Search Results (203)

The use of Uncrewed Aerial Systems (UASs) for remote sensing applications has increased significantly in recent years due to their low cost, operational flexibility, and rapid advancements in sensor technologies. In many cases, UAS platforms are considered viable alternatives to conventional satellite and crewed airborne platforms, offering very high spatial, spectral, and temporal resolution data. However, the radiometric quality of UAS-acquired data has not received equivalent attention, particularly with respect to absolute calibration. In this study, we (1) evaluate the absolute radiometric performance of two commonly used UAS sensors: the Headwall Nano-Hyperspec hyperspectral sensor and the MicaSense RedEdge-MX Dual Camera multispectral system; (2) assess the effectiveness of the Empirical Line Method (ELM) in improving the radiometric accuracy of reflectance products generated by these sensors; and (3) investigate the influence of calibration target characteristics—including size, material type, reflectance intensity, and quantity—on the performance of ELM for UAS data. A field campaign was conducted jointly by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the USGS National Uncrewed Systems Office (NUSO) from 15 to 18 July 2023, at the USGS EROS Ground Validation Radiometer (GVR) site in Sioux Falls, South Dakota, USA, over a 160 m × 160 m vegetated area. Absolute calibration accuracy was evaluated by comparing UAS sensor-derived reflectance to in situ measurements of the site. Results indicate that the Headwall Nano-Hyperspec and MicaSense sensors underestimated reflectance by approximately 0.05 and 0.015 reflectance units, respectively. While the MicaSense sensor demonstrated better inherent radiometric accuracy, it exhibited saturation over bright targets due to limitations in its automatic gain and exposure settings. Application of the ELM using just two calibration targets reduced discrepancies to within 0.005 reflectance units. Reflectance products generated using various target materials—such as felt, melamine, or commercially available validation targets—showed comparable agreement with in situ measurements when used with the Nano-Hyperspec sensor. Furthermore, increasing the number of calibration targets beyond two did not yield measurable improvements in calibration accuracy. At a flight altitude of 200 ft above ground level (AGL), a target size of 0.6 m × 0.6 m or larger was sufficient to provide pure pixels for ELM implementation, whereas smaller targets (e.g., 0.3 m × 0.3 m) posed challenges in isolating pure pixels. Overall, the standard manufacturer-recommended calibration procedures were insufficient for achieving high radiometric accuracy with the tested sensors, which may restrict their applicability in scenarios requiring greater accuracy and precision. The use of the ELM significantly improved data quality, enhancing the reliability and applicability of UAS-based remote sensing in contexts requiring high precision and accuracy. Full article

The Advanced Very High-Resolution Radiometer (AVHRR) onboard NOAA-7 through NOAA-19 satellites has been the primary data source for two Climate Data Records (CDRs) that were developed specifically for Arctic and Antarctic studies: the AVHRR Polar Pathfinder (APP) and Extended AVHRR Polar Pathfinder (APP-x). With the decommissioning of these satellites and the loss of the AVHRR, a method for extending the CDRs with the Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA’s recent satellites is presented. The goal is to produce long-term, continuous, consistent, and traceable CDRs for polar climate research. As a result, APP and APP-x can now be continued as the VIIRS Polar Pathfinder (VPP) and Extended VIIRS Polar Pathfinder (VPP-x) CDRs. To ensure consistency, a VIIRS Global Area Coverage (VGAC) dataset that is comparable to AVHRR GAC data was used to develop an analogous VIIRS Polar Pathfinder suite. Five VIIRS bands (I1, I2, M12, M15, and M16) were selected to correspond to AVHRR Channels 1, 2, 3b, 4, and 5, respectively. A multivariate regression approach was used to intercalibrate these VIIRS bands to AVHRR channels based on data from overlapping AVHRR and VIIRS observations from 2013 to 2018. The data from 2012 and 2019 were reserved for independent validation. For the Arctic region north of 60°N at 14:00/04:00 Local Solar Time (LST) during 2012–2019, mean biases between APP and VPP composites at a spatial resolution of 5 km are −0.85%/3.03% (Channel 1), −1.22%/3.65% (Channel 2), −0.18 K/0.81 K (Channel 3b), 0.01 K/0.24 K (Channel 4), and 0.07 K/0.19 K (Channel 5). Mean biases between APP-x and VPP-x at a spatial resolution of 25 km for the same region and period are −1.52%/−1.48% for surface broadband albedo, 0.69 K/0.61 K for surface skin temperature, and −0.011 m/−0.017 m for sea ice thickness. Similar results were observed for the Antarctic region south of 60°S at 14:00/02:00 LST, indicating strong agreement between APP and VPP, and between APP-x and VPP-x. Full article

Retrieving LST from infrared spectral observations is challenging because it needs separation from emissivity in surface radiation emission, which is feasible only when the state of the surface–atmosphere system is known. Thanks to its high spectral resolution, the Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard Metop polar-orbiting satellites is the only sensor that can simultaneously retrieve LST, the emissivity spectrum, and atmospheric composition. Still, it cannot penetrate thick cloud layers, making observations blind to surface emissions under cloudy conditions, with surface and atmospheric parameters being flagged as voids. The present paper aims to discuss a downscaling–fusion methodology to retrieve LST missing values on a spatial field retrieved from spatially scattered IASI observations to yield level 3, regularly gridded data, using as proxy data LST from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) flying on Meteosat Second Generation (MSG) platform, a geostationary instrument, and from the Advanced Very High-Resolution Radiometer (AVHRR) onboard Metop polar-orbiting satellites. We address this problem by using machine learning techniques, i.e., Gradient Boosting, Random Forest, Gaussian Process Regression, Neural Network, and Stacked Regression. We applied the methodology over the Po Valley region, a very heterogeneous area that allows addressing the trained models’ robustness. Overall, the methods significantly enhanced spatial sampling, keeping errors in terms of Root Mean Square Error (RMSE) and bias (Mean Absolute Error, MAE) very low. Although we demonstrate and assess the results primarily using IASI data, the paper is also intended for applications to the IASI follow-on, that is, IASI Next Generation (IASI-NG), and much more to the Infrared Sounder (IRS), which is planned to fly this year, 2025, on the Meteosat Third Generation platform (MTG). Full article

Cosmogenic nuclide exposure dating is an important technique for reconstructing glacial histories. Many of the most commonly applied cosmogenic nuclides are extracted from the mineral quartz, meaning sampling of felsic (silica-rich) rock is often preferred to sampling of mafic (silica-poor) rock for exposure dating studies. Fieldwork in remote regions such as Antarctica is subject to time constraints and considerable logistical challenges, making efficient sample recovery critical to successful research efforts. Remote sensing offers an effective way to map the geology of large areas prior to fieldwork and expedite the sampling process. In this study, we assess the viability of multispectral remote sensing to distinguish felsic from mafic rock outcrops at visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelengths using both the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and very high-resolution Worldview-3 (WV-3) imagery. We applied a combination of spectral mapping and ground truth from spectral measurements of 17 rock samples from Mount Murphy in the Amundsen Sea sector of West Antarctica. Using this approach, we identified four dominant rock types which we used as a basis for felsic–mafic differentiation: felsic granites and gneisses, and mafic basalts and fragmental hydrovolcanic rocks. Supervised classification results indicate WV-3 performs well at differentiating felsic and mafic rock types and that ASTER, while coarser, could also achieve satisfactory results and be used in concert with more targeted WV-3 image acquisitions. Finally, we present a revised felsic–mafic geological map for Mt Murphy. Overall, our results highlight the potential of spectral mapping for preliminary reconnaissance when planning future cosmogenic nuclide sampling campaigns in remote, unvisited areas of the polar regions. Full article

In this study, the global land surface albedo namely GAC43 was retrieved for the years 1979 to 2020 using Advanced Very High Resolution Radiometer (AVHRR) global area coverage (GAC) data onboard National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites. We provide a comprehensive retrieval process of the GAC43 albedo, followed by a comprehensive assessment against in situ measurements and three widely used satellite-based albedo products, the third edition of the CM SAF cLoud, Albedo and surface RAdiation (CLARA-A3), the Copernicus Climate Change Service (C3S) albedo product, and MODIS BRDF/albedo product (MCD43). Our quantitative evaluations indicate that GAC43 demonstrates the best stability, with a linear trend of ±0.002 per decade at nearly all pseudo invariant calibration sites (PICS) from 1982 to 2020. In contrast, CLARA-A3 exhibits significant noise before the 2000s due to the limited availability of observations, while C3S shows substantial biases during the same period due to imperfect sensors intercalibrations. Extensive validation at globally distributed homogeneous sites shows that GAC43 has comparable accuracy to C3S, with an overall RMSE of approximately 0.03, but a smaller positive bias of 0.012. Comparatively, MCD43C3 shows the lowest RMSE (~0.023) and minimal bias, while CLARA-A3 displays the highest RMSE (~0.042) and bias (0.02). Furthermore, GAC43, CLARA-A3, and C3S exhibit overestimation in forests, with positive biases exceeding 0.023 and RMSEs of at least 0.028. In contrast, MCD43C3 shows negligible bias and a smaller RMSE of 0.015. For grasslands and shrublands, GAC43 and MCD43C3 demonstrate comparable estimation uncertainties of approximately 0.023, with close positive biases near 0.09, whereas C3S and CLARA-A3 exhibit higher RMSEs and biases exceeding 0.032 and 0.022, respectively. All four albedo products show significant RMSEs around 0.035 over croplands but achieve the highest estimation accuracy better than 0.020 over deserts. It is worth noting that significant biases are typically attributed to insufficient spatial representativeness of the measurement sites. Globally, GAC43 and C3S exhibit similar spatial distribution patterns across most land surface conditions, including an overestimation compared to MCD43C3 and an underestimation compared to CLARA-A3 in forested areas. In addition, GAC43, C3S, and CLARA-A3 estimate higher albedo values than MCD43C3 in low-vegetation regions, such as croplands, grasslands, savannas, and woody savannas. Besides the fact that the new GAC43 product shows the best stability covering the last 40 years, one has to consider the higher proportion of backup inversions before 2000. Overall, GAC43 offers a promising long-term and consistent albedo with good accuracy for future studies such as global climate change, energy balance, and land management policy. Full article

Monitoring and predicting land surface phenology (LSP) are essential for understanding ecosystem dynamics, climate change impacts, and forest and agricultural productivity. Satellite Earth observation (EO) missions have played a crucial role in the advancement of LSP research, enabling global and continuous monitoring of vegetation cycles. This review provides a brief overview of key EO satellite missions, including the advanced very-high resolution radiometer (AVHRR), moderate resolution imaging spectroradiometer (MODIS), and the Landsat program, which have played an important role in capturing LSP dynamics at various spatial and temporal scales. Recent advancements in machine learning techniques have further enhanced LSP prediction capabilities, offering promising approaches for short-term prediction of vegetation phenology and cropland suitability assessment. Data cubes, which organize multidimensional EO data, provide an innovative framework for enhancing LSP analyses by integrating diverse data sources and simplifying data access and processing. This brief review highlights the potential of satellite-based monitoring, machine learning models, and data cube infrastructure for advancing LSP research and provides insights into current trends, challenges, and future directions. Full article

Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are considerable advantages that arise from these different characteristics (spatiotemporal resolution, time of observation, etc.), it also means that there is a need for understanding the ability of sensors in capturing spatial and temporal SUHI patterns. For this, several land surface temperature products are compared for the cities of Madrid and Paris, retrieved from five sensors: the Spinning Enhanced Visible and InfraRed Imager onboard Meteosat Second Generation, the Advanced Very-High-Resolution Radiometer onboard Metop, the Moderate-resolution Imaging Spectroradiometer onboard both Aqua and Terra, and the Thermal Infrared Sensor onboard Landsat 8 and 9. These products span a wide range of LST algorithms, including split-window, single-channel, and temperature–emissivity separation methods. Results show that the diurnal amplitude of SUHI may not be well represented when considering daytime and nighttime polar orbiting platforms. Also, significant differences arise in SUHI intensity and spatial and temporal variability due to the different methods implemented for LST retrieval. Full article

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVI^THM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025. Full article

Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human lives. In the case of such a kind of disastrous events, timely and accurate information about the location and extent of the affected areas can be crucial to better plan and implement recovery and containment interventions. Satellite systems may efficiently provide such information at different spatial/temporal resolutions. Several authors have developed satellite techniques to detect and map inundated areas using both Synthetic Aperture Radar (SAR) and a new generation of high-resolution optical data but with some accuracy limits, mostly due to the use of fixed thresholds to discriminate between the inundated and unaffected areas. In this paper, the RST-FLOOD fully automatic technique, which does not suffer from the aforementioned limitation, has been exported for the first time to the mid–high-spatial resolution (20 m) optical data provided by the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI). The technique was originally designed for and successfully applied to Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data at a mid–low spatial resolution (from 1000 to 375 m). The processing chain was implemented in a completely automatic mode within the Google Earth Engine (GEE) platform to study the recent strong flood event that occurred in May 2023 in Emilia Romagna (Italy). The outgoing results were compared with those obtained through the implementation of an existing independent optical-based technique and the products provided by the official Copernicus Emergency Management Service (CEMS), which is responsible for releasing information during crisis events. The comparisons carried out show that RST-FLOOD is a simple implementation technique able to retrieve more sensitive and effective information than the other optical-based methodology analyzed here and with an accuracy better than the one offered by the CEMS products with a significantly reduced delivery time. Full article

Surface downward longwave radiation (SDLR) is crucial for maintaining the global radiative budget balance. Due to their ease of practicality, SDLR parameterization models are widely used, making their objective evaluation essential. In this study, against comprehensive ground measurements collected from more than 300 globally distributed sites, four SDLR parameterization models, including three popular existing ones and a newly proposed model, were evaluated under clear- and cloudy-sky conditions at hourly (daytime and nighttime) and daily scales, respectively. The validation results indicated that the new model, namely the Peng model, originally proposed for SDLR estimation at the sea surface and applied for the first time to the land surface, outperformed all three existing models in nearly all cases, especially under cloudy-sky conditions. Moreover, the Peng model demonstrated robustness across various land cover types, elevation zones, and seasons. All four SDLR models outperformed the Global Land Surface Satellite product from Advanced Very High-Resolution Radiometer Data (GLASS-AVHRR), ERA5, and CERES_SYN1de-g_Ed4A products. The Peng model achieved the highest accuracy, with validated RMSE values of 13.552 and 14.055 W/m² and biases of −0.25 and −0.025 W/m² under clear- and cloudy-sky conditions at daily scale, respectively. Its superior performance can be attributed to the inclusion of two cloud parameters, total column cloud liquid water and ice water, besides the cloud fraction. However, the optimal combination of these three parameters may vary depending on specific cases. In addition, all SDLR models require improvements for wetlands, bare soil, ice-covered surfaces, and high-elevation regions. Overall, the Peng model demonstrates significant potential for widespread use in SDLR estimation for both land and sea surfaces. Full article

The NOAA-AVHRR (National Oceanographic and Atmospheric Administration–Advanced Very High-Resolution Radiometer) archive includes data from 1981 onwards, which allow for estimating land surface temperature (LST), a key parameter for the study of global warming as well as surface characterization. However, algorithms for LST retrieval were developed before the latest sensors and were based on more reduced atmospheric datasets. Here, we present 50 novel sets of coefficients for an LST retrieval algorithm from NOAA-AVHRR sensors, to which we added one historical methodology, which we validate against historical in situ as well as independent satellite data. This validation shows that the historical algorithm performs surprisingly well, with an in situ RMSE below 1.5 K and a quasi-null bias when compared with independent satellite data. A couple of the novel algorithms also perform within expectations (errors below 1.5 K), so any of these could be used for the complete processing of the AVHRR dataset. In our case, considering consistency with previous works, we opt for the use of the historical algorithm, now also tested for more recent periods. Full article

Snow cover products are primarily derived from the Moderate-resolution Imaging Spectrometer (MODIS) and Advanced Very-High-Resolution Radiometer (AVHRR) datasets. MODIS achieves both snow/non-snow discrimination and snow cover fractional retrieval, while early AVHRR-based snow cover products only focused on snow/non-snow discrimination. The AVHRR Climate Data Record (AVHRR-CDR) provides a nearly 40-year global dataset that has the potential to fill the gap in long-term snow cover fractional monitoring. Our study selects the Qinghai–Tibet Plateau as the experimental area, utilizing AVHRR-CDR surface reflectance data (AVH09C1) and calibrating with the MODIS snow product MOD10A1. The snow cover percentage retrieval from the AVHRR dataset is performed using Surface Reflectance at 0.64 μm (SR1) and Surface Reflectance at 0.86 μm (SR2), along with a simulated Normalized Difference Snow Index (NDSI) model. Also, in order to detect the effects of land-cover type and topography on snow inversion, we tested the accuracy of the algorithm with and without these influences, respectively (vanilla algorithm and improved algorithm). The accuracy of the AVHRR snow cover percentage data product is evaluated using MOD10A1, ground snow-depth measurements and ERA5. The results indicate that the logic model based on NDSI has the best fitting effect, with R-square and RMSE values of 0.83 and 0.10, respectively. Meanwhile, the accuracy was improved after taking into account the effects of land-cover type and topography. The model is validated using MOD10A1 snow-covered areas, showing snow cover area differences of less than 4% across 6 temporal phases. The improved algorithm results in better consistency with MOD10A1 than with the vanilla algorithm. Moreover, the RMSE reaches greater levels when the elevation is below 2000 m or above 6000 m and is lower when the slope is between 16° and 20°. Using ground snow-depth measurements as ground truth, the multi-year recall rates are mostly above 0.7, with an average recall rate of 0.81. The results also show a high degree of consistency with ERA5. The validation results demonstrate that the AVHRR snow cover percentage remote sensing product proposed in this study exhibits high accuracy in the Tibetan Plateau region, also demonstrating that land-cover type and topographic factors are important to the algorithm. Our study lays the foundation for a global snow cover percentage product based on AVHRR-CDR and furthermore lays a basic work for generating a long-term AVHRR-MODIS fractional snow cover dataset. Full article

Remote sensing data are becoming increasingly important for quantifying long-term changes in land surfaces. Optical sensors onboard satellite platforms face a tradeoff between temporal and spatial resolutions. Spatiotemporal fusion models can produce high spatiotemporal data, while existing models are not designed to produce moderate-spatial-resolution data, like Moderate-Resolution Imaging Spectroradiometer (MODIS), which has moderate spatial detail and frequent temporal coverage. This limitation arises from the challenge of combining coarse- and fine-spatial-resolution data, due to their large spatial resolution gap. This study presents a novel model, named multi-scale convolutional neural network for spatiotemporal fusion (MSCSTF), to generate MODIS-like data by addressing the large spatial-scale gap in blending the Advanced Very-High-Resolution Radiometer (AVHRR) and Landsat images. To mitigate the considerable biases between AVHRR and Landsat with MODIS images, an image correction module is included into the model using deep supervision. The outcomes show that the modeled MODIS-like images are consistent with the observed ones in five tested areas, as evidenced by the root mean square errors (RMSE) of 0.030, 0.022, 0.075, 0.036, and 0.045, respectively. The model makes reasonable predictions on reconstructing retrospective MODIS-like data when evaluating against Landsat data. The proposed MSCSTF model outperforms six other comparative models in accuracy, with regional average RMSE values being lower by 0.005, 0.007, 0.073, 0.062, 0.070, and 0.060, respectively, compared to the counterparts in the other models. The developed method does not rely on MODIS images as input, and it has the potential to reconstruct MODIS-like data prior to 2000 for retrospective studies and applications. Full article

Understanding the net ecosystem productivity (NEP) is essential for understanding ecosystem functioning and the global carbon cycle. Utilizing meteorological and The Advanced Very High Resolution Radiometer (AVHRR) remote sensing data, this study employed the Carnegie–Ames–Stanford Approach (CASA) and the Geostatistical Model of Soil Respiration (GSMSR) to map a monthly vegetation NEP in China from 1982 to 2020. Then, we examined the spatiotemporal trends of NEP and identified the drivers of NEP changes using the Geodetector model. The mean NEP over the 39-year period amounted to 265.38 gC·m⁻². Additionally, the average annual carbon sequestration amounted to 1.89 PgC, indicating a large carbon sink effect. From 1982 to 2020, there was a general fluctuating increasing trend observed in the annual mean NEP, exhibiting an overall average growth rate of 4.69 gC·m⁻²·a⁻¹. The analysis revealed that the majority of the vegetation region in China, accounting for 93.45% of the entirety, exhibited increasing trends in NEP. According to the Geodetector analysis, precipitation change rate, solar radiation change rate, and altitude were the key driving factors in NEP change rate. Furthermore, the interaction between the precipitation change rate and altitude demonstrated the most significant effect. Full article

In recent years, vegetation on the Qinghai–Tibet Plateau (QTP) has undergone significant greening. However, the causal factors underpinning this phenomenon, whether attributable to temperature fluctuations, precipitation patterns, or anthropogenic interventions, remain a subject of extensive scholarly debate. This study conducted a comprehensive analysis of the evolving vegetation across the QTP. The National Oceanic and Atmospheric Administration Climate Data Record Advanced Very High Resolution Radiometer Normalized Vegetation Difference Index (NOAA CDR AVHRR NDVI) dataset was employed to elucidate the intricate relationship between climatic variables and human activities driving vegetative transformations. The findings were as follows: The NDVI on the QTP has exhibited a significant greening trend at a rate of 0.0013/a (per year). A minor decline, accounting for only 17.6% of grasslands, was observed, which was primarily concentrated in the northwestern and northern regions. Through residual analysis, climate change was found to be the predominant driver, explaining 70.6% of the vegetation variability across the plateau. Concurrently, noticeable trends in temperature and precipitation increases were observed on the QTP, with the southern region demonstrating improved sensitivity to precipitation alterations. In summary, these results substantiate that a confluence of climatic warming, enhanced moisture availability, and a reduction in livestock population collectively creates an environment conducive to enhanced vegetation vigor on the QTP. This study highlights the significance of acknowledging the dual influence of climate and human agency in shaping vegetative dynamics, which is a critical consideration for informed land management strategies and sustainable development initiatives on this ecologically pivotal plateau. Full article