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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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    Utility of hyperspectral compared to multispectral remote sensing data in estimating forest biomass and structure variables in Finnish boreal forest

    9页
    查看更多>>摘要：Three-quarters of Finland's land surface area is filled with forests, which compose a great part of the country's biomass, carbon pools and carbon sinks. In order to acquire up-to-date information on the forests, optical remote sensing techniques are commonly used. Moreover, in the future hyperspectral satellite missions will start providing data to support the needs of natural resource management practices,.such as forestry.It is, however, unclear what would be the additional value from using hyperspectral data compared to multispectral in quantifying forest variables of Finnish boreal forest. In this study, we used the remote sensing data by hyperspectral AISA imager (128 bands, 400-1000 nm, resolution 0.7 m) and Sentinel-2 (10 bands, resolution 10 m) to assess the possible benefits of higher spectral resolution. As reference data, we used a new nationwide forest resource dataset (stand-level data), which has a high potential in further remote sensing applications. In addition, we used a set of independent in situ measurements (plot-level data) for validation. We applied two kernel-based machine learning regression algorithms (Gaussian process and support vector regression) to relate boreal forest variables with the remote sensing data. The variables of interest were mean height, basal area, leaf area index (LAI), stem biomass and main tree species. The regression algorithms were trained with stand-level data and estimations were evaluated with stand- and plot-level holdout sets. The estimation accuracies were examined with absolute and relative root-mean-square errors. Successful variable estimations showed that kernel-based regression algorithms are suitable tools for forest structure estimation. Based on the results, the additional value of hyperspectral remote sensing data in forest variable estimation in Finnish boreal forest is mainly related to variables with species-specific information, such as main tree species and LAI. The more interesting variables for forestry industry, such as mean height, basal area and stem biomass, can also be estimated accurately with more traditional multispectral remote sensing data.
      原文链接:
    • NSTL
    • Elsevier

    Mapping soil salinity using spectral mixture analysis of landsat 8 OLI images to identify factors influencing salinization in an arid region

    16页
    查看更多>>摘要：Soil salinization is one of the most serious environmental issues degrading land resources globally, particularly in arid and semi-arid regions. Therefore, regional and precise monitoring of soil salinity is required to prevent and mitigate salinization. This study aimed to specify an effective monitoring method with remote sensing techniques using the Dakhla Oasis, central Western Desert of Egypt as a case study area. For ground-truthing, electrical conductivity, pH, reflectance spectra, and mineral compositions were measured for top soil samples from 31 points. Spectral data from a Landsat8 OLI image of one scene acquired close to the ground sampling time was used to estimate soil salinity using a variety of methods, including single band, band ratio and combination, spectral index, linear spectral unmixing (LSU), and mixture tuned matched filtering (MTMF). After estimating the salinity over the study area through the best regression model between the spectral data and measured salinity data, the image was classified into five salinity classes. The classified salinized zones were verified by the resistivity and thickness of the near-surface layers and depth to the groundwater table, using vertical electrical sounding (VES) at 46 profiles. The most salinized zones in the southern area were congruent with the lowest VES resistivity. The surface layer thickness and day content were specified as the main cause of the salinity difference between the southern and northern areas. The land surface temperature (LST) retrieved from the thermal band data of the OLI image and another Landsat ETM + image in 2001 was identified as increasing salinization. Finally, urban and vegetation land covers along with the five soil salinity classes were characterized by the influencing factors of elevation, slope, LST, soil pH, top layer resistivity and thickness, and depth to the groundwater table. LSU proved to predict salinity more accurately with 76% correctness than the MTMF model (67%) and the band combination and spectral indices (55% at most). The proposed methods will be useful for soil salinity mapping from satellite imagery in similar environments to this study.
      原文链接:
    • NSTL
    • Elsevier

    Rapid assessment of juniper distribution in prairie landscapes of the northern Great Plains

    9页
    查看更多>>摘要：Woody plant species including eastern redcedar (Juniperus virginiana) and Rocky Mountain juniper (Juniperus scopulorum) are expanding throughout the prairie ecosystems of the Great Plains because of fire suppression, land management practices, and climate change. Juniper encroachment threatens native grasslands by altering soil characteristics, limiting herbaceous biomass, hindering native plant regeneration, and reducing rangeland productivity. Existing land cover products do not effectively characterize the distribution of juniper over a range of densities, making it difficult to assess the scale of the problem. We evaluated a method for rapid classification and mapping of juniper using matched filtering with Landsat 8 snow-covered and snow-free winter imagery (January-March), and snow-free spring imagery (April-June) for 2015-2016. We used data from two path/rows (29/30 and 30/30) in southeastern South Dakota and northeastern Nebraska (approximately 23,000 km(2)). In both path/rows, we found that snow-covered winter images increased contrast between juniper and the image background and resulted in the highest overall classification accuracies of 94.5% and 88.9% for juniper densities above 15%, compared to 91.4% and 85.7% for snow-free winter imagery and 57.8% and 74.1% for growing season imagery. Using winter imagery, we successfully captured pixels containing juniper density above 50% with >= 90% detection probability. However, the true positive rate dropped to less than 50% once juniper density fell below 20%. We identified 84 791 ha within the study area occupied by juniper (3.6% of the total area), including 27 504 ha in deciduous forests (33% of deciduous forest area) and 38 738 ha in grasslands (6% of grassland area).
      原文链接:
    • NSTL
    • Elsevier

    Mapping woody vegetation cover across Australia's arid rangelands: Utilising a machine-learning classification and low-cost Remotely Piloted Aircraft System

    13页
    查看更多>>摘要：Knowledge of the extent and degree of wooded vegetation cover in the arid parts of Australia is essential for land-holders and management agencies. The balance between wooded and ground-cover vegetation is important to livestock production and landscape health. Adequate mapping of changes in wooded vegetation cover allows the assessment of its expansion and contraction as input for improved management of production and conservation. The aim of this study was to develop a method to accurately map the extent and degree of wooded tree and shrub cover across an area of arid rangeland in central Australia. Its open and sparse distribution throughout the landscape and its adaptation to an arid environment present challenges to obtaining both representative field measurements and scale appropriate remotely sensed imagery. Recent advancements and access to high spatial resolution satellite imagery provide opportunities for improved mapping. The rapid development of Remotely Piloted Aircraft Systems (RPAS) or drones also provides further opportunities to improve the accuracy of field measurements used in the classification of wooded vegetation. An optimised machine-learning classification was developed using high resolution Planet Dove cube-sat and Sentinel2 imagery and compared to medium resolution Landsat8 imagery. An efficient method of collecting plot scale (ha) wooded vegetation cover estimates for the training and assessment of the satellite image classification was also developed using the RPAS. It was comparable to other field based measurements. The results of the classifications showed a moderate degree of accuracy in distinguishing wooded cover from non-wooded cover, highest with the Planet Dove imagery. An improved accuracy in distinguishing between wooded cover classes was also seen in the Sentinel2 classification. The mapping and subsequent monitoring of wooded vegetation in these landscapes has been shown to be improved with higher resolution satellite imagery.
      原文链接:
    • NSTL
    • Elsevier

    A methodology based on GEOBIA and WorldView-3 imagery to derive vegetation indices at tree crown detail in olive orchards

    20页
    查看更多>>摘要：Precision agriculture (PA) is becoming an essential practice for the Implementation of sustainable agriculture that encompasses the efficient use of resources and a systematic crops monitoring. The increasing temporal and spatial resolution of satellite imagery, coupled with their availability and decreasing costs, create new possibilities for generating accurate datasets on different crops variables, more frequently available as ready-to-use data. The availability of very high-resolution (VHR) satellite imagery, such as the WorldView-3 (WV-3), leads to the advanced potential of satellite Remote Sensing (RS), becoming in the last decade one of the main data source in precision agriculture (PA). In the broad overview of these procedures, geographic object-based image classification (GEOBIA) techniques, gained broad interest as methods to produce geographic information in GIS-ready format. In this paper, methodologies for a semiautomatic process workflow is presented, providing olive tree crown detection in two different olive orchards in Calabria (Italy), collected by means of GEOBIA procedures, in order to investigate olive tree spectral behavior and the reliability of WW-3 derived vegetation indices (VIs). The semi-automated classification method, accomplished by imagery pre-processing steps, may constitute an operational processing chain for mapping and monitoring olive orchards at tree scale detail. Five VIs were investigated: Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index 2 (MSAVI 2), Normalized Difference Red Edge Vegetation Index (NDRE), Modified Chlorophyll Absorption Ratio Index Improved (MCARI2), and NDVI2. The obtained results were statistically tested and their accuracy assessed. Thematic accuracy ranges from 95.33% to 96% in both study areas with an overall tree detection rate of 96.8%. Statistical analysis showed that the major differences in spectral behavior, over different plots of the investigated olive orchards, are mainly due to the component of the red-infrared regions of the electromagnetic spectrum (EM), where the red-edge becomes important in assessing the state of general vigor. Moreover, the proposed methodology increases the possibility of detecting tree stress at earlier stages and the benefits of using satellite-based approaches in terms of: larger area coverage, less processing and operator interaction coupled with more spectral information, thus reducing the need to collect costly reference data sampling.
      原文链接:
    • NSTL
    • Elsevier

    Estimation of flow in various sizes of streams using the Sentinel-1 Synthetic Aperture Radar (SAR) data in Han River Basin, Korea

    13页
    查看更多>>摘要：This study proposes a novel approach of estimating stream discharge using the European Space Agency Sentinel. 1 satellite data. Fifteen hydrological stations with a mean discharge ranging between 2 m(3) s(-1) and 305 m(3) s(-1) in the Han River Basin in Korea were chosen as test sites. A series of Sentinel-1 Synthetic Aperture Radar (SAR) images and observed streamflow data were selected to develop and validate the methodology. The methodology relates the stream discharge to the water area that is extracted from the satellite image analysis. The satellite image analysis involved the following sequential steps: (1) A series of SAR images for a given test site is preprocessed for thermal noise, radiometric calibration, speckle filtering and terrain correction; (2) The histogram matching technique is applied to all images to unify the backscatter distribution; (3) The polygonal area including the stream is manually delineated along the reach and the image filter with the shape of the polygon is applied to all images so that only the water area of the stream is extracted; (4) A single backscatter threshold value is applied to all images to extract a series of water area; (5) the power-law relationship between the series of the water area and the corresponding stream discharge is established and the correlation coefficient of the relationship is calculated; (6) The process of (4) and (5) was repeated to identify the optimal backscatter threshold that maximizes the correlation coefficient of the relationship. A clear relationship was developed for the 13 stations except for the two at which flow is highly influenced by hydraulic structures such as dam. At the 13 stream locations, the R value of the power-law relationship varied between 0.68 and 0.98 with a mean value of 0.89. The relationship was influenced by the geometric properties of the stream such as the size and side slope.
      原文链接:
    • NSTL
    • Elsevier

    Monitoring 3D areal displacements by a new methodology and software using UAV photogrammetry

    12页
    查看更多>>摘要：Nowadays, a lot of different geodetic methods based on terrestrial and remote sensing (Global Navigation Satellite System-GNSS, Total Station, Lidar, ground-based radar, Synthetic-aperture radar-SAR, etc.) are used in three-dimensional (3D) monitoring of mass movements. In recent years, Unmanned Aerial Vehicle (UAV) photogrammetry has been used in monitoring large mass movements, especially landslides. In this study, the traceability of 3D areal displacements in a dump site, which is jointly used by three different open marble pits, with a new methodology based on a UAV which has advantages compared to other methods was investigated, and the results of the application were revealed by developing software specific to the methodology. In this context, a deformation network consisting of 46 specially designed plates was established as to include the area and dump benches by paying attention not to being in the locations where new dumps were made since surface topography and morphology changed continuously due to new dumps, and 3-periodic UAV flights were performed. As a result of these flights, periodic orthomosaics and digital elevation models (DEM) were produced. The special plates placed in the field were automatically detected with the software developed and 3-D coordinates of each plate were obtained. From these coordinates obtained, the velocity values of the points were' calculated using the Kalman filtering technique. The velocity values obtained by the GNSS method at the same points were used to verify the results of UAV photogrammetry and to reveal its performance. Whether the velocity values obtained by the GNSS and UAV methods could be considered as equal was determined by statistical analyses (t-test, f-test, RMSE, and VAF). As a result of these analyses, it was found out that significant velocity values greater than 1.5 x GSD (Ground Sample Distance) obtained from GNSS could be determined successfully with UAV. Furthermore, interpolation maps were generated from GNSS and UAV velocity values to compare areal displacements, and it was observed that north (n), east (e), height (up) components of maps were compatible with the correlation values of 0.92, 0.75, 0.87, respectively.
      原文链接:
    • NSTL
    • Elsevier

    Estimating forest aboveground biomass using small-footprint full-waveform airborne LiDAR data

    11页
    查看更多>>摘要：Forest biomass is a key biophysical parameter for climate change, ecological modeling and forest management. Compared with discrete-return LiDAR data, full-waveform LiDAR data can provide more accurate and abundant vertical structure information on vegetation and thus have been increasingly applied to the estimation of forest aboveground biomass (AGB). The main objective of this research is to estimate forest AGB using full waveform airborne LiDAR data. In this study, we constructed voxel-based waveforms (0.5 x 0.5 m) using small footprint full waveform LiDAR data, and then aggregated voxel based waveforms into pseudo large footprint waveforms with a plot size of 20 x 20 m. We extracted a range of waveform metrics from voxel-based waveforms and pseudo-large-footprint waveforms (FWm), respectively, and then calculated the mean of the voxel-based waveform metrics within a plot (FW mu). Based on the Random Forest (RF) regression, the forest biomasses were estimated using two types of waveform metrics: FWm (R-2 = 0.84, RMSE% = 21.4%, bias = -0.11 Mg ha(-1)) and FW mu (R-2 = 0.81, RMSE% = 23.3%, bias = 0.13 Mg ha(-1)). We found that slightly higher biomass estimation accuracy was obtained with FW(m )than with FW mu. In addition, a comparison between the biomasses predicted by the waveform metrics and by the traditional discrete-return metrics (R-2 = 0.80, RMSE% = 23.4%, bias = 0.20 Mg ha(-1)) was performed to explore the potential to improve biomass estimates using the waveform metrics, and the results showed that both waveform metrics and discrete-return metrics could accurately predict forest biomass. However, the biomass estimations from the waveform metrics were more accurate than those from the traditional discrete-return metrics. We concluded that the method proposed in this study has the potential to estimate vegetation structure parameters using full-waveform LiDAR data.
      原文链接:
    • NSTL
    • Elsevier

    The role of GRACE total water storage anomalies, streamflow and rainfall in stream salinity trends across Australia's Murray-Darling Basin during and post the Millennium Drought

    11页
    查看更多>>摘要：By influencing water tables of saline aquifers, multiyear dry or wet periods can significantly delay or accelerate dryland salinity, but this effect remains poorly quantified at the large river basin scale. The Gravity and Climate Recovery Experiment (GRACE) satellite measures changes in the total water storage of river systems, providing a unique opportunity for better understanding connections between stream salinity and changes in catchment water storages at the large river basin scale. Here, we quantified the role of GRACE total water storage anomalies (TWSA) in stream salinity variability across Australia's Murray-Darling Basin ( similar to 1 million km(2)), while also accounting for streamflow and rainfall. We used the MERRA-2 global land surface model to i) place our findings in the context of the longer-term hydroclimatology (1980-present) and ii) to decompose TWSA into groundwater storage as an alternative driver variable. Multivariate time series regression models (generalized additive mixed models or GAMM) showed that the driver variables could explain 20-50% of the variability in stream salinity across 8 sub-catchments in the Murray Darling Basin. TWSA commonly explained as much variability as stream flow, while groundwater storage and TWSA had very similar explanatory power and rainfall only negligible contributions. The 2000-2009 Millennium Drought and the subsequent La Nina Floods had a predominantly decelerating and accelerating effect on stream salinity respectively and these trends were partially explained by trends in TWSA. Our study illustrates that GRACE can be a useful addition for monitoring and modeling dryland salinity over large river basins.
      原文链接:
    • NSTL
    • Elsevier

    Mapping tropical dry forest age using airborne waveform LiDAR and hyperspectral metrics

    13页
    查看更多>>摘要：Tropical dry forest (TDF) regeneration has been extensively characterized as three deterministic successional stages, i.e., early, intermediate, and late, for the past few decades. This deterministic definition, however, ignores many biophysical and biochemical processes in the forest regeneration. This study mapped a second TDF as a function of regeneration age at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site, Costa Rica, using an airborne full-waveform LiDAR (Laser Vegetation Imaging Sensor or LVIS), a hyperspectral dataset (Hyperspectral MAPper or HyMap) and three advanced machine learning methods. We defined five age groups (0-10 years, 10-20 years, 20-30 years, 30-50 years, 50 + years) based on historical forest cover maps, and analyzed their effective LiDAR waveforms and cumulative return energy curves (derived from LVIS) and their reflectance (derived from the HyMap). Then, nine LVIS metrics and eleven HyMap indices were calculated and their abilities to differentiate the age groups were evaluated using Multiple Comparison Analysis (MCA). We found that six of the LVIS metrics which describe the vertical structure of the forests can significantly differentiate all age groups. None of HyMap metrics can differentiate all age groups, but some of them can identify certain age groups. Selected LVIS metrics and HyMap indices were used to map TDF age, through Support Vector Machine, Artificial Neural Network and Random Forest (RF). We found LVIS plus HyMap metrics generally produced more accurate forest age maps than either LVIS metrics or HyMap indices and RF better performed than other two classifiers. We finally proposed a method to synthesize different forest age maps into one age map which had the highest accuracy for all age groups. Our study highlighted the importance to consider the forest regeneration as a continuous stochastic phenomenon, and also highlighted the advantages to incorporate multiple remote sensing techniques to describe the forest regeneration. Our method to synthesize the forest age map can also benefit other researchers who need to take advantage of multiple mapping results.
      原文链接:
    • NSTL
    • Elsevier