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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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    Using 3D robust smoothing to fill land surface temperature gaps at the continental scale

    Pham, Hung T.Kim, SeokhyeonMarshall, LucyJohnson, Fiona...
    6页
    查看更多>>摘要：Land surface temperature (LST) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products has been widely applied in environmental studies, and natural disaster management. However missing data in space and time due to cloud contamination, cloud shadows and atmospheric conditions has hindered its application. Accurate gap filling algorithms for a large spatiotemporal scale of LST data are necessary to enhance the utility of this product. This study applied a three-dimensional (3-D) gap-filling method to fill gaps in 9 years of LST data over Australia (2002-2011). As the gap-filling method relies on a smoothing parameter that controls the accuracy of the reconstruction algorithm, we estimated optimal smoothing parameters to separately reconstruct daytime and nighttime LST products. The reconstructed LST were validated against ground-based LST obtained from the OzFlux network and recommendations made on appropriate smoothing parameters. The results demonstrate that the gap-filling algorithm provides an accurate approach to generating reconstructed LST products for a long period over large spatial scales.
      原文链接:
    • NSTL
    • Elsevier

    Canopy height estimation with TanDEM-X in temperate and boreal forests

    Aumann, CraigErasmi, StefanSchlund, MichaelMagdon, Paul...
    13页
    查看更多>>摘要：Various semi-empirical models for linking PolInSAR data (polarimetric synthetic aperture radar interferometry) to canopy height of vegetation exist. However, only single-polarized data were used during the TanDEM-X mission in order to create a global digital elevation model (DEM). Therefore, simplifications of the semi-empirical models have to be applied to use the PolInSAR models for canopy height estimation with single-polarized TanDEM-X data. We extracted the volume coherence from TanDEM-X acquisitions and used a linear as well as a sinc model for the estimation of canopy height, which are based on the semi-empirical Random Volume over Ground model (RVoG). Both, the linear as well as the sinc model, were applied in temperate forests of Germany and boreal forests of Canada. The estimated canopy height was validated with LiDAR based canopy height models. In general, the sine model resulted in higher coefficients of determination R-2 from 0.08 to 0.64 and lower root mean squared errors (RMSE) between 4.8 m and 12.5 m compared to the linear model with R-2 values between 0.08 and 0.62 (RMSE = 5.4 m to 13.5 m). Higher accuracies were generally achieved in winter and with higher height of ambiguity.
      原文链接:
    • NSTL
    • Elsevier

    Cloud detection algorithm using SVM with SWIR2 and tasseled cap applied to Landsat 8

    Wynne, Randolph H.Joshi, Pratik P.Thomas, Valerie A.
    10页
    查看更多>>摘要：Landsat satellite images are subject to cloud cover effects resulting in erroneous analysis and observations of ground features. In this work, we present a novel algorithm (STmask) combining tasseled cap band 4 (TC4) with short wave infrared spectral band 2, SWIR2 (2.107-2.294 mu m) for generating cloud, water, shadow, snow and vegetation masks. A support vector machine (SVM) with a non-linear kernel is trained on a feature space of TC4 versus SWIR2 for generating feature masks. To develop a generic and unbiased algorithm, the SVM is trained using reference data comprised of 12891 pixels from Landsat 8 scenes from ten spatially and temporally diverse biomes including deciduous forest, rainforest, great plain, savanna, desert, ocean, freshwater, taiga, tundra, and icesheet. 960000 text pixels spanning 96 scenes across 8 biomes from the USGS Landsat cloud cover assessment data set are used for accuracy assessment of STmask as well as to compare its performance with the operational Landsat algorithm, C function of mask (CFmask). Using McNemar's statistic, STmask is shown to maximize both the precision and sensitivity of the classification of all features compared to CFmask. It addresses the challenges of CFmask through statistically significant improvement in the precision of cloud detection over snow/ice, barren, water, urban, and shrubland biomes. Aggregated over all biomes, the average improvement in cloud detection over CFmask is observed to be similar to 3.8% using the F-measure. The classification of non-cloud features exhibits promising improvements and mostly comparable performance to CFmask. Overall classification performance is promising, and thus STmask is a novel, biome-independent, parsimonious, and computationally efficient alternative (and/or a cloud screening addition) to the operational CFmask algorithm. The work is timely and is targeted as an innovative processing solution for the land surface remote sensing research community.
      原文链接:
    • NSTL
    • Elsevier

    Monitoring policy-driven crop area adjustments in northeast China using Landsat-8 imagery

    Mijiti, RuzemaimaitiYang, LingboWang, LiminHuang, Jingfeng...
    18页
    查看更多>>摘要：To keep pace with increasing demands for food, China is advancing robust policies on food security, and policy-oriented adjustments in crop planting area are needed to strike a balance among environmental sustainability, socio-economic development, agriculture and food security. To this end, the crop area adjustment policy was introduced in northeast China since 2015 and the policy provides incentives for quick planting area changes between different crops. However, geospatial data that quantify the magnitude and direction of these crop area adjustments are grossly inadequate in content and accuracy, thereby limiting our understanding of the overall policy impacts. To fill these data needs, applying the random forest (RF) classification algorithm on temporal images of Landsat-8 Operational Land Imager (011) acquired over the Heilongjiang Province (1235 scenes) in 2015 and 2016, this study proposed an integrated approach to the identification and monitoring of adjustments in the planting areas of maize, soybean and paddy rice. Overall crop classification accuracies of 88.7% and 87.9% were obtained for 2015 and 2016, respectively, and our approach further accounted for localized error distribution patterns. The result indicated that the area of soybean and maize had a significant change in 2016 (maize area decreased by 22.0% with about 1,261,775 ha and soybean area increased by 39.8% with about 696,653 ha, compared to 2015). The expansion in soybean-planted area recorded in this study is mostly in regions that previously mapped suitable for soybean cultivation based on local agro-climatic factors. The decrease in maize-planted area in tandem with the increase in soybean-planted area is significant in the northern parts of the Songneng Plain where initial profit differentials between the two crops are minimal due to being more suitable for soybean, and where with the introduction of subsidies, soybean recorded higher profits per unit area than maize. This suggests that with more incentives to cover the profit margin between soybean and maize, or the direction of subsidies to areas where soybean naturally records higher yields as a function of land suitability, the crop area adjustment policy is expected to achieve its goal in Heilongjiang. The current study, having provided information on the relative shifts in agricultural land-uses as informed by agricultural policy and land suitability data at a biannual scale, more detailed studies that would apply the proposed approach to multiple years of satellite data are therefore encouraged to provide information on the dynamics of agricultural land-use as informed by climate-induced changes inland suitability, market demand and government policy.
      原文链接:
    • NSTL
    • Elsevier

    A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing

    Pelta, RanCarmon, NimrodBen-Dor, Eyal
    15页
    查看更多>>摘要：One of the most ubiquitous and detrimental types of environmental contamination in the world is crude oil pollution. When released into either the aquatic or terrestrial environments, this pollution can negatively impact flora and fauna, as well as human health. Hence, a rapid and affordable spatial assessment of the pollution is favored to limit the spill's effects. Using airborne hyperspectral remote sensing (HRS) for crude oil detection in terrestrial areas has been investigated in previous studies, which mainly relied on heavily oiled artificial samples. These studies and others based their methodologies on the premise that the spectral features of petroleum hydrocarbon (PHC) are clearly observable, which might not be true in all cases. In this study, we aimed at assessing the true potential of using HRS for terrestrial oil spill mapping in a real disaster site in southern Israel, where laboratory and controlled conditions do not apply. Using the AISA SPECIM Fenixl K sensor, we collected airborne image of the study site and analyzed the data with advanced data mining techniques. Various challenges and limitations arose from the airborne HRS image being taken two and a half years after the crude oil had been released into the environment and exposed to the surface. Here, no spectral features of PHC were detectable in the spectrum, preventing the use of PHC indices and spectral methods developed by others. Nevertheless, by using standardization techniques, vicarious band selection, dimension reduction, multivariate calibration, and supervised machine-learning, we were able to successfully distinguish between contaminated pixels from non-contaminated ones. Classification accuracy metrics of overall accuracy, sensitivity, specificity, and Kappa yielded good results of 0.95, 0.95, 0.95 and 0.9, respectively, for cross-validation, and 0.93, 0.91, 0.94 and 0.85, for the validation dataset. Classified image and test scenes also showed strong agreement with an orthophoto image taken several days after the disaster, when the pollution was clearly visible. Thus, we conclude that HRS technology can detect PHC traces in an oil spill site, even under the most challenging conditions.
      原文链接:
    • NSTL
    • Elsevier

    Exploring improvements to the design of an operational seasonal forage scarcity index from NDVI time series for livestock insurance in East Africa

    De Oto, LucasVrieling, AntonFava, Francescode Bie, Kees C. A. J. M....
    13页
    查看更多>>摘要：Recurrent drought represents a major threat in arid and semi-arid regions of East Africa where pastoralists depend on their livestock for subsistence. In Kenya and southern Ethiopia, an existing satellite-based index insurance scheme aims to protect pastoralists against the adverse effects of drought. Under that scheme, payouts are made based on an area-aggregated seasonal forage scarcity index derived from remotely-sensed Normalized Difference Vegetation Index (NDVI). NDVI values are directly averaged per unit areas of insurance (UAI), which are based on administrative borders but take into limited account the ecological variability within the unit. The choice of administrative boundaries at the onset of the analysis may negatively impact the performance of the product. Our study explores an alternative index design based on an ecological stratification of the study area. First, we performed an unsupervised classification of NDVI time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) to group pixels with similar temporal NDVI trajectories. Next, we used average NDVI profiles and ancillary data to discard areas deemed insignificant for forage production. We then transformed NDVI values into z-scores to assess how each pixel relates to the multi-year distribution of NDVI values per class and season. In the final step, we calculated the alternative forage scarcity index, which is the percentage of pixels with anomalously low NDVI values per season and UAI (i.e. z-score <= -1.0). To evaluate its performance, we compared unit-level results for both the original and alternative designs against spatially-aggregated monthly household survey data on livestock mortality from 16 sample sites corresponding to eight administrative units within the study area. Besides performing better in predicting livestock mortality (i.e. increases of 53% and 39% in correlation coefficients as measured by Pearson's r and Spearman's rho, respectively) and strengthening the ecological significance of the index, the proposed design has a number of other advantages: 1) the index is calculated using a much larger statistical basis, 2) it allows for analysis of forage conditions at subunit level, and 3) it offers a more flexible structure for payout calculation. These advantages could be of particular relevance for expanding the index-insurance scheme to agro-pastoral regions characterized by more heterogeneous landscapes. Finally, we propose to consider using this approach to better account for the ecological variability of rangelands in other NDVI-based early warning and monitoring systems.
      原文链接:
    • NSTL
    • Elsevier

    Land subsidence in Beijing and its relationship with geological faults revealed by Sentinel-1 InSAR observations

    Hu, LeyinDai, KerenXing, ChengqiLi, Zhenhong...
    10页
    查看更多>>摘要：Beijing, the capital city of China, has been affected by land subsidence due to intensive groundwater extraction since 1935. Recent studies reported that the maximum subsidence occurred in the east of Beijing, reaching more than 11 cm/year till 2017. To investigate the subsidence (2015(similar to)2017) in Beijing, in this paper, time series interferometric synthetic aperture radar (TS-InSAR) analysis was performed with 22 Sentinel-1 Terrain Observation by Progressive Scans (TOPS) mode SAR data. Results show that wide areas in the east of Beijing are subsiding with a maximum rate of 14 cm/year, which is consistent with GPS data. Detailed analysis of the obtained subsidence map reveals the existence of several characteristic near-linear boundaries within the subsiding areas. As a result of our previous three-year project, we generated the exact location, direction, and late Quaternary activity of the main potential active faults in the Beijing area. The relationship between the trace of these existing geological faults and the mentioned subsidence boundaries was investigated in detail. It is suggested that land subsidence in Beijing was mainly caused by the over-extraction of groundwater, with its spatial pattern being controlled by geological faults.
      原文链接:
    • NSTL
    • Elsevier

    Apple orchard inventory with a LiDAR equipped unmanned aerial system

    Hadas, EdytaJozkow, GrzegorzWalicka, AgataBorkowski, Andrzej...
    10页
    查看更多>>摘要：Knowledge about the number of trees in an orchard and their geometric parameters is beneficial in precise farming and together with other information may be used to predict the yield. These parameters can be obtained based on time-consuming field measurements or more effectively, from very high resolution 3D data collected with Unmanned Aerial Vehicles (UAV). Numerous UAV experiments have been conducted in agricultural areas; however, most of studies are limited to the use of a passive optical sensor (camera). This study demonstrates an experiment on the novel remote sensing approach of determining selected geometric parameters of trees in an apple orchard, based on a high-density point cloud obtained from a Velodyne HDL-32E laser scanner mounted on a small UAV platform Leica Aibot X6 V2. Reference data of selected geometric parameters of trees was obtained from orthophotomap and with geodetic surveying methods. Original and robust methodology is proposed for the point cloud processing, which is the inventive combination of an alpha-shape algorithm, principal component analysis and detection of local minima on crown profiles. The developed approach allowed for the correct identification of 99% of the trees in the test orchard. The root mean square error of determined crown areas was equal to 0.98 m(2). The accuracy of tree top identification, tree height and crown base height determination was equal to 0.38, 0.09 and 0.09 m, respectively.
      原文链接:
    • NSTL
    • Elsevier

    Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series

    Bendini, Hugo do NascimentoFonseca, Leila Maria GarciaSchwieder, MarcelKorting, Thales Sehn...
    10页
    查看更多>>摘要：The paradox between environmental conservation and economic development is a challenge for Brazil, where there is a complex and dynamic agricultural scenario. This reinforces the need for effective methods for the detailed mapping of agriculture. In this work, we employed land surface phenological metrics derived from dense satellite image time series to classify agricultural land in the Cerrado biome. We used all available Landsat images between April 2013 and April 2017, applying a weighted ensemble of Radial Basis Function (RBF) convolution filters as a kernel smoother to fill data gaps such as cloud cover and Scan Line Corrector (SLC)-off data. Through this approach, we created a dense Enhanced Vegetation Index (EVI) data cube with an 8-day temporal resolution and derived phenometrics for a Random Forest (RF) classification. We used a hierarchical classification with four levels, from land cover to crop rotation classes. Most of the classes showed accuracies higher than 90%. Single crop and Non-commercial crop classes presented lower accuracies. However, we showed that phenometrics derived from dense Landsat-like image time series, in a hierarchical classification scheme, has a great potential for detailed agricultural mapping. The results are promising and show that the method is consistent and robust, being applicable to mapping agricultural land throughout the entire Cerrado.
      原文链接:
    • NSTL
    • Elsevier

    Timing of red-edge and shortwave infrared reflectance critical for early stress detection induced by bark beetle (Ips typographus, L.) attack

    Abdullah, HaidiSkidmore, Andrew K.Darvishzadeh, RoshanakHeurich, Marco...
    13页
    查看更多>>摘要：Forest disturbance in Europe, induced by European spruce bark beetle Ips typographus, L., results in regionalscale dieback. Early stress detection in Norway spruce stands caused by bark beetle infestation at the green attack stage (when trees are yet to show distinct symptoms observable by the human eye) is crucial and can lead to improved forest management and reduced economic losses. This study aims to investigate and understand the dynamics of leaf traits and reflectance of Norway spruce (Picea abies) trees during bark beetle attack. Using high-resolution temporal images from RapidEye and SPOT-5 in parallel with the collection of field data, we examined which spectral regions and leaf traits are affected by infestation over time and how they help the discrimination between healthy and infested plots at the early stage of the attack. To achieve this aim, we used a novel approach by targeting both leaf and canopy level. We measured leaf reflectance spectra and six leaf traits (water content, nitrogen, chlorophyll fluorescence, chlorophyll and stomatal conductance) from 66 (30 plots) healthy and 54 (8 plots) infested trees at three consecutive time measurements in the summer of 2015 in the Bavarian Forest National Park. Concurrently, canopy reflectance and spectral vegetation indices (SVIs) were extracted from the selected plots (30 healthy plots) using seven RapidEye images and six SPOT-5 images. For the infested plots, in addition to the field measured plots (8), canopy spectral reflectance were extracted from the reference infestation data (291 plots) obtained through visual interpretation of high-resolution aerial photographs. Results demonstrated significant differences (p < 0.05) in the studied leaf traits between healthy and infested samples, and these differences increased with the progression of infestation. We found that leaf and canopy reflectance were significantly higher (p <= 0.05) for the infested trees by bark beetle than the healthy ones in the 'red edge' (680-790 nm) and 'shortwave infrared' (1110-1490 nm) spectrum throughout the infestation event. Our results further demonstrated that the spectral vegetation indices calculated from the red-edge and SWIR spectral bands, such as NDRE, DSWI, LWCI and NDWI, were able to differentiate between healthy and infested trees earlier than the other SVIs. The new insight offered by these results is that the red-edge and SWIR spectral information from multispectral satellites has the potential to considerably improve monitoring and detection of forest stress and has important implications for European field bark beetle management and future studies.
      原文链接:
    • NSTL
    • Elsevier