Zong, ShengweiLembrechts, Jonas J.Du, HaiboHe, Hong S....
14页
查看更多>>摘要：Pronounced climate warming has resulted in a significant reduction of snow cover extent, as well as poleward and upslope shifts of shrubs in Arctic and alpine ecosystems. However, it is difficult to establish links between changes in snow cover and shrub distribution changes due to a lack of in situ and long-term snow records in relation to abundance shifts of shrubs at their leading (i.e., cold) and trailing (i.e., warm) edges. We used remote sensing to extract long-term changes in both snow cover and shrub distributions in response to climate change in the alpine tundra of the Changbai Mountains in Northeast China. First, we analyzed spatio-temporal changes in snow cover during the snowmelt period (April 1st to June 15th) over the past 54 years (1965-2019). Then, we analyzed distribution changes of the dominant evergreen alpine shrub, Rhododendron aureum, using 31 years (1988-2019) of Landsat NDVI archives. We applied a novel approach by analyzing NDVI data from autumn only, when R. aureum is green yet most of the surrounding plants are already brown. Finally, we tested the relationship between snowmelt date and the distribution of R. aureum. We found that the fraction cover of R. aureum experienced greater loss than gain in the last 30 years. R. aureum expanded at the leading edge, establishing in snow-rich habitats, yet retracted further at the trailing edge due to loss of snow habitats. We identified the preferred snowmelt regime (habitats with snowmelt date of 20 April or later) of this shrub species and found that further advances in snowmelt dates would lead to the upward range shift of R. aureum in a warming climate. Our results indicate that spring snow cover change affected distribution changes of R. aureum. Our study highlights that long-term changes in snow cover due to climate change have already had marked impacts on plant species distributions in alpine ecosystems.
查看更多>>摘要：ABS T R A C T Traditional optical remote sensing data have been widely used for snow cover detection and monitoring. However, they are limited to daytime detection and often suffer from large data gaps due to frequent cloud obscuration. This is in particular a serious challenge for high-latitude and polar regions where long nights prevail during the winter. Nighttime light sensors have a strong capability of sensing the low-level reflected moonlight. They potentially provide a new way to detect snow cover. In this study, we quantitatively analyzed the moonlight intensity for snow detection and developed a Minimum Error Thresholding (MET) algorithm to detect snow cover from the data collected by Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-NPP VIIRS) satellite data. For the two case study sites, Abisko in the sub-Arctic zone and the Tibetan Plateau, our analysis results suggest that the moonlight provides sufficient illumination to map snow cover for approx-imately 10 days in a lunar month. Our nighttime snow cover detection method was quantitatively evaluated by comparing our S-NPP VIIRS DNB snow cover estimates with in situ station observations, Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover products, and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products over Abisko region and the Tibetan Plateau during the 2017-2018 snow season. The overall accuracy of S-NPP VIIRS snow cover estimates was approximately 80.3% in Abisko region and 76.7% in the Tibetan Plateau. The data gaps in our S-NPP VIIRS DNB snow cover estimates were smaller than those of the MODIS snow cover products by 22.1% and 5.1% over Abisko region and the Tibetan Plateau, respectively. Further, we found that nearly 92.8% and 74.6% of data gaps in the MODIS snow-cover product can be filled up by incorporating our S-NPP VIIRS DNB snow cover estimates in Abisko region and the Tibetan Plateau. The total accuracy of daily MODIS snow cover products can be improved to 91.0% in the Tibetan Plateau. Our results indicate that S-NPP VIIRS DNB nighttime satellite data can provide reliable snow products over polar regions and mid-latitude mountainous areas, which is complementary to the standard MODIS snow cover products.
查看更多>>摘要：Severe droughts caused unprecedented impacts on grasslands in Central Europe in 2018 and 2019. Yet, spatially varying drought impacts on grasslands remain poorly understood as they are driven by complex interactions of environmental conditions and land management. Sentinel-2 time series offer untapped potential for improving grassland monitoring during droughts with the required spatial and temporal detail. In this study, we quantified drought effects in a major Central European grassland region from 2017 to 2020 using a regression-based unmixing framework. The Sentinel-2-based intra-annual time series of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and soil fractional cover provide easily interpretable quantities relevant for understanding drought effects on grasslands. Fractional cover estimates from Sentinel-2 matched in-situ conditions observed during field visits. The comparison to a multitemporal reference dataset showed the best agreement for PV cover (MAE = 7.2%). Agreement was lower for soil and NPV, but we observed positive relationships between fractional cover from Sentinel-2 and the reference data with MAE = 10.1% and MAE = 15.4% for soil and NPV, respectively. Based on the fractional cover estimates, we derived a Normalized Difference Fraction Index (NDFI) time series contrasting NPV and soil cover relative to PV. In line with meteorological and soil moisture drought indices, and with the Normalized Difference Vegetation Index (NDVI), NDFI time series showed the most severe drought impacts in 2018, followed by less severe, but persisting effects in 2019. Drought-specific metrics from NDFI time series revealed a high spatial variability of onset, duration, impact, and end of drought effects on grasslands. Evaluating drought metrics on different soil types, we found that grasslands on less productive, sandy Cambisols were strongly affected by the drought in 2018 and 2019. In comparison, grasslands on Gleysols and Histosols were less severely impacted suggesting a higher drought resistance of these grasslands. Our study emphasizes that the high temporal and spatial detail of Sentinel-2 time series is mandatory for capturing relevant vegetation dynamics in Central European lowland grasslands under drought.
查看更多>>摘要：NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias.
查看更多>>摘要：Spatiotemporal image fusion is a potential way to resolve the constraint between the spatial and temporal resolutions of satellite images and has been developed rapidly in recent years. However, two key challenges related to fusion accuracy remain: a) reducing the uncertainty of image fusion caused by sensor differences and b) addressing strong temporal changes. To solve the above two issues, this paper presents the newly proposed Reliable and Adaptive Spatiotemporal Data Fusion (RASDF) method. In RASDF, the effects of four kinds of sensor differences on fusion are analyzed systematically. A reliability index is therefore proposed to describe the spatial distribution of the reliability in input data for image fusion. An optimization strategy based on the spatial distribution of the reliability quantified by the index is developed to improve the robustness of the fusion. In addition, an adaptive global unmixing model and an adaptive local unmixing model are constructed and utilized collaboratively to enhance the ability to retrieve strong temporal changes. The performance and robustness of RASDF were compared with six representative fusion methods for both real and simulated datasets covering both homogeneous and heterogeneous sites. Experimental results indicated that RASDF achieves a better performance and provides a more reliable image fusion solution in terms of reducing the impact of sensor differences on image fusion and retrieving strong temporal changes.
查看更多>>摘要：Glaciers in polar regions are sensitive to climate and ocean changes and can thin rapidly as a consequence of global warming. Digital Elevation Models (DEMs) from remote sensing observations have been widely used to detect changes in polar glaciers. DEMs from Terrestrial Radar Interferometer (TRI) have recently been used for high frequency glacier change and glacier-ocean interaction studies. However, it is unclear whether TRI DEM over a large study area can be combined directly with remote sensing observations to investigate glacier changes as well as the accuracy of TRI DEM at far range. In this study, we deployed a TRI close to Helheim Glacier, East Greenland and generated DEMs using TRI and satellite laser altimetry. We analyzed the accuracy of the TRI DEM using theoretical calculations, comparisons based on repeat observations, and comparisons with a high accurate ArcticDEM. The validation results suggest that for stable ground surfaces, the uncertainty (standard deviation) is <5 m at range < 9.8 km. Averaging across time (e.g. one hour) decreases the uncertainty almost linearly with range, over 0.5 m to 1.2 m when the range increases from 7.0 km to 10.0 km. Increasing the correlation coefficient threshold for phase unwrapping does not significantly reduce uncertainty. TRI DEMs are influenced by systematic error at far range primarily due to coarse azimuth resolution and phase unwrapping difficulties in discontinuous interferograms. As the absolute accuracy of TRI DEMs is not uniformly distributed in the range direction (farther points have worse uncertainty), our findings indicate that TRI DEMs within range of 10 km can reach <5 m uncertainty, which can be compared with DEMs obtained from remote sensing satellites to detect glacier thinning.
查看更多>>摘要：Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Retrogressive thaw slumps (RTS) are abrupt permafrost disturbances that expand by several meters each year and lead to an increased soil organic carbon release. Local Remote Sensing studies identified increasing RTS activity in the last two decades by increasing number of RTS or heightened RTS growth rates. However, a large-scale assessment across diverse permafrost regions and at high temporal resolution allowing to further determine RTS thaw dynamics and its main drivers is still lacking. In this study we apply the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to North Siberia (8.1 x 106km2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.609). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very highresolution RapidEye and PlanetScope imagery. Our study presents the first automated detection and assessment of RTS and their temporal dynamics at largescale for 2001-2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across North Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331% compared to 2000 (2000: 20,158 ha, 2001-2019: 66,699 ha). Contrary to this, 5 focus sites show spatiotemporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The majority of identified RTS was active from 2000 onward and only a small proportion initiated during the assessment period, indicating that the increase in RTS-affected area was mainly caused by enlarging existing RTS and not by new RTS. The detected increase in RTS dynamics suggests advancing permafrost thaw and underlines the importance of assessing abrupt permafrost disturbances with high spatial and temporal resolution at large-scales. Obtaining such consistent disturbance products will help to parametrise regional and global climate change models.
查看更多>>摘要：Land surface temperature (LST) is listed as an essential climate variable (ECV) and supports quantitative estimates of the energy budget while serving as a proxy for measuring the effects of climate change and extreme events. Forested areas are considered a major land unit impacted by temperature rise; therefore, thorough monitoring is mandatory. An accuracy assessment of the LST of forests must consider their directional anisotropy (DA). This latter can be well depicted by thermal infrared (TIR) radiative transfer models, but the problem is complex for forests because many of the shaded areas generate multiscale gradients of temperature. In this paper, we adapted a mature and widely used visible and near-infrared (VNIR) radiative transfer model called forest reflectance and transmittance (FRT) to enhance the characterization of the DA of forest temperature. In the FRT model, the vertical heterogeneity of the forest is quantified by using the discrete elements of multilayer scene components (i.e., the tree crown, trunk, understory vegetation, and soil), thus inferring vertical thermal gradients. The Planck function and spectral-invariant theory are considered to assess the thermal emissions of the scene components and their multiple scattering processes. The FRT model is validated using directional forest brightness temperatures (BT) measured from an unmanned aerial vehicle (UAV) and simulated by using the three-dimensional ray-tracing LESS (large-scale remote sensing data and image simulation framework over heterogeneous 3D scenes) model. The results show that FRT behaves reliably since the root mean square error (RMSE) is lower than 1.0 degrees C for UAV measurements obtained at 09:20 and 13:10 and with coefficients of determination (R2) larger than 0.74 and 0.56, respectively; these results are better than the simulated results by existing models. Moreover, the comparison with ray-tracing simulations was also deemed satisfactory. According to the analysis, large variations in BT DAs may appear for different forests and seasonal changes staged by structural and thermal stratification, thus indicating the necessity of using the FRT model for complex and dynamic forest canopies.
查看更多>>摘要：Permafrost on the Qinghai-Tibet Plateau (QTP) undergoes significant thawing and degradation, which affects the hydrological processes, ecosystems and infrastructure stability. The ground deformation, a key indicator of permafrost degradation, can be quantified via geodetic observations, especially using multi-temporal InSAR techniques. The previous InSAR studies, however, either rely on data-driven models or Stefan-equation-based models, which are both lacking of consideration of the spatial-temporal variations of freeze-thaw processes. Furthermore, the magnitudes and patterns of the permafrost-related ground deformation over large scales (e.g., 1 x 10(5) km(2) or larger) is still insufficiently quantified or poorly understood. In this study, to account for the spatial heterogeneity of freeze-thaw processes, we develop a permafrost-tailored InSAR approach by incorporating a MODIS-land-surface-temperature-integrated ground deformation model to reconstruct the seasonal and long-term deformation. Utilizing the approach to Sentinel-1 SAR images on the vast regions of about 140,000 km(2) of the central QTP during 2014-2019, we observe widespread seasonal deformation up to about 80 mm with a mean value of about 10 mm and linear subsidence up to 20 mm/year. We apply the geographical detector to determine the controlling factors on the permafrost-related deformation. We find that the slope angle is the primary controller on the seasonal deformation: strong magnitudes and variations of seasonal deformation are most pronounced in flat or gentle-slope regions. The aspect angle, vegetation and soil bulk density exhibit a certain correlation with seasonal deformation as well. Meanwhile, we find that a linear subsidence is higher in the regions with high ground ice content and warm permafrost. It indicates that warm and ice-rich permafrost regions are more vulnerable to extensive long-term subsidence. We also observe that the cold permafrost regions experience lower linear subsidence even with high ground ice content, which indicate ice loss is limited. Thus, we infer that under continuously warming, the transition from cold permafrost to warm permafrost may lead to more extensive ground ice melting. Moreover, the strong subsidence/uplift signals surrounding some lakes suggesting that the change of local hydrological conditions may induce localized permafrost degradation/aggradation. Our study demonstrates the capability of the permafrost-tailored InSAR approach to quantify the permafrost freeze-thaw dynamics as well as their spatial-temporal patterns over large scales in vast permafrost areas.
Khabbazan, S.Steele-Dunne, S. C.Vermunt, P.Judge, J....
17页
查看更多>>摘要：The presence of surface water on the canopy affects radar backscatter. However, its influence on the relationship between radar backscatter and crop biophysical parameters has not been investigated. The aim of this study was to quantify the influence of surface canopy water (SCW) on the relationship between L-band radar backscatter and biophysical variables of interest in agricultural monitoring. In this study, we investigated the effect of SCW on the relationship between co- and cross-polarized radar backscatter, cross ratios (VH/VV and HV/HH), and radar vegetation index (RVI) and dry biomass, vegetation water content (VWC), plant height and leaf area index (LAI). In addition, the effect of SCW on estimated vegetation optical depth (VOD) and its relationship with internal VWC was investigated. The analysis was based on data collected during a field experiment in Florida, USA in 2018. A corn field was scanned with a truck-mounted, fully polarimetric, L-band radar along with continuous monitoring of SCW (dew, interception) and soil moisture every 15 min for 58 days. In addition, pre-dawn destructive sampling was conducted to measure internal vegetation water content and dry biomass. Results showed that the presence of SCW can increase the radar backscatter up to 2 dB and this effect was lower for cross ratios (CRs) and RVI. The Spearman's rank correlations between radar observables and biophysical parameters were, on average, 0.2 higher for dry vegetation compared to wet vegetation. The estimated VOD from wet vegetation was generally higher than those from dry vegetation, which led to different fitting parameter (socalled b) values in the linear fit between VOD and VWC. The results presented here underscore the importance of considering the influence of SCW on the retrieval of biophysical variables of interest in agricultural monitoring. In particular, they highlight the importance of overpass time, and the impact that daily patterns in dew and interception can have on the retrieval of biophysical variables of interest.
NSTL
Elsevier