Vegetation Types Research Articles

Cisuralian plant fossils from Khenifra (Central Morocco): Palaeoenvironmental and palaeobiogeographical reconstructions

This study focuses on newly collected plant fossils from the Cisuralian successions of the Khenifra Basin in Central Morocco, revealing a diverse assemblage dominated by conifers, pteridosperms, and sphenophytes. The assemblage includes eleven taxa of vegetative and reproductive organs preserved as compressions and impressions. Two pteridosperm species taxa (Neurodontopteris auriculata, Odontopteris subcrenulata) are recorded for the first time from Central Morocco, whereas other taxa are known from the early Permian deposits of Bou Achouch and Tiddas basins. Contrary to previous interpretations, the flora is does not exhibit a mixed character, but instead exhibits a distinct Euramerican affinity, despite the absence of the widespread callipterids Autunia conferta and A. naumannii. These similarities between this flora and coeval Euramerican floras highlight the significant biogeographical connections across Pangea during the Cisuralian. The plant fossil assemblage is dominated by meso- to xerophilous species adapted to drought, with rare hygrophilous elements adapted to humid habitats, reflecting a heterogeneous landscape with various vegetation types.

Drone and ground-truth data collection, image annotation and machine learning: A protocol for coastal habitat mapping and classification

Aerial drone imaging is an efficient tool for mapping and monitoring of coastal habitats at high spatial and temporal resolution. Specifically, drone imaging allows for time- and cost-efficient mapping covering larger areas than traditional mapping and monitoring techniques, while also providing more detailed information than those from airplanes and satellites, enabling for example to differentiate various types of coastal vegetation. Here, we present a systematic method for shallow water habitat classification based on drone imagery. The method includes:•Collection of drone images and creation of orthomosaics.•Gathering ground-truth data in the field to guide the image annotation and to validate the final map product.•Annotation of drone images into – potentially hierarchical – habitat classes and training of machine learning algorithms for habitat classification.As a case study, we present a field campaign that employed these methods to map a coastal site dominated by seagrass, seaweed and kelp, in addition to sediments and rock. Such detailed but efficient mapping and classification can aid to understand and sustainably manage ecologically and valuable marine ecosystems.

Riparian forests shape trophic interactions in detrital stream food webs

Abstract Freshwater and terrestrial biodiversity are linked through spatial resource flows. Key examples are detrital subsidies from the riparian vegetation that form the base of food webs in small streams. Despite the central role of detritivores in these food webs, the consequences of altered riparian vegetation type and resource availability on their trophic strategies are less known. Therefore, we experimentally tested direct and indirect effects of riparian vegetation type (i.e. riparian forests present vs. absent) on trophic interactions and dietary imbalances of aquatic detritivores. To characterise trophic strategies, we used stoichiometric and isotopic differences between consumers and resources as functional measures of trophic link strength in leaf litter bags naturally colonised by stream decomposers and detritivores. Our results show that an absence of riparian forests affected stoichiometric and isotopic properties in stream detrital food webs. Where local riparian forests were absent, leaf litter showed lower C:N ratios, which only trichopteran detritivores mirrored, whereas plecopteran and crustacean detritivores showed differences in their isotopic signatures. Diverging patterns between resources and consumers could lead to a rewiring of energy flow paths and weaken the coupling between aquatic detritivores and terrestrial detritus. Consequently, our findings demonstrate that riparian forests are essential for aquatic food webs by not only influencing organisms themselves but also their trophic interactions and energy flow paths. Aquatic detritivores depending on terrestrial subsidies can be highly sensitive to local changes in their resource environment. Therefore, functionally divergent patterns between resources and consumers in recipient systems highlight how habitat properties of the donor system can affect food webs connected across ecosystem boundaries. Read the free Plain Language Summary for this article on the Journal blog.

Leaf Functional Traits and Their Influencing Factors in Six Typical Vegetation Communities.

Leaf functional traits (LFTs) have become a popular topic in ecological research in recent years. Here, we measured eight LFTs, namely leaf area (LA), specific leaf area (SLA), leaf thickness (LT), leaf dry matter content (LDMC), leaf carbon content (LCC), leaf nitrogen content (LNC), leaf phosphorus content (LPC), and leaf potassium content (LKC), in six typical vegetation communities (sclerophyllous evergreen broad-leaved forests, temperate evergreen coniferous forests, cold-temperate evergreen coniferous forests, alpine deciduous broad-leaved shrubs, alpine meadows, and alpine scree sparse vegetation) in the Chayu River Basin, southeastern Qinghai-Tibet Plateau. Our aim was to explore their relationships with evolutionary history and environmental factors by combining the RLQ and the fourth-corner method, and the method of testing phylogenetic signal. The results showed that (i) there were significant differences in the eight LFTs among the six vegetation communities; (ii) the K values of the eight LFTs were less than 1; and (iii) except for LCC, all other LFTs were more sensitive to environmental changes. Among these traits, LA was the most affected by the environmental factors, followed by LNC. It showed that the LFTs in the study were minimally influenced by phylogenetic development but significantly by environmental changes. This study further verified the ecological adaptability of plants to changes in environmental factors and provides a scientific basis for predicting the distribution and diffusion direction of plants under global change conditions.

The Effectiveness of Public Green Open Space Capabilities in Reducing Flooding

The Palopo City watershed could not accommodate the high discharge of rainfall that had fallen previously. The Green Open Space (GOS) had an ecological function by absorbing rainwater. Urban green open spaces absorbed surface flow water in the past, reducing the risk of flooding. In the past, this research measured the effectiveness of three green open spaces (Islamic Center, Pancasila Field, and Salubulo Field) in reducing flooding in Palopo City. We collected data on vegetation types, infiltration capacity, and runoff discharge. Data analysis involved the analysis of infiltration capacity (F), runoff discharge (Q), and green open space effectiveness. In the three green space locations, the following vegetation types were found: Katapang (Terminalia catappa), Palm (Dypsis lutescens), Trambesi (Samanea Saman), and Grass (Cyperus rotundus). The highest total infiltration capacity in the Salubulo Field Green Space resulted in 155 m3/hour (fast category). In the past, the most significant runoff discharge's total value was GOS Pancasila Field: 713.4 mm3/minute. The study concluded that the Salubulo Field Green Space had the highest infiltration capacity (160 m3/hour) and runoff discharge (69 mm3/minute), making it the most effective in reducing flooding in Palopo City in the past. This was > 1 (effectiveness criteria achieved), and the space was dominated by vegetation (trees and grasses).

Mapping plant communities of the Karoo National Park, South Africa, using Sentinel-2 and topo-morphological data

AimsThis study aimed at classifying, mapping and describing the plant communities of the Karoo National Park using floristic surveys in conjunction with Sentinel-2 and topo-morphological data. Study areaKaroo National Park, Western Cape, South Africa. MethodsThe vegetation of the Karoo National Park was delineated into homogenous physiognomic-physiographic units using Sentinel-2 images. A total of 128 survey plots (100 m2 each) were surveyed within the different homogeneous units during the period 2016 to 2020. Within each survey plot, all rooted species were identified and their cover abundance estimated. Each plot was photographed and its geolocation recorded. The floristic data were captured using the Braun Blanquet Personal Computer suite and exported to the JUICE Software programme. A modified TWINSPAN classification was done to derive a first tabled synopsis of the plant communities. The different plant communities were subsequently classified and described according to their diagnostic and dominant species gleaned from the synoptic table. Species richness was determined by counting the number of different species per plant community while the Shannon–Wiener Index and Rich-Gini–Simpson Index of diversity (D) were used to derive indices of species diversity per plant community. Results12 major communities and two sub-communities that are distinctly linked to various abiotic factors were identified, described and mapped. The higher-lying rocky steep midslopes as well as the valley bottomland areas had the highest diversity and species richness. ConclusionsThis study proves the efficacy of using Sentinel-2 and topo-morphological data in classification, description and mapping vegetation of extensive natural areas. The vegetation map and classification of plant communities provide a baseline to inform management decisions.Taxonomic reference: SA-Plant Checklist-2019–2020, South African National Biodiversity Institute, 2020, Botanical Database of Southern Africa (BODATSA) (http://posa.sanbi.org/) [accessed January 2022].

Spatial and Temporal Dynamics in Vegetation Greenness and Its Response to Climate Change in the Tarim River Basin, China

Vegetation in ecologically sensitive regions has experienced significant alterations due to global climate change. The underlying mechanisms remain somewhat obscure owing to the spatial heterogeneity of influencing factors, particularly in the Tarim River Basin (TRB) in China. Therefore, this study targets the TRB, analyzing the spatial and temporal dynamics of vegetation greenness and its climatic determinants across multiple spatial scales. Utilizing Normalized Difference Vegetation Index (NDVI) data, vegetation greenness trends over the past 23 years were assessed, with future projections based on the Hurst exponent. Partial correlation and multiple linear regression analyses were employed to correlate NDVI with temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET), elucidating NDVI’s response to climatic variations. Results revealed that from 2000 to 2022, 90.1% of the TRB exhibited an increase in NDVI, with a significant overall trend of 0.032/decade (p < 0.01). The difference in NDVI change across sub-basins and vegetation types highlighted the spatial disparity in greening. Notable greening predominantly occurred near rivers at lower elevations and in extensive cropland areas, with projections indicating continued greening in some regions. Conversely, future trends mainly suggested a shift towards browning, particularly in higher-elevation areas with minimal human influence. From 2000 to 2022, the TRB experienced a gradual increase in TMP, PRE, and PET. The latter two factors were significantly correlated with NDVI, indicating their substantial role in greening. However, vegetation sensitivity to climate change varied across sub-basins, vegetation types, and elevations, likely due to differences in plant characteristics, hydrothermal conditions, and human disturbances. Despite climate change influencing vegetation dynamics in 51.5% of the TRB, its impact accounted for only 25% of the total NDVI trend. These findings enhance the understanding of vegetation ecosystems in arid regions and provide a scientific basis for developing ecological protection strategies in the TRB.

Climate Change and Vegetation Greening Jointly Promote the Increase in Evapotranspiration in the Jing River Basin

Within the Earth’s terrestrial environment, evapotranspiration significantly contributes to the hydrological cycle, accounting for around 80% of the precipitation on landmasses to be reintroduced into the atmosphere. This mechanism profoundly affects the distribution and availability of surface water resources throughout the ecosystem. Gaining insight into the factors influencing local evapotranspiration fluctuations in response to varying climatic and vegetative scenarios is crucial for effective water management strategies and rehabilitating ecosystem resilience. To this end, our study focuses on the Jing River Basin in the Loess Plateau, utilizing multi-source remote sensing data and climatic information to investigate the spatiotemporal dynamics of evapotranspiration from 1984 to 2018 through the application of the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model. Our research results indicate a general ascending tendency in evapotranspiration across the investigated region, demonstrating a notably discernible escalation at a pace of approximately 3.11 mm/year (p < 0.01), with an annual vegetation ET volume reaching 533.88 mm. Across different vegetation types in the Jing River Basin between 1984 and 2018, the mean yearly ET was observed to be highest in forests (572.88 mm), followed by croplands (564.74 mm), shrublands (536.43 mm), and grasslands (503.42 mm). The leaf area index (LAI) demonstrated the strongest partial correlation with ET (r = 0.35) and contributed the most significantly to the variation in ET within the Jing River Basin (0.41 mm/year). Additionally, LAI indirectly influences ET through its impact on vapor pressure deficit (VPD), precipitation (Pre), and temperature (Temp). Radiation is found to govern most ET changes across the region, while radiation and precipitation notably affected ET by modulating air temperature. In summary, these radiant energy changes directly affect the evaporation rate and total evapotranspiration of surface water. It provides important support for understanding how evapotranspiration in the Jing River Basin is adjusting to climate change and increased vegetation cover. These findings serve as a theoretical foundation for devising sustainable vegetation restoration strategies to optimize water resource utilization within the region.

Drought sensitivity analysis of meteorological and vegetation indices in Dak Nong, Vietnam

ABSTRACT Drought poses an increasing threat to agricultural sustainability in Dak Nong, Vietnam. This study investigates the sensitivity of meteorological and vegetation indices (VIs) to drought conditions in the region. We analyzed 23 years (2000–2022) of data from 11 meteorological stations and remote sensing imagery covering various crop types. Our methodology involved correlating multiple VIs (VHI, VCI, TCI, and TVDI) with meteorological drought indices (SPI and SPEI) at different time scales. Results reveal a strong correlation (r > 0.7, p < 0.001) between the vegetation health index (VHI) and the standardized precipitation evapotranspiration index (SPEI), particularly at a 4-month time scale (SPEI4). This combination proved most effective for drought monitoring across diverse vegetation types. Spatial analysis identified drought-sensitive zones covering 10.8% of the province, with steep terrain and limited river density. These areas, predominantly occupied by perennial agriculture, annual crops, and production forests, show heightened vulnerability to water scarcity. Our findings provide a scientific basis for targeted drought management strategies, including establishing an early warning system using SPEI4 and VHI, implementing water-efficient agricultural practices, and prioritizing farmer support in high-risk areas. This study enhances drought resilience and sustainable water resource management in Dak Nong and similar tropical regions.

Aboveground biomass inversion of forestland in a Jinsha River dry-hot valley by integrating high and medium spatial resolution optical images: A case study on Yuanmou County of Southwest China

It is crucial to develop a comprehensive method for estimating the aboveground biomass (AGB) of trees, shrubs, grasslands, and sparse tree areas in ecologically fragile dry, hot valley regions with vertical zonation. Multi-source remote-sensing data can fulfill this requirement, providing help in monitoring the health of ecosystems and providing a basis for regional biodiversity conservation and restoration. Sentinel-2A satellite imagery was used to classify the forests, shrubs, and grasslands in Yuanmou County, Chuxiong Yi Autonomous Prefecture, Yunnan Province, China. The Gaofen-2 satellite (GF-2) was used to extract the canopy width and calculate tree biomass in the valley-type savanna region. These data were combined with remote-sensing factors and measured survey data, and random forest (RF) and extreme gradient boosting (XGBoost) models were used to estimate the biomass. Using GF-2 images to segment sparse tree areas effectively reduced the overestimation of low-resolution remote-sensing images, enabling the AGB of sparse trees to be accurately estimated. The biomass estimations based on the Sentinel-2A images attained coefficient of determination (R2) values of 0.45 and 0.47 for the forest, 0.55 and 0.61 for the shrubs, and 0.32 and 0.37 for the grasslands using RF and XGBoost models, respectively, demonstrating variable effectiveness across vegetation types. In addition, the XGBoost model was more robust than the RF model for all three vegetation types. Our methodology provides scientific support for the sustainable development of ecologically fragile dry, hot valleys and savanna areas.

Introduction pages, Nova Geodesia 4(3), 2024

Nova Geodesia (https://novageodesia.ro), Issue 3, Volume 4, 2024: The papers published in this issue represent interesting novelties in different topics of geodetic science or related fields. Nova Geodesia publishes significant papers in geodesy and close topics related to cartography, engineering projects and construction, planetology, hydrography, geography, sociodemographic factors, urban administration, planning and environment, landscape, biodiversity, and ecology. Among the exciting articles, we invite readers to find news about: Forest sampling techniques in different types of vegetation applying plot sampling, non-plot sampling, and remote sensing; Soil surface erosion susceptibility analysis using the USLE model. Case study: Bran - Dragoslavele Corridor, Romania; Feeding butterflies with Tridax procumbens (L.), a beneficial plant for maintaining butterfly populations; Impacts of climate-smart soil and water conservation practices and slope gradient on selected soil chemical properties in Eastern Ethiopia: A case study of the Kulkullessa Sub-watershed, Goro Gutu District; Comparative assessment of different rose cultivars under environmental conditions in central Transylvania, Romania; Plants associated with water towers in the Saharan environment: Floristic diversity and therapeutic virtues.

Characterization of Diarrheagenic Escherichia coli and Salmonella enterica from Produce in the Chobe District of Botswana

Diarrheal disease is a leading cause of death in children in low- and moderate-income countries. Fresh produce, including fruits and vegetables, may harbor diarrheal disease-causing bacteria including strains of Salmonella enterica and Escherichia coli. This study aimed to determine the prevalence and antibiotic resistance profiles of S. enterica and E. coli isolated from produce samples (n = 207) obtained from retail markets in northern Botswana in Chobe District of Botswana in 2022. Samples were enriched in the appropriate selective media: Brilliant Green Bile Broth for E. coli and Rappaport Vassiliadis Broth for S. enterica. E. coli were confirmed by PCR detecting the phoA gene, and classified as potentially pathogenic through screening for the eae, stx, and stx2 and estIb genes. S. enterica isolates were confirmed using invA primers. Isolates were evaluated for resistance to ampicillin, amoxicillin-clavulanic acid, chloramphenicol, cefotaxime, doxycycline, streptomycin, sulfamethoxazole, and tetracycline antibiotic using the Kirby-Bauer Disk Diffusion method. E. coli was isolated from 15.5% of produce samples (n = 207). The gene eae was detected from 1.5% of samples, while stx1, stx2, and estIb were not detected. Resistance to one or more antibiotics was common (72%) with the majority of the resistant E. coli (n = 32) isolated from fruits (22%) and greens (18%) compared to other types of vegetables. Multidrug resistance (MDR, resistant to 3 or more antibiotics) was identified in 18% of samples. S. enterica was isolated from 3.4% of produce samples (7, n = 207). Resistance was uncommon among the S. enterica isolates (1/7). Overall prevalence of diarrheagenic S. enterica and E. coli was low; however, their presence and that of MDR E. coli in foods commonly consumed raw increases the risk to vulnerable populations. Strategies to reduce contamination of fresh produce and public education on washing and cooking some types of produce may be useful to reduce disease.

Impact of vegetation albedo on the habitability of Earth-like exoplanets

ABSTRACT Vegetation can modify the planetary surface albedo via the Charney mechanism, as plants are usually darker than the bare surface of the continents. We updated ESTM (Earth-like surface temperature model) to incorporate the presence, distribution and evolution of two dynamically competing vegetation types that resemble grasslands and trees (the latter in the double stages of life: adults and seedlings). The newly developed model was applied to estimate how the climate-vegetation system reaches equilibrium across different rocky planetary configurations, and to assess its impact on temperature and habitability. With respect to a world with bare granite continents, the effect of vegetation-albedo feedback is to increase the average surface temperature. Since grasses and trees exhibit different albedos, they affect temperature to different degrees. The ultimate impact on climate depends on the outcome of the competition between these vegetation types. The change in albedo due to vegetation extends the habitable zone and enhances the overall planetary habitability beyond its traditional outer edge. This effect is especially relevant for planets that have a larger extension of continents than Earth. For Earth, the semimajor axis d = 1.04 au represents the turning point where vegetation enhances habitability from h = 0.0 to 0.485 (in the grass-dominance case), to h = 0.584 (in the case of coexistence between grasses and trees), and to h = 0.612 (in the tree-dominance case). This illustrates the transition from a snowball state to a planet with intermediate habitability at the outer edge of the circumstellar habitability zone.

Impacts of forest canopy heterogeneity on plot-scale hydrometeorological variables - Insights from an experiment in the humid boreal forest with the Canadian Land Surface Scheme

High latitude regions, including the circumpolar boreal biome, are experiencing important changes in the availability of usable surface water because of climate change. In this context, an adequate representation of the land-atmosphere interaction is critical to ensure optimal management of current and future water resources, forest management, and climate prediction. However, the task is particularly intricate in high-latitude boreal forest, as land surface model faces several challenges due to the unique environmental conditions and ecological characteristics. The objective of this study is to quantify the impact of forest landscape heterogeneity, specifically stand leaf-area index (LAI), soil texture, and drainage regime, on surface water and energy balance in a small boreal high-latitude sub-catchment. To this end, hydrometeorological conditions at seventeen 20×20 m plots in a 1-km2 boreal forest sub-basin are simulated using the Canadian Land Surface Scheme (CLASS), a land surface model, at the point scale. The subplot-scale soil texture, drainage regime, and vegetation characteristics and type are based as closely as possible on field measurements and observations for the 17 plots. The model-driven experiment comprises two sets of simulations using CLASS, each employing the same model setup and run for the 17 experimental plots. The main set employs meteorological forcing from a local micrometeorological tower within the sub-basin to investigate the plot-to-plot variability of albedo, energy fluxes, and soil state variables. A second set of simulations is conducted using meteorological forcing from the ERA5-Land reanalysis, which spans from 1986 to 2022. This data provides a longer time series, enabling a more accurate representation of the interannual climatic variability in the sub-basin. The results of the main and secondary sets of CLASS simulations are used to assess the plot-to-plot and temporal variability of several key hydrometeorological variables by calculating a monthly spread. In brief, the following conclusions and broader implications can be drawn from the findings: i) The simulated total annual evapotranspiration remains relatively uniform between plots despite notable variation in its partitioning from plot to plot. ii) In the presence of a full snowpack, the albedo exhibits substantial heterogeneity at the subplot scale, linked to the canopy's LAI. iii) Local soil properties, drainage regime, and vegetation structure and type exhibit substantial influence on the plot-to-plot variability in soil water content. iv) When parameterized with localized observations and measurements, CLASS can represent and be responsive to the complex dynamics of energy and water fluxes at the plot scale within the heterogeneous surface of boreal forests.

Bat Conservation Strategy In Duasudara Mountain Nature Reserve As Ecotourism Potential Of North Sulawesi, Indonesia

Sulawesi is a geologically complex island with unique fauna, including significant bat populations. This study aimed to identify bat species diversity, endemism, conservation status, and recommend conservation strategies in the Gunung Duasudara Nature Reserve. The research, conducted from May to August 2018 using mist nets across seven vegetation types, recorded 15 bat species with 475 individuals. Notable species include Acerodon celebensis, Boneia bidens, and Thoopterus nigrescens, categorized as near threatened by the IUCN. Newly discovered species M. tailiniensis, R. tangkokoensis, and T. tailiniensis exhibit distinct characteristics. The study's analyses suggest that maintaining the Nature Reserve’s status is crucial for its development as a center for education, research, and ecotourism, emphasizing the need for supportive regional policies and stakeholder involvement to ensure sustainable conservation efforts.

Soil desiccation in the context of vegetation restoration: A scientometric analysis

AbstractThis research aims to fill the gap in understanding the evolution of soil desiccation studies, focusing on its impact on sustainable crop production in China's Loess Plateau. The study explores trends, influential works, authors, journals, and emerging research areas within the field of soil desiccation. To achieve this, this study pioneers a comprehensive scientometric exploration of soil desiccation. The study analyzed 1952 publications on soil desiccation from January 2013 to December 2023, sourced from the Web of Science. Various co‐citation techniques such as author co‐citation analysis (ACA), document co‐citation analysis (DCA), journal co‐citation analysis (JCA), and keyword analysis are employed. Network development, visualization tools, and text‐mining methods are utilized to discern patterns and relationships within the dataset. Here, for the first time, nine distinct clusters were identified through DCA. The findings reveal the impactful emergence of significant clusters, such as the one focusing on the Loess Plateau, marking a significant advance in our understanding of soil desiccation. Multiple research frontiers or sub‐specialties emerge, including topics such as different vegetation types, the impact on grain yield, water use efficiency, growth, soil water content, hydraulic redistribution, drought, soil moisture, and modeling. This innovative scientometric review of soil desiccation outlines the current scientific and technological trends while identifying knowledge gaps. The findings are beneficial for researchers, graduate students, and professors interested in understanding research trajectories, pivotal publications, and influential scholars shaping this field.

Analysing the capacity of multispectral indices to map the spatial distribution of potential post-fire soil losses based on soil burn severity

The area burned in Spain exceeded historical records in 2022, when exceptionally warm conditions influenced wildfire events. The predicted intensification of wildfire regimes includes an increase in frequency, severity, and size. Therefore, a study of the wildfires that occurred in 2022 is necessary to understand their behaviour and possible environmental impacts. The objective of this study is to analyse the applicability of using spectral indices and Geographic Information System (GIS) approaches to map the spatial distribution and estimate potential soil losses using Sentinel-2 imagery and fire severity field data. Soil losses were estimated using an empirical model based on soil burn severity data collected in the field after wildfire. The relationship between the Normalized Difference Infrared Index (NDII), Difference Normalized Wildfire Ash Index (dNWAI), and the Blue Normalized Difference Vegetation Index (BNDVI) with the estimated soil losses was then evaluated. In addition, the influence of different time scales of the satellite images was analysed. The first period considered (Date I) ranges from 8 to 20 days after the beginning of the wildfire, which coincides with the field data collection. The second period considered (Date II) ranges from 28 to 35 days after the start of the wildfire. The results obtained showed a significant dependence relationship between the BNDVI index (using satellite images of Date I) and the estimated soil losses (R2 = 0.756), while the results of the NDII (R2 = 0.31) and dNWAI (R2 = 0.061), showed no spatial relationship with the estimated soil losses. Three of the largest wildfires in 2022 in Spain were analysed, and the results showed strong correlations of BNDVI index for Folgoso do Courel (R2 = 0.808), for Carballeda de Valedorras (R2 = 0.906), and for Sierra de la Culebra (R2 = 0.939). In addition, these results allowed the mapping and quantification of potential soil losses in areas where fire severity was high, totalling ∼2,50,000 Mg ha−1 in Folgoso do Courel, ∼3,70,000 Mg ha−1 in Carballeda de Valdeorras, and ∼4,70,000 Mg ha−1 in Sierra de la Culebra. Moreover, BNDVI values for estimating soil loss vary by vegetation type, and there is a positive correlation between severity classes and the BNDVI index. This approach can inform post-fire land management decisions in future wildfires and could be applied to other regions.

A novel AMSR2 retrieval algorithm for global C-band vegetation optical depth and soil moisture (AMSR2 IB): Parameters' calibration, evaluation and inter-comparison

Effective monitoring of soil and vegetation properties on a global scale is essential for better understanding climate changes, hydrological dynamics, and ecological processes. Passive microwave remote sensing at C-band radio frequency, with long observation period and relatively high penetration capability, has been widely used to retrieve soil moisture (SM) and vegetation optical depth (C-VOD). The retrieval process is generally achieved by inversion of the τ-ω radiative transfer model, which depends on crucial parameters such as effective scattering albedo (ω) and soil roughness (HR) for accurate retrievals. Current SM/C-VOD retrieval algorithms, such as the Land Parameter Retrieval Model (LPRM), predominantly rely on globally-constant ω and HR values, ignoring the inherent sensitivity of those parameters to varying soil conditions and vegetation types. To evaluate the impact of ω and HR variables on SM and C-VOD retrievals and to improve their accuracy, this study proposed and evaluated a novel retrieval approach from AMSR2 C-band observations during 2017–2020 using the C-band Microwave Emission of the Biosphere (C-MEB) model. We evaluated two new retrieval algorithms, considering either a globally-constant calibration or a land cover-based calibration of ω and HR. As a benchmark for the calibration, we optimized the values of ω and HR by evaluating the retrieved SM against in situ measurements from the International Soil Moisture Network (ISMN) and OzNet hydrological monitoring networks. The main originality compared to previous algorithms is that i) it includes a comprehensive calibration exploring the optimal values of ω and HR, applicable globally or tailored to specific land cover; ii) field SM measurements were leveraged to constrain the calibrated value of ω and HR.For the globally-constant calibration, the optimal values of ω = 0.05 and HR = 0.1 were found to yield the best results. For the land cover-based calibration, an inverse relationship between ω/HR and canopy height was observed, with ω ranging from 0.04 to 0.06 and HR ranging from 0.1 to 0.7 for heights between 0 and 30 m. The algorithm employing a land cover-based calibration (INRAE Bordeaux 2, IB2) exhibited better performance than the one utilizing a globally-constant calibration (INRAE Bordeaux 1, IB1) in evaluating retrieved SM against in situ measurements, as well as in evaluating C-VOD vs various vegetation variables including aboveground biomass (AGB), tree cover, canopy height and several optical vegetation indices. Comparison with LPRM suggested that our IB2 C-VOD retrievals present improved performances in terms of both spatial and temporal results with all considered vegetation variables (spatial correlation (R) between various vegetation variables and C-VOD of 0.76–0.83 for IB2 vs 0.69–0.79 for LPRM), and exhibited lower saturation effects when compared with AGB. In addition, the IB2 SM produced lower root mean squared error (RMSE) (0.147 vs 0.217 m3/m3), bias (−0.03 vs 0.09 m3/m3), and ubRMSE (0.066 vs 0.067 m3/m3) when compared with in situ measurements, although it showed a lower R compared to LPRM SM.

Remote sensing reveals trends in vegetative recovery and land cover transformation post-reclamation at tar creek superfund site

The harmful effects of mining waste on human and ecosystem health make reclamation of former mine sites an environmental management priority. However, field-based monitoring of reclamation requires significant investments of time, labor, and money. Remote sensing offers a less expensive alternative to field-based monitoring, but work is still needed to determine which metrics can be reliably estimated using remote sensing techniques. This study uses remote sensing to examine over 20 years of reclamation efforts at Tar Creek Superfund Site and to assess revegetation after site restoration. Using the Normalized Difference Vegetation Index (NDVI), we quantify key factors affecting vegetation recovery and stability after reclamation across 123 surface mining waste cleanup locations within Tar Creek. Leveraging long-term imagery from Landsat and high-resolution PlanetScope imagery, we combine time series analysis of vegetation regrowth and landcover change detection for a comprehensive picture of recovery at each site. Across all reclamation sites, the average recovery duration was 3.5 years, and average recovery rate was 0.1 NDVI year−1. After vegetative growth had plateaued, reclaimed sites had an average NDVI of 0.70. All reclamation areas were converted from majority barren landcover to vegetated landcover classes after reclamation, and vegetative stability at reclamation sites was high (70% of reclaimed area saw continuous vegetative cover from 2017 to 2023). These results demonstrate a strong potential for multi-method remote sensing techniques in tracking and explaining vegetation recovery after reclamation and represent a cost-effective approach for real-time monitoring of reclamation progress and outcomes.

Unaccounted for nonforest vegetation loss in the Brazilian Amazon

The Brazilian Amazon Satellite Monitoring Program (PRODES Amazonia) tracks forest loss in the Brazilian Amazon but excludes about 6.6% ( ~ 280,000 km²) of non-forest vegetation (NF). Here we developed the PRODES NF system to address this gap by adapting the well-established methodology of PRODES Amazonia. Initial findings from PRODES NF show that the Brazilian Amazon lost 10.46% ( ~ 30,000 km²) of NF area, mainly in the last two decades, with the estates of Mato Grosso, Roraima, and Amapá being primary hotspots of losses. Savannas were the most affected (13.3% of their extent). NF loss strongly correlates (r = 0.87; p < 0.0001) with deforestation, suggesting a continuum of vegetation loss in the biome regardless of the predominant vegetation type. Combining data from PRODES Amazonia and PRODES NF reveals an official estimate of ~798,000 km² in total primary vegetation loss ( ~ 19% of the entire biome) in the Brazilian Amazon by 2022.