FROM AUTHOR]

Large-extent maps of three-dimensional vegetation structure are important for understanding the hydrologic cycle, climate, carbon fluxes, and habitat. We aggregated over 7 billion lidar shots from the Global Ecosystem Dynamics Investigation (GEDI) to produce analysis-ready, gridded rasters of 36 vegetation structure metrics at three spatial resolutions (1, 6, and 12 km). We used 8 statistics to grid shots in every pixel, specifically the mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, Shannon's Diversity Index, and shot count. We quantified uncertainty of the mean by randomly selecting 100 subsets of shots (i.e. bootstrapping) within each pixel. We also assessed the accuracy of several gridded metrics using fine spatial resolution airborne laser scanning data. The gridded metrics are generally more accurate at mid latitudes due to higher shot density and lower density of vegetation. Statistics associated with the central or maximum tendency of a metric are more accurate than statistics related to variability of metric values within the pixel. [ABSTRACT FROM AUTHOR]

A work on the flora and vegetation of Milos, a small island in the southwest Kiklades, Greece is planned. Progress and a summary of results so far, are presented. [ABSTRACT FROM AUTHOR]

Land Use and Land Cover Change (LULCC) is a key driver of changes in land–atmosphere interactions, playing an essential role in climate and weather patterns in South America (SA). This study used a modelling approach to assess the land–atmosphere changes introduced by LULCC over the Brazilian territory. Numerical experiments of early (2006), neutral (2004) and late (2008) rainy season onset years were performed in the Integrated BIosphere Simulator (IBIS) to assess the land‐surface parameters, and in Brazilian Atmospheric Model (BAM) to assess the atmospheric variables. The models were run with a natural (NAT) vegetation map and an updated (UP) vegetation map, which incorporated realistic Brazil deforestation up to the early 2000s. The differences between the two maps were particularly larger over 25°–15°S and 50°–40°W, where the Atlantic Forest and Cerrado biomes were replaced by pasture. IBIS experiments showed that over the area of larger LULCC there was an increase in albedo of up to 8% while reductions in net radiation, surface heat fluxes, surface temperature and surface roughness were noticed. Land–atmosphere feedbacks assessed with BAM experiments showed that LULCC contributed to a drier and colder (stable) atmosphere over central‐east SA that opposes the necessary conditions for the rainy season onset. The changes included increases in surface pressure and low‐level wind associated with reductions in 2 m temperature and surface roughness. These changes contributed to decrease the cloud formation and less precipitation in all three rainy season onset years, but particularly in the late rainy season onset year during September–October–November (SON). The atmospheric changes induced by LULCC show a pattern of dry atmosphere (reduced precipitation), similar to those of late onset years, and indicate that LULCC enhances the late rainy season onset condition over central‐east SA. Additionally, this study highlights the importance of considering up‐to‐date vegetation maps because these changes significantly affect both surface and atmospheric variables. [ABSTRACT FROM AUTHOR]

Lobed leaves are vital in the high-density cultivation and breeding of wax gourd (Benincasa hispida). Thus, determining the molecular mechanisms underlying the development of lobed leaves is important. To this end, this study aimed to resequence 105 recombinant inbred lines, constructed using the parental lines, GX-7 and my-1, to elucidate the molecular mechanisms underlying leaf development in wax gourd. Genes associated with lobed leaves in wax gourds were first evaluated via quantitative trait loci (QTL) mapping. Next, the F2 population was expanded to 2000 plants for fine mapping and candidate gene analysis. Thus, the candidate area was reduced to 1.129 Mb, located between markers InDel980 and InDel853. Functional analyses of candidate genes were performed using gene functional annotation, coding sequence analyses, and expression analyses. Among 48 genes in the candidate region, only Bch04G012650 (termed BhDDL4.1) showed differences in expression between the two parents. Using sequence differences of previously screened candidate genes, an InDel marker (InDel623) was developed in BhDDL4.1 for molecular marker–assisted breeding of wax gourd; the accuracy rate was 74.03%. Our results indicate that BhDDL4.1 may play a key role in the regulation of the lobed leaf trait; thereby, we provide a theoretical basis for further exploration of the molecular mechanisms underlying the lobed leaf trait in wax gourd. [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]

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

Plant height (PH) and flag leaf (FL) are two crucial agronomic traits for crop yield. A recombinant inbred line (RIL) population from 'Mian 96-5' (M96-5) and 'Guixie 3' (GX3) was investigated in five environments to study PH and FL characteristics in Chinese southwest wheat. Eight QTLs related to PH were detected on five chromosomes 1D (1), 2D (1), 4B (2), 4D (2), and 5A (2). Of these, Qph.gaas.2D explained phenotypic variance (PVE) of 22.40%, Qph.gaas.4B.1 (PVE = 21.92%), Qph.gaas.4D.1 (PVE = 12.29%), and Qph.gaas.4D.2 (PVE = 13.69%), which were consistent with known Rht8, Rht-B1, and Rht-D1 loci, respectively. The other four QTLs about PH, namely Qph.gaas.1D (212.50–258.36 Mb), Qph.gaas.4B.2 (643.50–646.67 Mb), Qph.gaas.5A.1 (515.06–523.07 Mb), and Qph.gaas.5A.2 (523.07–528.26 Mb), were novel QTLs for reduced wheat PH. Five, four, and five QTLs were related to length, width, and area of flag leaf. Among them, a major QTL (Qfll.gaas.2D, 15.79 ~ 37.04 Mb) was detected on chromosome 2D, where six candidate genes related to the photo-respiration stage of photosynthesis were found. Two important regions on chromosome 4B (25.83 ~ 29.20 Mb and 32.96 ~ 35.63 Mb) were related to the length, width and area of flag leaf at multi-environments, and including genes related to programmed cell death and metabolic regulation of gibberellin in the wheat grain-filling stages. These QTLs would be useful in further studies for decreasing plant height, providing theoretical support for the improvement of wheat plant type and boosting grain yield through pyramiding breeding. [ABSTRACT FROM AUTHOR]

Abstract A combined multiscale approach using ground, drone and satellite surveys can provide accurate landscape scale spatial mapping and monitoring. We used field observations with drone collected imagery covering 70 ha annually for a 5‐year period to estimate changes in living and dead vegetation of four widespread and abundant arid zone woody shrub species. Random forest classifiers delivered high accuracy (> 95%) using object‐based detection methods, with fast repeatable and transferrable processing using Google Earth Engine. Our classifiers performed well in both dominant arid zone landscape types: dune and swale, and at extremes of dry and wet years with minimal alterations. This highlighted the flexibility of the approach, potentially delivering insights into changes in highly variable environments. We also linked this classified drone vegetation to available temporally and spatially explicit Landsat satellite imagery, training a new, more accurate fractional vegetation cover model, allowing for accurate tracking of vegetation responses at large scales in the arid zone. Our method promises considerable opportunity to track vegetation dynamics including responses to management interventions, at large geographic scales, extending inference well beyond ground surveys.

This study develops a structure for mapping native vegetation in a transition area between the Brazilian Cerrado and the Atlantic Forest from integrated spatial information of Sentinel-1 and Sentinel-2 satellites. Most studies use integrated data to improve classification accuracy in adverse atmospheric conditions, in which optical data have many errors. However, this method can also improve classifications carried out in landscapes with favorable atmospheric conditions. The use of Sentinel-1 and Sentinel-2 data can increase the accuracy of mapping algorithms and facilitate visual interpretation during sampling by providing more parameters that can be explored to differentiate land use classes with complementary information, such as spectral, backscattering, polarimetry, and interferometry. The study area comprises the Lobo Reservoir Hydrographic Basin, which is part of an environmental conservation unit protected by Brazilian law and with significant human development. LULC were classified using the random forest deep learning algorithm. The classifying attributes were backscatter coefficients, polarimetric decomposition, and interferometric coherence for radar data (Sentinel-1), and optical spectral data, comprising bands in the red edge, near-infrared, and shortwave infrared (Sentinel-2). The attributes were evaluated in three settings: SAR and optical data in separately settings (C1 and C2, respectively) and in an integrated setting (C3). The study found greater accuracy for C3 (96.54%), an improvement of nearly 2% compared to C2 (94.78%) and more than 40% in relation to C1 (55.73%). The classification algorithm encountered significant challenges in identifying wetlands in C1, but performance improved in C3, enhancing differentiation by stratifying a greater number of classes during training and facilitating visual interpretation during sampling. Accordingly, the integrated use of SAR and optical data can improve LULC mapping in tropical regions where occurs biomes interface, as in the transitional Brazilian Cerrado and Atlantic Forest. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is crucial for global climate change and China's ecological security. Given recent drastic changes in vegetation from climate change and human activities, long-term vegetation monitoring is urgently required. This study produced the vegetation maps of the TP from 1990 to 2020 every ten years using random forest classifier and Landsat imagery. We selected the same stable samples and features for mapping to reduce errors between years and proposed spatial filtering to further improve the accuracy. The overall accuracy surpassed 95.00%, with all Kappa coefficients exceeding 0.95. A further assessment based on sampling sites from literature and field survey was higher than 80%. The importance ranking results indicated that in the TP, climate factors and terrain factors are the most important factors in the vegetation mapping. This study provides a method for mapping vegetation in alpine areas and data support for researching the dynamic change of vegetation on the TP and evaluating its response to climate change. [ABSTRACT FROM AUTHOR]

The Qinghai Tibet Plateau (QTP), known as the 'Third Pole' of the Earth' and the 'Water Tower of Asia,' plays a crucial role in global climate regulation, biodiversity conservation, and regional socio-economic development. Continuous annual vegetation types and their geographical distribution data are essential for studying the response and adaptation of vegetation to climate change. However, there is very limited data on vegetation types and their geographical distributions on the QTP due to harsh natural environment. Currently, land cover/surface vegetation (LCSV) data are typically obtained using independent classification methods for each period's product, based on remote sensing information. These approaches do not consider the time continuity of vegetation to presence, and leads to a gradual increase in the number of misclassified pixels and the uncertainty of their locations, consequently decreasing the interpretability of the long-time series remote sensing products. To address this issue, this study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the QTP from 2000 to 2022 at a 500 m spatial resolution through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping from 2000 to 2022 reached 80.9 % based on 733 samples from literature, with the reference annual 2020 reaching an accuracy of 86.5 % and a Kappa coefficient of 0.85. The study supports the use of remote sensing data to mapping a long-term continuous annual vegetation. [ABSTRACT FROM AUTHOR]

The classification of ultra-high-resolution (UHR) imagery, characterized by spatial resolutions exceeding 10 cm, presents opportunities and challenges distinct from lower-resolution counterparts. Particularly, challenges are pronounced in some scenarios, such as mapping plant species in coastal environments, where similar vegetation responses and small plant sizes pose additional difficulties. The present work addressed such issues by developing a UHR vegetation cover classification model at the single plant level using data from uncrewed aerial systems (UASs) equipped with a multispectral optical sensor. The model was tested across the San Rossore Regional Park (Italy), where three pilot areas were defined as training-test-validation sites. The proposed solution consists of a hierarchical two-level-of-detail machine learning model based on object-based image analysis (OBIA) and random forest. This model considers spectral features and indices, elevation, and texture and can classify twelve plant species and two service classes (debris and sand) within the study areas. Train and test were carried out utilizing UAS flight data collected during two specific phenological periods and precise field data derived from in-situ vegetation surveys, which provided 937 herbaceous and shrub samples. The model performance was evaluated based on the error matrix and 50-fold stratified cross-validation method, obtaining an overall accuracy (OA) of 0.76 and a standard deviation of 0.08. Such assessment underscored the crucial role of texture information, in addition to radiometric and elevation. Finally, the model was tested against an unseen dataset, proving its transferability (OA equal to 0.62). Although the discussion highlights some aspects to be further improved and claims for future research, the first version of this hierarchical classification model demonstrated its potential for mapping and monitoring coastal sand dune ecosystems, providing data for understanding and, eventually, modeling ecological and biogeomorphological dynamics. [ABSTRACT FROM AUTHOR]

Optical satellite data products (e.g., Sentinel-2, PlanetScope, Landsat) require proper validation across diverse ecosystems. This has conventionally been achieved using airborne and more recently unmanned aerial vehicle (UAV) based hyperspectral sensors which constrain operations by both their cost and complexity of use. The MicaSense Altum is an accessible multispectral sensor that integrates a radiometric thermal camera with 5 bands (475 nm–840 nm). In this work we assess the spectral reflectance accuracy of a UAV-mounted MicaSense Altum at 25, 50, 75, and 100 m AGL flight altitudes using the manufacturer provided panel-based reflectance conversion technique for atmospheric correction at the Mer Bleue peatland supersite near Ottawa, Canada. Altum derived spectral reflectance was evaluated through comparison of measurements of six known nominal reflectance calibration panels to in situ spectroradiometer and hyperspectral UAV reflectance products. We found that the Altum sensor saturates in the 475 nm band viewing the 18% reflectance panel, and for all brighter panels for the 475, 560, and 668 nm bands. The Altum was assessed against pre-classified hummock-hollow-lawn microtopographic features using band level pair-wise comparisons and common vegetation indices to investigate the sensor's viability as a validation tool of PlanetScope Dove 8 band and Sentinel-2A satellite products. We conclude that the use of the Altum needs careful consideration, and its field deployment and reflectance output does not meet the necessary cal/val requirements in the peatland site. [ABSTRACT FROM AUTHOR]

The effective identification of reclaimed vegetation species is important for the subsequent management of ecological restoration projects in mining areas. Hyperspectral remote sensing has been used for identifying vegetation species. However, few studies have focused on mine-reclaimed vegetation. Even if there are studies in this field, the methods used by the researches are mainly traditional discriminant analyses. The environmental conditions of reclaimed mining areas lead to significant intraclass spectral differences in reclaimed vegetation, and there is uncertainty in the identification of reclaimed vegetation species using traditional classification models. In this study, in situ hyperspectral data were used to analyze the spectral variation in the reclaimed vegetation canopy in mine restoration areas and evaluate their potential in the identification of reclaimed vegetation species. We measured the canopy spectral reflectance of five vegetation species in the study area using the ASD FieldSpec 4. The spectral characteristics of vegetation canopy were analyzed by mathematically transforming the original spectra, including Savitzky–Golay smoothing, first derivative, reciprocal logarithm, and continuum removal. In addition, we calculated indicators for identifying vegetation species using mathematically transformed hyperspectral data. The metrics were submitted to a feature selection procedure (recursive feature elimination) to optimize model performance and reduce its complexity. Different classification algorithms (regularized logistic regression, back propagation neural network, support vector machines with radial basis function kernel, and random forest) were constructed to explore optimal procedures for identifying reclaimed vegetation species based on the best feature metrics. The results showed that the separability between the spectra of reclaimed vegetation can be improved by applying different mathematical transformations to the spectra. The most important spectral metrics extracted by the recursive feature elimination (RFE) algorithm were related to the visible and near-infrared spectral regions, mainly in the vegetation pigments and water absorption bands. Among the four identification models, the random forest had the best recognition ability for reclaimed vegetation species, with an overall accuracy of 0.871. Our results provide a quantitative reference for the future exploration of reclaimed vegetation mapping using hyperspectral data. [ABSTRACT FROM AUTHOR]

Fire alters soil hydrologic properties leading to increased risk of catastrophic debris flows and post‐fire flooding. As a result, US federal agencies map soil burn severity (SBS) via direct soil observation and adjustment of rasters of burned area reflectance. We developed a unique application of digital soil mapping (DSM) to map SBS in the Creek Fire which burned 154,000 ha in the Sierra Nevada. We utilized 169 ground‐based observations of SBS in combination with raster proxies of soil forming factors, pre‐fire fuel conditions, and fire effects to vegetation to build a digital soil mapping model of soil burn severity (DSMSBS) using a random forest algorithm and compared the DSMSBS map to the established SBS map. The DSMSBS model had a cross‐validation accuracy of 48%. The established technique had 46% agreement between field observations and pixels. However, since the established technique is manual, it could not be compared to the DSMSBS model via cross‐validation. We produced SBS class uncertainty maps, which showed high prediction probabilities around field observations, and low probabilities away from field observations. SBS prediction probabilities could aid post‐fire assessment teams with sample prioritization. We report 107 km2 more area classified as high and moderate SBS compared to the established technique. We conclude that blending soil forming factors based mapping and vegetation burn severity mapping can improve SBS mapping. This represents a shift in SBS mapping away from validating remotely sensed reflectance imagery and toward a quantitative soil landscape model, which incorporates both fire and soils information to directly predict SBS. Core Ideas: A digital soil mapping method to map wildfire soil burn severity (DSMSBS) was developed.Soil field observations were combined with rasters of environmental covariates and fire effects.Excellent fidelity between field observations of soil burn severity (SBS) and the final DSMSBS map was reported.Direct classification of SBS improves SBS mapping compared to validation of remotely sensed burn severity.Class probabilities generated for SBS may aid post‐fire assessment. [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 in mesic areas to alpine grassland at the higher altitudes and in xeric areas. Accurate and detailed mapping of the vegetation distribution on the TP is essential for an improved understanding of climate change effects on terrestrial ecosystems. Yet, existing land cover datasets for the 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 to as TP_LC10-2022) by leveraging state-of-the-art remote-sensing approaches including Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and four machine learning models using the Google Earth Engine platform. Our TP_LC10-2022 dataset achieved an overall classification accuracy of 86.5 % with a kappa coefficient of 0.854. Upon comparing it with four 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, which is ignored in existing land cover datasets, occupied 13.99 % of the TP region, and shrublands, which are characterized by distinct forms (deciduous shrublands and evergreen shrublands) that are largely determined by the topography and are missed in existing land cover datasets, occupied 4.63 % of the TP region. Our dataset provides a solid foundation for further analyses which need accurate delineation of these unique vegetation types in the TP. TP_LC10-2022 and the sample dataset are freely available at 10.5281/zenodo.8214981 and 10.5281/zenodo.8227942 , respectively. Additionally, the classification map can be viewed at https://cold-classifier.users.earthengine.app/view/tplc10-2022 (last access: 6 June 2024). [ABSTRACT FROM AUTHOR]

Climate change is causing major changes in terrestrial ecosystems and biomes around the world. This is particularly concerning in oceanic islands, considered reservoirs of biodiversity, even more in those with a significant altitudinal gradient and high complexity in the vegetation they potentially harbour. Here, in Tenerife (Canary Islands), we have evaluated the changes in potential vegetation belts during the last 20 years by comparing them with a previous study. Considering the intimate linkage between vegetation and climate, we used a methodology based on phytosociological knowledge, ordination techniques and geostatistics, using multivariate spatial interpolations of bioclimatic data. This has allowed us to spatially detect the variations experienced by eight vegetation units during the last 20 years and incorporating a set of vulnerability metrics. New bioclimatic and vegetation cartography are provided according to the current scenario studied (1990–2019). Our results indicate that summit vegetation, humid laurel forest and thermo-sclerophyllous woodland are the habitats that have experienced a very high area loss and mismatch index, strong changes, if we consider that we are only comparing a period of 20 years. Simultaneously, the more xeric vegetation belts, the dry laurel forest and the pine forest would have benefited from this new warmer and drier climate, by gaining area and experiencing strong upward movements. These changes have not been spatially uniform, indicating that the elevational gradient studied not explain completely our results, showing the influence of the complex island topography. Effective landscape management should consider current remnants, transition capacity and movement limitations to better understand current and future vegetation responses in a global change context. [ABSTRACT FROM AUTHOR]

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]

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]

Highly diverse agroecosystems are increasingly of interest as the realization of farms' invaluable ecosystem services grows. Simultaneously, there has been an increased use of uncrewed aerial systems (UASs) in remote sensing, as drones offer a finer spatial resolution and faster revisit rate than traditional satellites. With the combined utility of UASs and the attention on agroecosystems, there is an opportunity to assess UAS practicality in highly biodiverse settings. In this study, we utilized UASs to collect fine-resolution 10-band multispectral imagery of coffee agroecosystems in Puerto Rico. We created land cover maps through a pixel-based supervised classification of each farm and assembled accuracy assessments for each classification. The average overall accuracy (53.9%), though relatively low, was expected for such a diverse landscape with fine-resolution data. To bolster our understanding of the classifications, we interviewed farmers to understand their thoughts on how these maps may be best used to support their land management. After sharing imagery and land cover classifications with farmers, we found that while the prints were often a point of pride or curiosity for farmers, integrating the maps into farm management was perceived as impractical. These findings highlight that while researchers and government agencies can increasingly apply remote sensing to estimate land cover classes and ecosystem services in diverse agroecosystems, further work is needed to make these products relevant to diversified smallholder farmers. [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 world's rangelands and drylands are undergoing rapid change, and consequently are becoming more difficult to manage. Big data and digital technologies (digital tools) provide land managers with a means to understand and adaptively manage change. An assortment of tools—including standardized field ecosystem monitoring databases; web‐accessible maps of vegetation change, production forecasts, and climate risk; sensor networks and virtual fencing; mobile applications to collect and access a variety of data; and new models, interpretive tools, and tool libraries—together provide unprecedented opportunities to detect and direct rangeland change. Accessibility to and manager trust in and knowledge of these tools, however, have failed to keep pace with technological advances. Collaborative adaptive management that involves multiple stakeholders and scientists who learn from management actions is ideally suited to capitalize on an integrated suite of digital tools. Embedding science professionals and experienced technology users in social networks can enhance peer‐to‐peer learning about digital tools and fulfill their considerable promise. [ABSTRACT FROM AUTHOR]

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future. [ABSTRACT FROM AUTHOR]

Environmental classifications are of paramount importance for assessing the impacts of land-use changes, for prioritizing conservation efforts, and for developing effective management strategies to mitigate the negative impacts of human activities. The aim of our research was to provide as complete an analysis as possible of the studies that have been carried out using the Braun-Blanquet approach. The global review of studies based on the Braun-Blanquet approach includes 1168 papers and was conducted using the PRISMA 2009 methodological recommendations, strict criteria for the selection/quality of papers, and modern methods of data analysis and visualization using VOSviewer software developed by Nees Jan van Eck and Ludo Waltman (Centre for Science and Technology Studies (CWTS) of Leiden University in the Netherlands) (version 1.6.18), which ensures a representative sample, minimization of subjective judgements, and reliability of conclusions. It was noted that the number of publications on Braun-Blanquet is growing exponentially. This is an indication of the scientific interest in this methodology and its continuous further development. Based on a detailed analysis of the keywords, the main research directions and challenges are identified. These include improving the conceptual and methodological foundations of the Braun-Blanquet approach; improvement in regional vegetation classifications, synthesizing them and producing a comprehensive classification for large areas as a basis for biodiversity conservation and sustainable land use; expansion of the geography; compilation and updating of databases of phytosociological data; management of dynamics and vegetation; discussion of the important problem of continuity and discreteness of vegetation in the context of ecological classifications; and vegetation mapping. The top 20 journals publishing the most cited articles were identified, as well as the top 20 most cited journals whose high citation rate is due to the large number of high-quality articles. The analysis of the bibliographic network of papers in dynamics has shown that the structure of relationships is not constant and has changed significantly. The analysis of the authors' publication activity showed that the vast majority of researchers have a low publication activity and have published only one to three papers. A peculiarity also emerges: if all the most cited authors are concentrated in Eurasia, then most of the most actively published authors are outside Eurasia. The importance of the Braun-Blanquet approach for the study and classification of forest vegetation should be emphasized. In this case, the Braun-Blanquet approach is integrated into forest typologies, increasing their ecological validity and environmental relevance. [ABSTRACT FROM AUTHOR]

Upland cotton accounts for a high percentage (95%) of the world's cotton production. Plant height (PH) and branch number (BN) are two important agronomic traits that have an impact on improving the level of cotton mechanical harvesting and cotton yield. In this research, a recombinant inbred line (RIL) population with 250 lines developed from the variety CCRI70 was used for constructing a high-density genetic map and identification of quantitative trait locus (QTL). The results showed that the map harbored 8298 single nucleotide polymorphism (SNP) markers, spanning a total distance of 4876.70 centimorgans (cMs). A total of 69 QTLs for PH (9 stable) and 63 for BN (11 stable) were identified and only one for PH was reported in previous studies. The QTLs for PH and BN harbored 495 and 446 genes, respectively. Combining the annotation information, expression patterns and previous studies of these genes, six genes could be considered as potential candidate genes for PH and BN. The results could be helpful for cotton researchers to better understand the genetic mechanism of PH and BN development, as well as provide valuable genetic resources for cotton breeders to manipulate cotton plant architecture to meet future demands. [ABSTRACT FROM AUTHOR]

Accurate and comparable annual mapping is critical to understanding changing vegetation distribution and informing land use planning and management. A U-Net convolutional neural network (CNN) model was used to map natural vegetation and forest types based on annual Landsat geomedian reflectance composite images for a 500 km × 500 km study area in southeastern Australia. The CNN was developed using 2018 imagery. Label data were a ten-class natural vegetation and forest classification (i.e., Acacia, Callitris, Casuarina, Eucalyptus, Grassland, Mangrove, Melaleuca, Plantation, Rainforest and Non-Forest) derived by combining current best-available regional-scale maps of Australian forest types, natural vegetation and land use. The best CNN generated using six Landsat geomedian bands as input produced better results than a pixel-based random forest algorithm, with higher overall accuracy (OA) and weighted mean F1 score for all vegetation classes (93 vs. 87% in both cases) and a higher Kappa score (86 vs. 74%). The trained CNN was used to generate annual vegetation maps for 2000–2019 and evaluated for an independent test area of 100 km × 100 km using statistics describing accuracy regarding the label data and temporal stability. Seventy-six percent of pixels did not change over the 20 years (2000–2019), and year-on-year results were highly correlated (94–97% OA). The accuracy of the CNN model was further verified for the study area using 3456 independent vegetation survey plots where the species of interest had ≥ 50% crown cover. The CNN showed an 81% OA compared with the plot data. The model accuracy was also higher than the label data (76%), which suggests that imperfect training data may not be a major obstacle to CNN-based mapping. Applying the CNN to other regions would help to test the spatial transferability of these techniques and whether they can support the automated production of accurate and comparable annual maps of natural vegetation and forest types required for national reporting. [ABSTRACT FROM AUTHOR]

The study is focused on the study of forest habitats of Svoge Municipality, situated in the western part of the country. The total area of Svoge Municipality is 868 km². 134 relevés were collected during the period of 2015-2019, following the Braun-Blanquet approach and 445 field points were verified, as well. As a result, nine forest habitat types, included in Directive 92/43/EEC, were identified, representing a habitat diverse municipality. They are covering 135.89 km² or 15.7% of the total municipality’s area. The habitat type of the Asperulo-Fagetum beech forests (9130) covers the largest area (57.86 km²), followed by the habitat type of the MedioEuropean limestone beech forests of the Cephalanthero-Fagion (9150) (31.03 km²). The dominance by beech forest is typical for the Western Balkan Range where these types of forests expanded due to climatic and anthropogenic factors, which led to the reduction of the belts, dominated by the common hornbeam, the spruce and the fir. Major threats include forest degradation and loss, because of logging activities, pests, presence of invasive species, quarries and illegal household disposing. Measures, including afforestation with local species and forest regeneration, are needed. [ABSTRACT FROM AUTHOR]

The composition of species, as well as their population and regeneration status of tree species, provide an important ecological foundation for assessing the current state and rate of ecosystem degradation. Therefore, the present study aimed to determine the plant species composition with population and regeneration status of the 40 different tree species in peri-urban vegetation of Ranchi, eastern India using 52 belt transects each of 0.10 ha. A total of 58 different plant species of 49 genera and 26 families were recorded with the majority (86%) of native Indian origin species. The 14% of exotic origin species included dominant invasives in herb, shrub, and tree layers viz., Mesosphaerum suaveolens (L.) Kuntze. (2949 ind. ha−1), Lantana camara L. (1667 ind. ha−1), and Acacia mangium Willd. (33 ind. ha−1), respectively. On the other hand, 59% of the total plant species were IUCN Red-listed including Endangered (Tectona grandis L.f.), Near Threatened (Aegle maremlos (L.) Corrêa), Data Deficient (Mangifera indica L.), and Least Concern (Dalbergia sissoo Roxb. ex DC). Total adult tree density and basal cover were 541 ind. ha−1 and 24.93 m2 ha−1, respectively and Shorea robusta C.F.Gaertn. was the most dominant tree (295 ind. ha−1) recorded in the majority of the studied patches (87%) where adult tree density (ind. ha− 1) ranged from 10 to 1520 with a mean of 363 ± 49 (SE) signifying that the majority of the studied urban vegetation was Sal dominated forest patches. The total density of tree seedlings (22,627 ind. ha−1) was highest in the studied peri-urban vegetation of Ranchi followed by total tree saplings density (2677 ind. ha−1) and adult tree (GBH ≥ 10 cm) density (541 ind. ha−1) signifying good regeneration status (density of seedlings > saplings > adults) of the peri-urban vegetation of Ranchi. was the most common human disturbance observed in 31% of the total studied patches. No regeneration of 56% of the total tree species, the prevalence of human disturbances in the 48% studied patches, and the occurrence of invasives in all three layers of vegetation in the studied peri-urban vegetation signify the ongoing biotic and abiotic pressure on the survival of different plant species at various growth stages. Besides, the predominance of plant species of rare and very rare occurrences (56.9%), IUCN Red-listed (59%) with a total of 84% native Indian origin species demonstrated the importance of the studied peri-urban vegetation and warrants for immediate conservation measures to protect it from further depletion. [ABSTRACT FROM AUTHOR]

A combined multiscale approach using ground, drone and satellite surveys can provide accurate landscape scale spatial mapping and monitoring. We used field observations with drone collected imagery covering 70 ha annually for a 5‐year period to estimate changes in living and dead vegetation of four widespread and abundant arid zone woody shrub species. Random forest classifiers delivered high accuracy (> 95%) using object‐based detection methods, with fast repeatable and transferrable processing using Google Earth Engine. Our classifiers performed well in both dominant arid zone landscape types: dune and swale, and at extremes of dry and wet years with minimal alterations. This highlighted the flexibility of the approach, potentially delivering insights into changes in highly variable environments. We also linked this classified drone vegetation to available temporally and spatially explicit Landsat satellite imagery, training a new, more accurate fractional vegetation cover model, allowing for accurate tracking of vegetation responses at large scales in the arid zone. Our method promises considerable opportunity to track vegetation dynamics including responses to management interventions, at large geographic scales, extending inference well beyond ground surveys. [ABSTRACT FROM AUTHOR]

Temporal-vegetation mapping bearing temporal-related features is important because it helps to understand the global climate changes that drive resource management and habitat conservation. This paper presents a Supervised Normalized Difference Vegetation Index (SNDVI) approach for mapping the vegetation cover in arid environment regions. The NDVI is used to extract features to classify land as a vegetation cover, water body, or bare soil. Through the use of Normalized Difference Vegetation Index (NDVI), regions can be categorized as dry or sandy, based on the soil reflectance values. NDVI is the most commonly deployed index for accurate vegetation cover estimates. The NDVI values lie in a range from -1 to +1, depending on the environmental region and vegetation conditions. It is difficult to assign a specific threshold value to distinguish between vegetation and non-vegetation for all the eco-regions under a specific landscape and ecological conditions. The proposed approach is based on the quantitative verification of the samples as well as the supervised classification method followed to categorize the images. The SNDVI approach has been applied to three different locations in three different seasons in arid ecoregions to extract features for vegetation mapping. The results disclose that SNDVI is a very reliable parameter in extracting true vegetation cover in arid regions. An accuracy evaluation matrix has been performed for each case study and the overall obtained accuracy value ranged from 82% to 100%, depending on the season of the area under investigation. The utility of the proposed method is determined by bench-marking the results with those of the techniques recently utilized by contemporary researchers. [ABSTRACT FROM AUTHOR]

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

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]

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

Mapping the vegetation coverage in a world-famous city provides a reference for tourists to initially experience the city. Luoyang, China, is a world-famous tourist city, and many tourists want relevant information on the vegetation coverage so that they can review the city's greening conditions before travelling. In this study, we used Landsat-8 images from 2021 as the data source to extract the normalized difference vegetation index (NDVI), used the NDVI dimidiate pixel model to obtain vegetation coverage images, removed blue ground objects in the images and conducted classification. Next, transformation analysis was performed of classification maps of different vegetation coverage levels in summer and winter. The results showed that the vegetation coverage area within the built-up area in summer was approximately 243.12 km2 (67.71%), whereas that in winter reached approximately 216.43 km2 (60.27%). Regardless of the season, the higher the vegetation coverage level is, the smaller the occupied area. Compared to those in summer, the types of high vegetation cover decreased in winter, and they shifted towards lower vegetation cover types. According to the experimental results, the vegetation coverage area in Luoyang is very large in summer, and although it slightly decreases in winter, it still occupies a large proportion of the total area. This indicates that tourists can enjoy large-scale vegetation in Luoyang. [ABSTRACT FROM AUTHOR]

Abstract Alpine ecosystems are particularly vulnerable to climate change. Monitoring the distribution of alpine vegetation is required to plan practical conservation activities. However, conventional field observations, airborne and satellite remote sensing are difficult in terms of coverage, cost and resolution in alpine areas. Ground‐based time‐lapse cameras have been used to observe the regions' snowmelt and vegetation phenology and offer significant advantages in terms of cost, resolution and frequency. However, they have not been used in research monitoring of vegetation distribution patterns. This study proposes a novel method for drawing georeferenced vegetation classification maps from ground‐based imagery of alpine regions. Our approach had two components: vegetation classification and georectification. The proposed vegetation classification method uses a pixel time series acquired from fall images, utilizing the fall leaf color patterns. We demonstrated that the performance of the vegetation classification could be improved using time‐lapse imagery and a Recurrent Neural Network. We also developed a novel method to accurately transform ground‐based images into georeferenced data. We propose the following approaches: (1) an automated procedure to acquire Ground Control Points and (2) a camera model that considers lens distortions for accurate georectification. We demonstrated that the proposed approach outperforms conventional methods, in addition to achieving sufficient accuracy to observe the vegetation distribution on a plant‐community scale. The evaluation revealed an F1 score and root‐mean‐square error of 0.937 and 3.4 m in the vegetation classification and georectification, respectively. Our results highlight the potential of inexpensive time‐lapse cameras to monitor the distribution of alpine vegetation. The proposed method can significantly contribute to the effective conservation planning of alpine ecosystems.

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]

Introduction: Plant height (PH) and ear height (EH) are key plant architectural traits in maize, which will affect the photosynthetic efficiency, high plant density tolerance, suitability for mechanical harvesting Methods: QTL mapping were conducted for PH and EH using a recombinant inbred line (RIL) population and two corresponding immortalized backcross (IB) populations obtained from crosses between the RIL population and the two parental lines. Results: A total of 17 and 15 QTL were detected in the RIL and IB populations, respectively. Two QTL, qPH1-1 (qEH1-1) and qPH1-2 (qEH1-4) in the RIL, were simultaneously identified for PH and EH. Combing reported genome-wide association and cloned PH-related genes, co-expression network analyses were constructed, then five candidate genes with high confidence in major QTL were identified including Zm00001d011117 and Zm00001d011108, whose homologs have been confirmed to play a role in determining PH in maize and soybean. Discussion: QTL mapping used a immortalized backcross population is a new strategy. These identified genes in this study can provide new insights for improving the plant architecture in maize. [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]