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Remote Sensing of Environment
Elsevier
Remote Sensing of Environment

Elsevier

0034-4257

Remote Sensing of Environment/Journal Remote Sensing of EnvironmentSCIISTPEI
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    Evaluating recovery metrics derived from optical time series over tropical forest ecosystems

    De Keersmaecker, WandaRodriguez-Sanchez, PabloMilencovic, MilutinHerold, Martin...
    11页
    查看更多>>摘要：An increase in the frequency and severity of disturbances (such as forest fires) is putting pressure on the resilience of the Amazon tropical forest; potentially leading to reduced ability to recover and to maintain a functioning forest ecosystem. Dense and long-term satellite time series approaches provide a largely untapped data source for characterizing disturbance- recovery forest dynamics across large areas and varying types of forests and conditions. Although large-scale forest recovery capacity metrics have been derived from optical satellite image time series and validated over various ecosystems, their sensitivity to disturbance (e.g. disturbance magnitude, disturbance timing, and recovery time) and environmental data characteristics (e.g. noise magnitude, seasonality, and missing values) are largely unknown. This study proposes an open source simulation framework based on the characteristics of sampled original satellite image time series to (i) compare the reliability of recovery metrics, (ii) evaluate their sensitivity with respect to environmental and disturbance characteristics, and (iii) evaluate the effect of pre-processing techniques on the reliability of the recovery metrics for abrupt disturbances, such as fires, in the Amazon basin forests. The effect of three pre-processing techniques were evaluated: changing the temporal resolution, noise removal techniques (such as time series smoothing and segmenting), and using a varying time span after the disturbance to calculate recovery metrics. Here, reliability is quantified by comparing derived and theoretical values of the recovery metrics (RMSE and R-2). From the three recovery metrics evaluated, the Year on Year Average (YrYr) and the Ratio of Eighty Percent (R80p) are more reliable than the Relative Recovery Index (RRI). Time series segmentation tends to improve the reliability of recovery metrics. Recovery metrics derived from temporal dense Landsat time series tend to show a higher reliability than those derived from time series aggregated to quarterly or annual values. Although the framework is demonstrated on Landsat time series of the Amazon tropical forest, it can be used to perform such test on other datasets and ecosystems.
      原文链接:
    • NSTL
    • Elsevier

    Terrain deformation measurements from optical satellite imagery: The MPIC-OPT processing services for geohazards monitoring

    Boissier, EnguerranPointal, ElisabethStumpf, AndrePacini, Fabrizio...
    18页
    查看更多>>摘要：Measuring terrain deformation over several spatial and temporal scales is relevant for many applications in Earth Sciences (i.e. active faults, volcanoes, landslides or glaciers understanding). The growing volume of freely available data represents nowadays a challenge in terms of storage capacity and computing resources which, together with the complexity of the processing (code parameterization, combination of the image sequences, coregistration of the images) may prevent the exploitation of long time series. We propose here a new version of the Multiple-Pairwise Image Correlation toolbox for processing OPTical images (MPIC-OPT). The toolbox proposes an end-to-end solution to compute the horizontal sub-pixel ground deformation time series from large Sentinel-2 datasets. In addition to time series inversion, several corrections and filtering options are integrated to reduce the noise and improve the accuracy and precision of the measurements. In particular, an automatic jitter correction based on wavelet filtering is proposed. Moreover, the MPIC-OPT service is deployed on the Tier 1.5 HighPerformance Computing cluster (e.g. Datacentre/EOST-A2S) of the University of Strasbourg and is accessible on-line through the ESA Geohazards Exploitation Platform (GEP) and the ForM@Ter Solid Earth computing infrastructure with a user-friendly environment to query the satellite data catalogues, parameterize the processing and visualize the outputs. We test the performances of MPIC-OPT on several use cases: the measurement of the co-seismic ground deformation of the 2019 Ridgecrest earthquake sequence (USA), of the rapid motion of the Slumgullion landslide (USA) and of the glaciers of the Mont-Blanc massif (France/Italy). We show that the results of MPIC-OPT are in agreement with in-situ data. The jitter correction significantly improves the precision (RMSEjitter=0.3m vs. RMSEnojitter=0.5m) and the accuracy (RMSEjitter=0.3m vs. RMSEnojitter=1.3m) of the measurement of the co-seismic displacement of the Rigdecrest seismic deformation. We show that the precision and accuracy of the terrain deformation estimation depend mainly on the correlation threshold and the temporal matching range parameters and we quantify and discuss their impacts. This work opens new perspectives to monitor automatically surface displacements/velocities of natural hazards over large scales and large periods of time.
      原文链接:
    • NSTL
    • Elsevier

    Graph-based block-level urban change detection using Sentinel-2 time series

    Wang, NanLi, WeiTao, RanDu, Qian...
    23页
    查看更多>>摘要：Remote sensing technology has been frequently used to obtain information on changes in urban land cover because of its vast spatial coverage and timeliness of observation. Block-level change detection with high tem-poral resolution image data provides fine detail of urban changes, is suitable for urban management, and has gradually received widespread attention. High-dimensional features are required to express the heterogeneous structure of the blocks. High-dimensional high-frequency time series, namely, multivariate time series, are formed by arranging high-dimensional features chronologically. Classic change detection methods treat multi-variate time series as univariate time series one by one. Few studies have analyzed the change in a multivariate time series by considering all variables as an entirety. Therefore, a graph-based segmentation for multivariate time series algorithm (MTS-GS) is proposed in this paper. Specifically, 1) we construct a similarity matrix to explore the changing patterns of multivariate time series for seasonal change, trend change, abrupt change, and noise disturbance; 2) a multivariate time series graph is defined based on the changing patterns; and 3) the corresponding graph segmentation algorithm is proposed in the paper to detect the abrupt and trend changes under noise and seasonal disturbances. Sentinel-2 images of the rapidly developing third-tier city of Luoyang, Henan province, China, are adopted to validate the algorithm. The F1-score in the spatial domain is 84.1%; the producer's and the user's accuracy in the temporal dimension are 81.8% and 80.1%, respectively. Seven change types are defined and extracted, showing the development pattern and the efficiency of land use in the city. Furthermore, the proposed MTS-GS can be used for pixel-level change detection and performs well under various time intervals and cloud covers.
      原文链接:
    • NSTL
    • Elsevier

    Towards accurate mapping of forest in tropical landscapes: A comparison of datasets on how forest transition matters

    Velasco, Ruben FerrerLippe, MelvinTamayo, FabianMfuni, Tiza...
    18页
    查看更多>>摘要：Tropical forests represent half of the Earth's remaining forest area, but they are shrinking at high rates, which poses a threat to their multiple ecosystem services. As a response, international environmental agreements and related programs require information about tropical forested landscapes. Despite the increasing quantity and quality of remote sensing-based data, the effective monitoring of forests in the tropics still faces operational challenges: (a) applicability at local levels, with lack of reference or cloud-free information; (b) overcoming geographical, ecological, or biophysical variability; (c): stratification, distinguishing forest categories related to functionality and disturbance history. We conducted an extensive ground verification campaign through 36 landscapes in 9 regions of Zambia, Ecuador and Philippines, which constitute a gradient of pantropical deforestation contexts or forest transitions. We collected over 16,000 ground control points and digitized over 18,000 ha with details on land use and forest disturbance history. We trained a random forest algorithm and generated high-resolution (30 m) binary forest maps covering ~15 Mha, building on 39 optical (Landsat-8), radar (Sentinel-1) and elevation bands, indices and textures. We validated the quality of the outputs across the studied deforestation gradient and compared them to (a): 3 national land cover maps used for international reporting, (b): 4 global forest datasets (Global Forest Change, Copernicus Land Cover, JAXA and TanDEM-X Forest/Non-Forest). Our method generated highly accurate (92%) forest maps for the studied regions when compared to the global datasets, which generally overestimated forest cover. We achieved accuracies similar to the national maps, following a standardized method for all countries. The difficulties in delineating forest increased in more advanced stages of deforestation, with recurring struggles to distinguish non-forest tree-based systems (e.g. perennials, palms, or agroforestry), shrublands and grasslands. Regrowth forests were repeatedly misclassified across contexts, countries and datasets, in contrast to reference or degraded forests. Our results highlight the importance of in situ verification as accompanying method to establish efficient forest monitoring systems, especially in areas with higher rates of forest cover change and in tropical regions of advanced deforestation or early reforestation stages. These are precisely the areas where current REDD+ or Forest Landscape Restoration initiatives take place.
      原文链接:
    • NSTL
    • Elsevier

    Effects of rain on CFOSAT scatterometer measurements

    Zhao, XiaokangLin, WenmingPortabella, MarcosWang, Zhixiong...
    9页
    查看更多>>摘要：The Ku-band scatterometer onboard China France Oceanography Satellite (CFOSAT) observes the sea surface with two conically scanning fan beams. Compared to the prior Ku-band pencil beam scatterometers, this innovative observing mechanism provides more independent backscatter measurements at varying incidence and azimuth angles, as such it brings challenges for the sea surface wind inversion, particularly under rainy conditions. In this paper, the rain effects on the CFOSAT SCATterometer (CSCAT) are investigated using the collocated numerical weather prediction (NWP) wind data and the Global Precipitation Measurement (GPM) microwave imager (GMI) rain data. Similar to the prior Ku-band or C-band scatterometers, the sensitivity of CSCAT radar backscatter to rain substantially varies with wind speed, radar polarization and incidence angle. However, due to the complex observation geometries, rain effects on the CSCAT retrieved winds is more complex than that of prior scatterometers, which may lead to a remarkable underestimation of CSCAT wind speed at high winds and heavy rain conditions. A simple simulation method is used to clarify the relation between the retrieved wind speed and the dependency of radar rain effects on the incidence angle. It is found that the backscatter measurements at low incidence angles, which are generally underestimated at high winds and heavy rainy conditions, have a larger influence on the wind inversion minimization, leading to much lower retrieved wind speeds than those of ECMWF and the pencil beam scatterometer (e.g., Haiyang-2B scattometer). Under low and moderate rain conditions though, a more compensated effect between low and high incidence angle measurements is found, leading to generally unbiased CSCAT high winds, in contrast to the generally underestimated pencil-beam scatterometer winds.
      原文链接:
    • NSTL
    • Elsevier

    Interference-sensitive coastal SAR altimetry retracking strategy for measuring significant wave height

    Schlembach, FlorianPassaro, MarcelloDettmering, DeniseBidlot, Jean...
    18页
    查看更多>>摘要：Satellite altimetry is a radar remote sensing technology for the precise observation of the ocean surface and its changes over time. Its measurements allow the determination of geometric and physical parameters such as sea level, significant wave height or wind speed. This work presents a novel coastal retracking algorithm for SAR altimetry to estimate the significant wave height. The concept includes an adaptive interference masking scheme to sense and mitigate spurious interfering signals that typically arise from strongly reflective targets in the coastal zone. The described procedure aims at increasing the number of valid records in the coastal zone. The effec-tiveness of the novel retracking algorithm is validated using the methodology recently developed in the framework of the European Space Agency Sea State Climate Change Initiative project. Several different metrics were extracted as functions of sea state and distance to the nearest coast: outliers, number of valid records, intrinsic noise, power spectral density, and correlation statistics for the comparison with wave model and in-situ data. Two coastal case study scenarios complement the validation. The results show that with the presented novel retracking algorithm, the number of valid 20-Hz records in the near coastal zone of less than 5 km off the coast is increased by more than 25% compared to the best competing coastal retracking algorithm with no degradation of quality of the estimated records. We emphasise the importance of the correct choice of the quality flag that is provided together with the significant wave height. Our findings suggest that the strategy for the significant wave height quality flag of the official baseline Level-2 product of the Sentinel-3 mission can be redefined to obtain more robust significant wave height estimates in the coastal zone.
      原文链接:
    • NSTL
    • Elsevier

    A deep learning model for incorporating temporal information in haze removal

    Wang, QunmingTong, XiaohuaAtkinson, Peter M.Ma, Xiaofeng...
    17页
    查看更多>>摘要：Haze contamination is a very common issue in remote sensing images, which inevitably limits data usability and further applications. Several methods have been developed for haze removal, which is an ill-posed problem. However, most of these methods involve various strong assumptions coupled with manually-determined parameters, which limit their generalization to different scenarios. Moreover, temporal information amongst time series images has rarely been considered in haze removal. In this paper, the temporal information is proposed to be incorporated for more reliable haze removal, and guided by this general idea, a temporal information injection network (TIIN) is developed. The proposed TIIN solution for haze removal extracts the useful information in the temporally neighboring images provided by the regular revisit of satellite sensors. The TIIN method is suitable for images with various haze levels. Moreover, TIIN is also applicable for temporal neighbors with inherent haze or land cover changes due to a long-time interval between images. The proposed method was validated through experiments on both simulated and real haze images as well as comparison with five state-ofthe-art benchmark methods. This research provides a new paradigm for enhancing haze removal by incorporating temporally neighboring images.
      原文链接:
    • NSTL
    • Elsevier

    A global 30-m ET model (HSEB) using harmonized Landsat and Sentinel-2, MODIS and VIIRS: Comparison to ECOSTRESS ET and LST

    Jaafar, HadiMourad, RoyaSchull, Mitch
    24页
    查看更多>>摘要：Advances in earth observation science in recent years have contributed to improving the quantification of evapotranspiration (ET) at field, regional and global scales. Many studies have stressed the need for a high temporal and spatial resolution ET product that minimizes the bias between modeled and actual water use for proper water accounting. We present a hybrid single-source energy balance (HSEB) model that calculates evapotranspiration at the field-scale based on the synergistic use of Sentinel-2, Landsat, Visible Infrared Imaging Radiometer Suite (VIIRS) and (Moderate Resolution Imaging Spectroradiometer) MODIS land surface temperature products. The model operates in Google Earth Engine as a time series using global atmospheric variables and 100-m Copernicus Land cover data. Evaluation of HSEB for calculating evaporation over 29 flux tower sites within an extensive range of climatic conditions and biomes over the US, Europe and Australia for 2018-2020 shows that the model significantly improves the temporal and spatial components of ET mapping. Overall, HSEB performed well in all considered biome types and climatic conditions (r = 0.81, 0.74, and 0.8, a Nash-Sutcliff efficiency of 0.6, 0.74, 0.8, and a bias of 4%, 1%, and - 0.9% at the daily, weekly, and monthly scales, respectively). Root Mean Square Error averaged at 1.31 mm/day. Comparison of instantaneous latent heat fluxes of the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) against tower data shows that HSEB produces acceptable results, although ECOSTRESS had a slightly lower bias (3.8% for HSEB vs. -0.8% for ECOSTRESS). HSEB performed better over croplands. We also discuss comparisons of sharpened LST from VIIRS VNP02, VNP21, MODIS, Landsat, Sentinel-3, and LST from ECOSTRESS versus ground LST measurements, and briefly discuss the sensitivity of HSEB to the thermal data used. Both ECOSTRESS and Landsat showed better performance at different LST ranges and time of day when compared to LST observation collected over a small potato field in Lebanon. The analysis of impact of LST product used in HSEB on ET results at US-ARM site showed that HSEB with MODIS LST outperforms HSEB ET from VNP02 (an overestimate) and VNP21 (an underestimate). We conclude that HSEB can be used as an operational global model for monitoring evaporative stress and evaporation at the small-agriculture field level with higher temporal and spatial resolution utilizing the wide suite of available satellite data.
      原文链接:
    • NSTL
    • Elsevier

    DART-Lux: An unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images

    Wang, YingjieKallel, AbdelazizYang, XueboRegaieg, Omar...
    20页
    查看更多>>摘要：Accurate and efficient simulation of remote sensing images is increasingly needed in order to better exploit remote sensing observations and to better design remote sensing missions. DART (Discrete Anisotropic Radiative Transfer), developed since 1992 based on the discrete ordinates method (i.e., standard mode DART-FT), is one of the most accurate and comprehensive 3D radiative transfer models to simulate the radiative budget and remote sensing observations of urban and natural landscapes. Recently, a new method, called DART-Lux, was integrated into DART model to address the requirements of massive remote sensing data simulation for large-scale and complex landscapes. It is developed based on efficient Monte Carlo light transport algorithms (i.e., bidirectional path tracing) and on DART model framework. DART-Lux can accurately and rapidly simulate the bidirectional reflectance factor (BRF) and spectral images of arbitrary landscapes. This paper presents its theory, implementation, and evaluation. Its accuracy, efficiency and advantages are also discussed. The comparison with standard DART-FT in a variety of scenarios shows that DART-Lux is consistent with DART-FT (relative differences <1%) with simulation time and memory reduced by a hundredfold. DART-Lux is already part of the DART version freely available for scientists (https://dart.omp.eu).
      原文链接:
    • NSTL
    • Elsevier

    Impact of temporal compositing on nighttime light data and its applications

    Zheng, QimingWeng, QihaoZhou, YuyuDong, Baiyu...
    13页
    查看更多>>摘要：In recent decades, nighttime light (NTL) images have been widely explored to portray human footprints. Most of the studies used monthly or yearly temporal composite NTL products as a solution for invalid observations due to cloud coverage and outlier signals. However, the impact of temporal compositing on NTL data and its applications remains largely unclear. Here, we utilized over 180,000 daily NTL tiles from NASA's Black Marble VIIRS product (VNP46A2, 2012-2020), covering 230 cities from China and the United States, to delve into the influence of temporal compositing on valid pixel coverage and spatiotemporal pattern of NTL data and the performance of three representative types of NTL-based applications. Our analysis showed temporal compositing was an imperative and efficient solution to the prevailing invalid observations. On average a 16-day composite was required to ensure at least 95% of valid pixel coverage for a city, where a longer composite period was needed for cities in a pluvial temperate climate zone. Compositing daily NTL data into a 3-day to 31-day period markedly reduced its spatiotemporal variation and incurred a 3-9 nWatts/cm2/sr, or 22%-37%, absolute difference in NTL magnitude, which was particularly high in developed cities and intra-city areas. We attributed such effect to the number of valid observations available for generating the composite data and the extremely high variation in daily NTL stemmed from human activities, as well as the uncertainties in VNP46 product and VIIRS instrument. The impact of temporal compositing on NTL-based applications varied greatly, from insignificant to very sensitive, across application types and spaces. Our analysis provides a comprehensive understanding of the capability and uncertainties in NTL data processing and applications, facilitating end-users to make the best use of NTL observations in high temporal frequency.
      原文链接:
    • NSTL
    • Elsevier