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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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    Estimating above ground biomass as an indicator of carbon storage in vegetated wetlands of the grassland biome of South Africa

    Naidoo, Lavenvan Deventer, HeidiRamoelo, AbelMathieu, Renaud...
    12页
    查看更多>>摘要：Wetlands store higher carbon content relative to other terrestrial ecosystems, despite the small extent they occupy. The increase in temperature and changes in rainfall pattern may negatively affect their extent and condition, and thus the process of carbon accumulation in wetlands. The introduction of the Sentinel series (S1 and S2) and WorldView space-borne sensors (WV3) have enabled monitoring of herbaceous above ground biomass (AGB) in small and narrow wetlands in semi-arid areas. The objective of this study was to assess (i) the capabilities of the high to moderate resolution sensors such as WV3, S1A and S2A in estimating herbaceous AGB of vegetated wetlands using SAR backscatter, optical reflectance bands, vegetation spectral indices (including Leaf Area Index or LAI measurements) and band ratio datasets and (ii) whether significant differences exists between the AGB ranges of wetland and surrounding dryland vegetation. A bootstrapped Random Forest modelling approach, with variable importance selection, was utilised which incorporated ground collected grass AGB for model calibration and validation. WorldView-3 (WV3) yielded the highest AGB prediction accuracies (R-2 = 0.63 and RMSE = 169.28 g/m(2)) regardless of the incorporation of bands only, indices only or the combination of bands and indices. In general, the optical sensors yielded higher modelling accuracies (improvement in R-2 of 0.04-0.07 and RMSE of 11.48-17.28 g/m(2)) than the single Synthetic Aperture Radar (SAR) sensor but this was marginal depending on the scenario. Incorporating Sentinel 1A (S1) dual polarisation channels and Sentinel 2A (S2) reflectance bands, in particular, yielded higher accuracies (improvement in R-2 of 0.03-0.04 and RMSE of 5.4-16.88 g/m(2)) than the use of individual sensors alone and was also equivalent to the performance of the high resolution WV3 sensor results. Wetlands had significantly higher AGB compared to the surrounding terrestrial grassland (with a mean of about 80 g/m(2) more). Monitoring herbaceous AGB at the scale of the wetland extent in semi-arid to arid grasslands enables improved understanding of their carbon sequestration potential, the contributions to global carbon accounting policies and also serving as a proxy for functional intactness.
      原文链接:
    • NSTL
    • Elsevier

    Analysis of broadleaf encroachment in coniferous forest plantations using multi-temporal satellite imagery

    McInerney, DanielKempeneers, PieterMarron, MagdaleneMcRoberts, Ronald E....
    8页
    查看更多>>摘要：In recent years, several critical issues have been identified concerning the performance of recently established spruce forest plantations in Ireland, in particular coniferous reforestation sites. More specifically, the reforestation of peatland areas in the midlands of Ireland have been subject to encroachment by broadleaf species, such as birch, and willow. These species regenerate naturally and compete with, and outgrow, the coniferous species (commonly Norway spruce) that were planted. In many cases, the growth of these trees is faster than the spruce, resulting in stands being completely encroached by approximately 15 years of age. Given the widespread nature of this problem coupled with the fragmented nature of the Irish forest estate, a project was established to use Earth observation data to predict the spatial distribution and species composition of the affected sites. The spatial distribution and associated area estimates of broadleaf encroachment within State owned spruce forests were assessed using multi-temporal Landsat satellite imagery. The overall accuracy of the encroachment map was 85.23% and a Kappa Index of Agreement of 0.58. The associated area of encroached or broadleaf dominated forests was 20,003 ha with a confidence interval of +/- 20% calculated using a sample-based estimator.
      原文链接:
    • NSTL
    • Elsevier

    The potential of retrieving snow line dynamics from Landsat during the end of the ablation seasons between 1982 and 2017 in European mountains

    Hu, ZhongyangDietz, AndreasKuenzer, Claudia
    11页
    查看更多>>摘要：Snow cover in the Northern Hemisphere is continuously decreasing during the ablation seasons in the context of climate change. Snow Line Elevation (SLE) is a suitable indicator illustrating detailed snow cover distribution dynamics at a regional scale. In order to carry out time-series analyses of the SLEs in mountain areas, long-term (> 30 years) and detailed spatial resolution (< 100 m) data are required. In this article, we have retrieved SLEs from Landsat during the end of the ablation seasons in European mountains between 1984 and 2017. Based on our analyses, it is possible to use the Landsat archive to illustrate potential long-term snow line dynamics in the Alps, the Carpathian Mountains, and the Pyrenees. The snow lines appear to recede to higher elevations in these Southern European Mountains. To further implement statistical analyses, it is required to fill the missing observations, and reduce uncertainties induced by intermediate snowfall events.
      原文链接:
    • NSTL
    • Elsevier

    Generating high-temporal and spatial resolution TIR image data

    Herrero-Huerta, M.Laguela, S.Alfieri, S. M.Menenti, M....
    14页
    查看更多>>摘要：Thermal InfraRed (TIR) image data at high temporal and spatial resolution are required to monitor the rapid development of crops during the growing season, taking into account the fragmentation of most agricultural landscapes. Moreover, integrating high-resolution satellite TIR data to calibrate hydrological models is a powerful information to efficiently monitor crop water use. Conversely, no single sensor meets these combined requirements in the TIR spectral region. Data fusion approaches offer an alternative to exploit observations from multiple sensors, providing image data to meet the combined requirements on spatial and temporal resolution. A novel spatio-temporal data fusion workflow based on a multi-sensor multi-resolution algorithm was developed and applied to generate TIR synthetic image data at high temporal and spatial resolution. The workflow includes two steps: in the first step, synthetic daily radiance images at Top of Atmosphere (TOA) and 30-m spatial resolution (at the ground) are generated using TIR radiometric data at TOA collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) daily 1-km and Landsat 8/TIRS 16-day 30-m. This procedure is applied to two image pairs on different dates. The workflow yields an estimator to generate TIR TOA radiance data on any given date, provided a MODIS radiance image is available. The next step applies constrained unmixing of the 30 m (now considered as low-resolution) TIR images using the information about sub-pixel land cover obtained from co-registered images at higher spatial resolution in the VNIR (Visible Near InfraRed) spectrum. In our case study, the L8/TIRS synthetic image data were unmixed to the Sentinel 2/MSI with 10 m x 10 m spatial resolution. Two geographically diverse experiments were carried out using the same procedure: one in The Netherlands to evaluate the procedure and other in Puglia (Italy) to generate a time series of the 10-m x 10-m TIR image data product. The validation experiment, where an actual TIRS image was applied as a reference, gave a RMSE value of 35.3 W/(m(2) mu m sr), which corresponds to a relative value of 8.5% against the TIRS reference values. The results confirm the feasibility of the proposed methodology, which yields a synthetic thermal band to integrate with the multi-spectral data provided by the S2/MSI at 10 m resolution.
      原文链接:
    • NSTL
    • Elsevier

    Comparative analyzes and use of evapotranspiration obtained through remote sensing to identify deforested areas in the Amazon

    Farias da Silva, Helder JoseGoncalves, Weber AndradeBezerra, Bergson Guedes
    12页
    查看更多>>摘要：The objective of this study was to improve the understanding of the spatial dynamics of evapotranspiration (ET) with a focus on the influence of the current level of Amazonian deforestation through comparative statistics of different land cover. The study area comprised the state of Rondonia, Brazil, which was subdivided into homogeneous regions regarding ET using cluster analysis. In addition, we analyzed the use of a logistic regression model to create deforestation maps in the Amazon based on ET fields. We used orbital data on ET and land cover type from the MOD16 product and the Amazon Forest Satellite Monitoring Project (PRODES), respectively, considering the period from 2000 to 2014. The cluster analysis results showed that for the study area, three homogeneous sub-regions were sufficient to represent the ET variability, mainly considering the intensity and seasonal pattern of this process. Regarding the impacts after changing from forest to deforested area, the analyses indicated that the ET of deforested areas decreased by an average of 28% in the dry period and increased by 4% in the rainy season. The effects observed in the rainy season were not significant at 5% significance according to the Student t-test, unlike the dry period, which presented statistical significance (p-value < 0.05). In general, the results indicated that MOD16 data can provide a satisfactory representation of the change in ET in large areas of the Brazilian Amazon. Logistic regression analysis showed that the spatial pattern of deforestation can be identified by biophysical factors such as ET with 87% accuracy, despite the spatial/temporal variability present in the region. These results support this approach as an effective tool for spatial identification of Amazonian deforestation.
      原文链接:
    • NSTL
    • Elsevier

    Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment

    Silveira, Eduarda M. O.Silva, Sergio Henrique G.Acerbi-Junior, Fausto W.Carvalho, Monica C....
    14页
    查看更多>>摘要：The Brazilian Atlantic Forest is a highly heterogeneous biome of global ecological significance with high levels of terrestrial carbon stocks and aboveground biomass (AGB). Accurate maps of AGB are required for monitoring, reporting, and modelling of forest resources and carbon stocks. Previous research has linked plot-level AGB with environmental and remotely sensed data using pixel-based approaches. However, few studies focused on investigating possible improvements via object-based image analysis (OBIA) including terrain related data to predict AGB in topographically variable and mountainous regions, such as Atlantic forest in Minas Gerais, Brazil. OBIA is expected to reduce known uncertainties related to the positional discrepancy between the image and field data and forest heterogeneity, while terrain derivatives are strong predictors in forest ecosystems driving forest biomass variability. In this research, we compare an object-based approach to a pixel-based method for modeling, mapping and quantifying AGB in the Rio Doce basin, within the Brazilian Atlantic Forest biome. We trained a random forest (RF) machine learning algorithm using environmental, terrain, and Landsat Thematic Mapper (TM) remotely sensed imagery. We aimed to: (i) increase the precision of the AGB estimates; (ii) identify optimal variables that fit the best model, with the lowest root mean square error (RMSE, Mg/ha); (iii) produce an accurate map of the AGB for the study area, and subsequently (iv) describing the AGB spatial distribution as a function of the selected variables. The RF object-based model notably improved the AGB prediction by reducing the mean absolute error (MAE) from 28.64 to 20.95%, and RMSE from 33.43 to 20.08 Mg/ha, and increasing the R-2 (from 0.57 to 0.86) by using a combination of selected remote sensing, environmental, and terrain variables. Object-based modelling is a promising alternative to common pixel-based approaches to reduce AGB variability in topographically diverse and heterogeneous environments. Investigation of mapped outcomes revealed a decreasing AGB from west towards the east region of the Rio Doce Basin. Over the entire study area, we map a total of 195,799,533 Mg of AGB, ranging from 25.52 to 238 Mg/ha, following seasonal precipitation patterns and anthropogenic disturbance effects. This study provided reliable AGB estimates for the Rio Doce basin, one of the most important watercourses of the globally important Brazilian Atlantic Forest. In conclusion, we highlight that OBIA is a better solution to map forest AGB than the pixel-based traditional method, increasing the precision of AGB estimates in a heterogeneous and mountain tropical environment.
      原文链接:
    • NSTL
    • Elsevier

    A multisensoral approach for high-resolution land cover and pasture degradation mapping in the humid tropics: A case study of the fragmented landscape of Rio de Janeiro

    De Torres, Friederike NaegeliRichter, RonnyVohland, Michael
    13页
    查看更多>>摘要：Pasture degradation is of increasing global concern as it enforces erosion processes and impacts the carbon storage capacities of the soil. Reliable methods for pasture degradation mapping are thus of great use to provide important information for sustainable landscape planning. Our research focusses on the Guapi-Macacu watershed (Rio de Janeiro (RJ), Brazil) as part of the biodiversity hotspot Mata Atlantica. The area is characterized by strong forest fragmentation and pasture degradation. We investigate the suitability of RapidEye and Landsat 5 TM data in comparison to a high-resolution image composite product based on RapidEye and downscaled Landsat 5 TM SWIR bands for land cover classification and pasture degradation mapping. Land cover classification results improved significantly for the image composite product (overall accuracy (OAA) 89%) compared to the application of RapidEye (OAA 87%) or Landsat 5 TM (OAA 85%) data alone. Pasture degradation was mapped using degradation class thresholds derived from field data and vegetation cover fractions on a per pixel basis and modelled using multiple endmember spectral mixture analysis (MESMA). The pasture degradation map based on the image composite achieved an overall accuracy of 77.5%, compared to 75% (RapidEye) and 61% (Landsat 5 TM). We further tested the relationship between degradation and slope class and concluded that more than 90% of the pastures on slopes > 10 degrees show signs of degradation, whereby on above 20 slopes the portion of moderate to strong degradation is above 57%.
      原文链接:
    • NSTL
    • Elsevier

    Mapping land cover change in northern Brazil with limited training data

    Crowson, MerryHagensieker, RonWaske, Bjoern
    13页
    查看更多>>摘要：Deforestation in the Amazon has important implications for biodiversity and climate change. However, land cover monitoring in this tropical forest is a challenge because it covers such a large area and the land cover change often occurs quickly, and sometimes cyclically. Here we adapt a method which eliminates the need to collect new training data samples for each update of an existing land cover map. We use the state-of-the-art probabilistic classifier Import Vector Machines and Landsat 8 Operational Land Imager (OLI) scenes of the area surrounding Novo Progresso, northern Brazil, to create an initial land cover map for 2013 with associated classification probabilities. We then conduct spectral change detection between 2013 and 2015 using a pair of Landsat images in order to identify the areas where land cover has changed between the two dates, and then reclassify these areas using a supervised classification algorithm, using pixels from the unchanged areas of the map as training data. In this study, we use the pixels with the highest classification probabilities to train the classifier for 2015 and compare the results to those obtained when pixels are chosen randomly. The use of probabilities in the selection of training samples improves the results compared to a random selection, with the highest overall accuracy achieved when 250 training samples with high probabilities are used. For training sample sizes greater than 1000, the differences in overall accuracy between the two approaches to training sample selection are reduced. The final updated 2015 map has an overall accuracy of 80.1%, compared to an overall accuracy of 82.5% for the 2013 map. The results show that this probabilistic method has potential to efficiently map the dynamic land cover change in the Amazon with limited training data, although some challenges remain.
      原文链接:
    • NSTL
    • Elsevier

    New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)

    Zhang, XianlongZhang, FeiQi, YaxiaoDeng, Laifei...
    12页
    查看更多>>摘要：Currently, many remote sensing images of the vegetation index being used have disadvantages, because of high cost, long cycles, and low resolution. Thus, it is difficult to extract and analyse vegetation information in the field. A vegetation index based on visible light images from an unmanned aerial vehicle (UAV) has the advantages of fast image acquisition and high ground resolution, which is superior to traditional remote sensing. However, the vegetation coverage in arid and semi-arid areas is low, and the soil background has a great impact on the common visible vegetation index. The real-time extraction and analysis of the index vegetation information can easily result in big errors. Therefore, according to the construction principle of the green-red vegetation index (GRVI) and modified green-red vegetation index (MGRVI), a new green-red vegetation index (NGRVI) is proposed in this study. First, the newly constructed index and several published indices are used to extract visible light images and generate greyscale images for each of the visible light vegetation indices. Then, the threshold of vegetation and non-vegetation pixel classification is established according to the method of iterative threshold, and the optimal threshold is used to extract the vegetation information from the greyscale images of each of the visible light vegetation indices. Finally, the accuracy difference in vegetation information extraction between the newly constructed and several published indices is compared. The results show that the precision of vegetation information extraction by NGRVI is higher than that of other visible light band vegetation indices; the kappa coefficient is 0.82, and the classification accuracy reaches near-complete consistency. To verify the accuracy of the NGRVI, one image from the same period was selected, and the vegetation information was extracted using the same method. The NGRVI based on UAV visible light images can accurately extract the vegetation information in arid and semi-arid areas, and the extraction accuracy can reach more than 90%. To summarize, NGRVI can accurately and effectively reflect the vegetation information in arid and semi-arid areas and become an important technical means for retrieving biological and physical parameters using visible light images.
      原文链接:
    • NSTL
    • Elsevier

    Laboratory calibration and field measurement of land surface temperature and emissivity using thermal infrared multiband radiometers

    Coll, CesarNiclos, RaquelPuchades, JesusGarcia-Santos, Vicente...
    13页
    查看更多>>摘要：Accurate ground measurements of land surface temperature (LST) are necessary for validating satellite LST products. In order to provide reliable data, ground radiometers must be calibrated with reference to an international standard, and radiometric temperatures must be corrected for land surface emissivity. As opposed to water, land surface emissivity is not usually known for many ground covers, so an emissivity value has to be assumed, assigned from spectral emissivity libraries or measured for each land cover and spectral band considered. The aim of this study is to show the laboratory calibration and the methodology for simultaneous field measurements of LST and emissivity employed in the comparison experiment held in the project Fiducial Reference Measurements for validation of Surface Temperature from Satellites (FRM4STS) funded by the European Space Agency. We used multiband CE-312 radiometers (five narrow bands plus one broad band in the 8-13 mu m window) to simultaneously retrieve LST and band emissivities by means of the temperature-emissivity separation (TES) method for different ground covers (clover, tarmac, soil, gravel and sand). The TES method requires near-simultaneous measurements of ground-leaving radiances and sky downwelling radiances; the latter being measured in the field using a gold reflectance panel. For each surface cover, TES provided band emissivities in the CE-312 bands and LST from continuous radiance measurements performed over time. As a result of the experiment, we present the laboratory calibration of the CE-312 radiometers carried out against a traceable, reference blackbody, and the LST series and band emissivity values for the ground covers considered, together with a detailed LST uncertainty analysis including the uncertainties associated to the calibration of ground radiometers, the emissivity estimation by means of the TES method, and the sky radiance measurements, among others. According to these results, the total LST uncertainty was estimated as 0.4-0.6 K for the ground covers measured during the experiment.
      原文链接:
    • NSTL
    • Elsevier