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International journal of applied earth observation and geoinformation
International Institute for Aerospace Survey and Earth Sciences
International journal of applied earth observation and geoinformation

International Institute for Aerospace Survey and Earth Sciences

1569-8432

International journal of applied earth observation and geoinformation/Journal International journal of applied earth observation and geoinformationISTPSCIAHCI
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    Supraglacial rivers on the northwest Greenland Ice Sheet, Devon Ice Cap, and Barnes Ice Cap mapped using Sentinel-2 imagery

    Yang, KangSmith, Laurence C.Sole, AndrewLivingstone, Stephen J....
    13页
    查看更多>>摘要：Supraglacial rivers set efficacy and time lags by which surface meltwater is routed to the englacial, subglacial, and proglacial portions of ice masses. However, these hydrologic features remain poorly studied mainly because they are too narrow (typically < 30 m) to be reliably delineated in conventional moderate-resolution satellite images (e.g., 30 m Landsat-8 imagery). This study demonstrates the utility of 10 m Sentinel-2 Multi-Spectral Instrument images to map supraglacial rivers on the northwest Greenland Ice Sheet, Devon Ice Cap, and Barnes Ice Cap, covering a total area of similar to 10,000 km(2). Sentinel-2 and Landsat-8 both capture overall supraglacial drainage patterns, but Sentinel-2 images are superior to Landsat-8 images for delineating narrow and continuous supraglacial rivers. Sentinel-2 mapping across the three study areas reveals a variety of supraglacial drainage patterns. In northwest Greenland near Inglefield Land, subparallel supraglacial rivers up to 55 km long drain meltwater directly off the ice sheet onto the proglacial zone. On the Devon and the Barnes ice caps, shorter supraglacial rivers (up to 15-30 km long) are commonly interrupted by moulins, which drain intemally drained catchments on the ice surface to subglacial systems. We conclude that Sentinel-2 offers strong potential for investigating supraglacial meltwater drainage patterns and improving our understanding of the hydrological conditions of ice masses globally.
      原文链接:
    • NSTL
    • Elsevier

    Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery

    Melville, BethanyFisher, AdrianLucieer, Arko
    11页
    查看更多>>摘要：Vegetation cover is a key environmental variable often mapped from satellite and aerial imagery. The derivation of ultra-high spatial resolution fractional vegetation cover (FVC) based on multispectral imagery acquired from an Unmanned Aerial System (UAS) has several applications, including the potential to revolutionise the collection of field data for calibration/validation of satellite products. In this study, abundance maps were derived using three methods, applied to data collected in a typical Australian rangeland environment. The first method used downscaling between Landsat FVC maps and UAS images with Random Forest regression to predict bare ground, photosynthetic vegetation and non-photosynthetic vegetation cover. The second method used spectral unmixing based on endmembers identified in the multispectral imagery. The third method used an object-based classification approach to label image segments. The accuracy of all UAS FVC and Landsat FVC products were assessed using 20 field plots (100 m diameter star transects), as well as from 138 ground photo plots. The classification method performed best for all cover fractions at the 100 m plot scale (12-13% RMSE), with the downscaling approach only able to accurately predict photosynthetic cover. The downscaling and unmixing generally over-predicted non-photosynthetic vegetation associated with Chenopod shrubs. When compared with the high-resolution photo plot data, the classification method performed the worst, while the downscaling and unmixing methods achieved reasonable accuracy for the photosynthetic component only (12-13% RMSE). Multispectral UAS imagery has great potential for mapping photosynthetic vegetation cover in rangelands at ultra-high resolution, though accurately separating non-photosynthetic vegetation and bare ground was only possible when the data was scaled-up to coarser resolutions.
      原文链接:
    • NSTL
    • Elsevier

    A simple and integrated approach for fire severity assessment using bi-temporal airborne LiDAR data

    Hu, TianyuMa, QinSu, YanjunBattles, John J....
    14页
    查看更多>>摘要：This study proposes a new method (profile area change, PAC) to quantify fire-induced forest structural changes at the individual tree and clump of trees scales using pre- and post-fire LiDAR data. The PAC measures the difference in profile area summarized from pre- and post-fire LiDAR points. We applied the PAC method to assess the effects of the 2013 American Fire in the Sierra Nevada, California, USA. Our LiDAR PAC metrics were compared to changes in commonly used LiDAR-derived canopy cover and tree height metrics at tree level, and to Landsat-8 imagery-derived relative differenced normalized burn ratio (RdNBR) at the 30m pixel level. A quantitative validation using field measured changes in basal area and leaf area index (LAI) confirmed that correlations between PAC metrics and field measurements (R-2 >= 0.67) were significantly higher than those from canopy cover or tree height metrics (R-2 <= 0.43), and much stronger than that from RdNBR (R-2 <= 0.26). The PAC metrics can also be used to infer the extent of tree canopy disturbance caused by fire, based on whether the majority of biomass loss occurred above or below the tree crown base height. Mapping of canopy disturbance indicated that over half (57.0%) of the American Fire region had tree canopy loss from fire, 22.5% of trees had sub-canopy loss, while the remaining area had no detectable tree canopy change. Overall, the LiDAR PAC metric, as a simple and integrated method, demonstrated promising potential in characterizing fine-grained changes in forest structure. The method can be beneficial for forest managers in evaluating fire-induced environmental and economic losses, and provide useful information for forest restoration design.
      原文链接:
    • NSTL
    • Elsevier

    Collaborative inversion heavy metal stress in rice by using two-dimensional spectral feature space based on HJ-1 A HSI and radarsat-2 SAR remote sensing data

    Li, XuqingLi, LongLiu, Xiangnan
    14页
    查看更多>>摘要：Accurate and extensive monitoring of heavy metal pollution levels in rice fields is crucial to agricultural production and food safety. Most previous studies used optical remote sensing data to monitor heavy metal stress in rice in which way the remote sensing features are not diversified and the monitoring accuracy is relatively low. Compared with that optical remote sensing can monitor features like color and cell internal structure, microwave remote sensing can monitor the morphology and geometrical features. The complementary characteristics of multi-source remote sensing data are reflected in two aspects. On the one hand, the high spectral resolution characteristics of hj-1a HIS and the high spatial resolution characteristics of radarsat-2 SAR satellite are used for feature fusion to obtain the image data of high altitude spectral resolution; on the other hand, the two remote sensing data can depict the vegetation characteristics from different angles. In order to fully extract the characteristics of crop stress, the model was constructed by utilizing the complementary characteristics of multi source remote sensing data. This paper synergized optical and microwave remote sensing to construct the crop heavy metal stress monitoring model. To this end, certain rice-growing areas polluted by heavy metal in Suzhou city were selected as the experimental areas, where ASD spectrum data, biochemical parameters and heavy metal content data of rice were collected during critical growth periods on one hand, HJ-1 A HIS and Radarsat-2 SAR satellite data were obtained almost simultaneously on the other hand. Based on heavy metal stress-responsive mechanism of rice, NVI (R598, R508) and SVI(HV,VH,HH,VV), spectral indexes sensitive to heavy metal stress, were extracted from the optical and radar data respectively to construct a two-dimensional feature space, based on which an optical-and-radar-remote-sensing-combined model for monitoring heavy metal stress in rice was constructed. In this paper, the study and main conclusions are as follows:(1) It used spectral characteristics analysis combined with statistic methods or random forest algorithm to build the canopy chlorophyll index NVI, which is sensitive to chlorophyll content changes of rice under heavy metal stress, and a heavy metal stress level inversion model was built based on hyperspectral HSI data accordingly. Also, it used statistic method to build the microwave index SVI, which is sensitive to biomass changes of rice under heavy metal stress, and a heavy metal stress level inversion model was built based on microwave SAR images as a result. (2) A combination of biochemical and morphological parameters, both responding to heavy metal stress, was synergized with NVI and SVI, sensitive to chlorophyll and biomass changes respectively, to build a two-dimensional feature space. Heavy metal stress levels were classified in this space. Thus, a synergetic model to retrieve heavy metal stress based on multi-source remote sensing data was developed. In this paper, the innovation lies in a model constructed by synergizing optical and radar remote sensing data to monitor heavy metal stress in rice based on a multi-dimensional spectral feature space and this model can be applied to monitoring multiple environmental stresses in crops.
      原文链接:
    • NSTL
    • Elsevier

    Combinational shadow index for building shadow extraction in urban areas from Sentinel-2A MSI imagery

    Sun, GenyunHuang, HuiWeng, QihaoZhang, Aizhu...
    13页
    查看更多>>摘要：Images from Multispectral Instrument (MSI) on Sentinel-2A are useful for studies of urban environments and their spatio-temporal changes. However, the frequent occurrence of building shadows in urban areas brings about great challenges in urban studies. Existing building shadow indices are not effective for Sentinel-2A MSI images due to that these indices do not make full use of the rich spectral information contained in Sentinel-2A. In this study, we propose a combinational shadow index (CSI) to address this challenge. In the formulation of CSI, three features, including the proposed shadow enhancement index (SEI), the normalized difference water index (NDWI) and the NIR band (B8), were combined to separate spectrally similar objects, such as building shadows, water and low albedo features at the Earth surface. The accuracy and robustness of CSI were tested by using six data sets from four cities, including Beijing, Shanghai, Guangzhou and Shenzhen in China. The performance of CSI was compared with three existing shadow indices, i.e., P algorithm, the normalized saturation-value difference index (NSVDI) and the shadow index (SI). Results show that CSI can detect building shadows with fine structures in both clear and cloudy images more effectively and worked well on large areas too. CSI can separate shadows from water and low albedo features as measured by a spectral discrimination index (SDI). Compared with the existing building shadow indices, CSI can improve the performance of building shadow detection by combining the feature information of SEI, NDWI and NIR band, and yielded satisfactory results for extracting building shadows from Sentinel-2A MSI imagery.
      原文链接:
    • NSTL
    • Elsevier

    Application of the MODIS MOD 17 Net Primary Production product in grassland carrying capacity assessment

    De Leeuw, JanNamazov, ElmaddinBayramov, EmilMarshall, Michael T....
    11页
    查看更多>>摘要：Remote sensing based grassland carrying capacity assessments are not commonly applied in rangeland management. Possible reasons for this include non-equilibrium thinking in rangeland management, and the costliness of existing remotely sensed biomass estimation that carrying capacity assessments require. Here, we present a less demanding approach for grassland biomass estimation using the MODIS Net Primary Production (NPP) product and demonstrate its use in carrying capacity assessment over the mountain grasslands of Azerbaijan. Based on publicly available estimates of the fraction of total NPP partitioned to aboveground NPP (fANPP) we calculate the aboveground biomass produced from 2005 to 2014. Validation of the predicted aboveground biomass with independent field biomass data collected in 2007 and 2008 confirmed the accuracy of the aboveground biomass product and hence we considered it appropriate for further use in the carrying capacity assessment. A first assessment approach, which allowed for consumption of 65% of aboveground biomass, resulted in an average carrying capacity of 12.6 sheep per ha. A second more realistic approach, which further restricted grazing on slopes steeper than 10%, resulted in a stocking density of 6.20 sheep per ha and a carrying capacity of 3.93 million sheep. Our analysis reveals overgrazing of the mountain grasslands because the current livestock population which consists of at least 8 million sheep, 0.5 million goats and an unknown number of cattle exceeds the predicted carrying capacity of 3.93 million sheep. We consider that the geographically explicit advice on sustainable stocking densities is particularly attractive to regulate grazing intensity in geographically varied terrain such as the mountain grasslands of Azerbaijan. We further conclude that the approach, given its generic nature and the free availability of most input data, could be replicated elsewhere. Hence, we advise considering its use where traditional carrying capacity assessments are difficult to implement.
      原文链接:
    • NSTL
    • Elsevier

    Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: a case study in northern Portugal

    Marcos, BrunoAlcaraz-Segura, DomingoCunha, MarioHonrado, Joao P....
    9页
    查看更多>>摘要：Wildfires constitute an important threat to human lives and livelihoods worldwide, as well as a major ecological disturbance. However, available wildfire databases often provide incomplete or inaccurate information, namely regarding the timing and extension of fire events. In this study, we described a generic framework to compare, rank and combine multiple remotely-sensed indicators of wildfire disturbances, in order to not only select the best indicators for each specific case, as well as to provide multi-indicator consensus approaches that can be used to detect wildfire disturbances in space and time. For this end, we compared the performance of different remotely-sensed variables to discriminate burned areas, by applying a simple change-point analysis procedure on time-series of MODIS imagery for the northern half of Portugal, without external information (e.g. active fire maps). Overall, our results highlight the importance of adopting a multi-indicator consensus approach for mapping and detecting wildfire disturbances at a regional scale, that allows to profit from spectral indices capturing different aspects of the Earth's surface, and derived from distinct regions of the electromagnetic spectrum. Finally, we argue that the framework here described can be used: (i) in a wide variety of geographical and environmental contexts; (ii) to support the identification of the best possible remotely-sensed functional indicators of wildfire disturbance; and (iii) for improving and complementing incomplete wildfire databases.
      原文链接:
    • NSTL
    • Elsevier

    Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing

    Liang, ShunlinCarter, Corinne
    7页
    查看更多>>摘要：Remote sensing retrieval of evapotranspiration (ET), or surface latent heat exchange (LE), is of great utility for many applications. Machine learning (ML) methods have been extensively used in many disciplines, but so far little work has been performed systematically comparing ML methods for ET retrieval. This paper provides an evaluation of ten ML methods for estimating daily ET based on daily Global LAnd Surface Satellite (GLASS) radiation data and high-level Moderate-Resolution Imaging Spectroradiometer (MOD'S) data products and ground measured ET data from 184 flux tower sites. Measurements of accuracy (RMSE, R-2, and bias) and run time were made for each of ten ML methods with a smaller training data set (n = 7910 data points) and a larger training data set (n = 69,752 data points). Inclusion of more input variables improved algorithm performance but had little effect on run time. The best results were obtained with the larger training data set using the bootstrap aggregation (bagging) regression tree (validation RMSE = 19.91 W/m(2)) and three hidden layer neural network (validation RMSE = 20.94 W/m(2)), although the less computationally demanding random kernel (RKS) algorithm also produced good results (validation RMSE = 22.22 W/m(2)). Comparison of results from sites with different ecosystem types showed the best results for evergreen, shrub, and grassland sites, and the weakest results for wetland sites. Generally, performance was not improved by training with data from only the same ecosystem type.
      原文链接:
    • NSTL
    • Elsevier

    Importance of structural and spectral parameters in modelling the aboveground carbon stock of urban vegetation

    Gao, JayWang, Vincent
    9页
    查看更多>>摘要：This study aims to comparatively assess the effectiveness of spectral and structural parameters of vegetation in estimating aboveground carbon (AGC) stock by vegetation type. A total of 38 structural metrics (including 21 percentiles) derived from LiDAR data, and 105 spectral indices and reflectance (including 10 percentiles for each of them) from Landsat 8 imagery were comprehensively assessed. It is found that the best-performing structural parameters vary with vegetation type. Namely, standard deviation of height is the best predictor for trees (R-2 = 0.83) while the mode of height is the best for shrubs (R-2 = 0.64). Of the spectral parameters, GNDVI(p80) (80 percentile of normalised difference vegetation index of green band) is the best for trees (R-2 = 0.51), and Green_Max is the best for shrubs (R-2 = 0.44). Furthermore, the estimation models based on structural parameters (R-2 >= 0.83 and RMSE >= 46.8 Mg C ha(-1) for trees, and R-2 >= 0.57 and RMSE >= 9.3 Mg C ha(-1) for shrubs) are more accurate than those based on spectral parameters (R-2 >= 0.46 and RMSE >= 54 Mg C ha(-1) for tress, and R-2 >= 0.44 and RMSE >= 11.9 Mg C ha(-1) for shrubs) despite the identified inaccuracy in LiDAR-derived height. Nevertheless, the joint consideration of both spectral and structural parameters in the same estimation model does not make it markedly more accurate than those involving either structural or spectral parameters.
      原文链接:
    • NSTL
    • Elsevier

    Comparative analysis of CORINE and climate change initiative land cover maps in Europe: Implications for wildfire occurrence estimation at regional and local scales

    Martin, M. P.Vilar, L.Garrido, J.Echavarria, P....
    16页
    查看更多>>摘要：Updated and harmonized land cover (LC) data is essential for wildfire estimation in fire-prone areas as is the case in southern Europe. CORINE Land cover (CLC) and ESA Climate Change Initiative Land Cover (CCI-LC) maps have been analyzed and compared their performance in the estimation of wildfire occurrence in Europe at regional and local scales for the period 2010-2014. LC maps legends were harmonized and similarities and discrepancies were compared. Overall agreement between the two maps for the whole Europe was (similar to)75%. Forest and agriculture showed the largest agreement, while shrubland and grassland the lowest. Quantity and allocation disagreements were calculated including exchange and shift components (Pontius and Santacruz, 2014) which provided detailed information about the contribution of each class to the overall disagreement. Spatial discrepancies were found in areas where grassland and shrubland were the dominant classes as in United Kingdom or East Turkey. Land Use and Coverage Area frame Survey (LUCAS) was used as ground truth for validation purposes. The agreement with LUCAS was slightly higher for CCI-LC (59%) than for CLC (56%). Generalized Linear Models (GLM), based on presence-absence of wildfires, were used to estimate wildfire occurrence at 3 x 3 km grid cell resolution from both LC maps at the European scale. LC interfaces and climatic variables (temperature and precipitation) where used as explicative variables while fires from European Forest Fire Information System EFFIS (2010-2014 period) were used as response variable. Wildfire occurrence was also estimated with the two maps at local scale in a test region (Zamora, Spain) using a more precise location of the response variable (x, y fire ignition points). At the European scale models obtained within the two maps showed similar results. CCI-LC model sensitivity was 77.26%, specificity 25.89% and omission error 22.74% while CLC model sensitivity was 75.68%, specificity 29.99% and omission error 24.32%. However, CLC performed slightly better in terms of the percent correct classification (62%). In the test region the models achieved better results in terms of specificity (66.07% and 68.93% for CCI-LC and CLC models respectively) and percent correct classification ((similar to)68% for CLC model). At local scale CLC model performed better than CCI-LC model. Wildfire occurrence estimation was more accurate at local scale because of the differences in the spatial accuracy of the response variable used.
      原文链接:
    • NSTL
    • Elsevier