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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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    Estimation of forest leaf water content through inversion of a radiative transfer model from LiDAR and hyperspectral data

    Darvishzadeh, RoshanakWang, TiejunSkidmore, Andrew K.Zhu, Xi...
    10页
    查看更多>>摘要：The accurate estimation of leaf water content (LWC) and knowledge about its spatial variation are important for forest and agricultural management since LWC provides key information for evaluating plant physiology. Hyperspectral data have been widely used to estimate LWC. However, the canopy reflectance can be affected by canopy structure, thereby introducing error to the retrieval of LWC from hyperspectral data alone. Radiative transfer models (RTM) provide a robust approach to combine LiDAR and hyperspectral data in order to address the confounding effects caused by the variation of canopy structure. In this study, the INFORM model was adjusted to retrieve LWC from airborne hyperspectral and LiDAR data. Two structural parameters (i.e. stem density and crown diameter) in the input of the INFORM model that affect canopy reflectance most were replaced by canopy cover which could be directly obtained from LiDAR data. The LiDAR-derived canopy cover was used to constrain in the inversion procedure to alleviate the ill -posed problem. The models were validated against field measurements obtained from 26 forest plots and then used to map LWC in the southern part of the Bavarian Forest National Park in Germany. The results show that with the introduction of prior information of canopy cover obtained from LiDAR data, LWC could be retrieved with a good accuracy (R-2 = 0.87, RMSE = 0.0022 g/cm(2), nRMSE = 0.13). The adjustment of the INFORM model facilitated the introduction of prior information over a large extent, as the estimation of canopy cover can be achieved from airborne LiDAR data.
      原文链接:
    • NSTL
    • Elsevier

    Integration of Range Split Spectrum Interferometry and conventional InSAR to monitor large gradient surface displacements

    Luo, HaibinLi, ZhenhongChen, JiajunPearson, Christopher...
    8页
    查看更多>>摘要：Incorrect unwrapping of dense interferometric fringes caused by large gradient displacements make it difficult to measure mining subsidence using conventional Interferometric Synthetic Aperture Radar (InSAR). This paper presents a Range Split Spectrum Interferometry assisted Phase Unwrapping (R-SSIaPU) method for the first time. The R-SSIaPU method takes advantage of (i) the capability of Range Split Spectrum Interferometry of measuring surface displacements with large spatial gradients, and (ii) the capability of conventional InSAR of being sensitive to surface displacements with limited spatial gradients. Both simulated and real experiments show that the R-SSIaPU method can monitor large gradient mining-induced surface movements with high precision. In the case of the Tangjiahui mine, the R-SSIaPU method agreed with GPS with differences of approximately 4.2 cm, whilst conventional InSAR deviated from GPS with differences of nearly 1 m. The R-SSIaPU method makes phase unwrapping less challenge, especially in the cases with large surface displacements. In addition to mining subsidence, it is believed that the R-SSIaPU method can be used to monitor surface displacements caused by landslides, earthquakes, volcanic eruptions, and glacier movements.
      原文链接:
    • NSTL
    • Elsevier

    A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake

    Zhang, FangfangLi, JunshengShen, QianZhang, Bing...
    12页
    查看更多>>摘要：Soft-classification-based methods for estimating chlorophyll-a concentration (Cad.) by satellite remote sensing have shown great potential in turbid coastal and inland waters. However, one of the most important water color sensors, the MEdium Resolution Imaging Spectrometer (MERIS), has not been applied to the study of turbid or eutrophic lakes. In this study, we developed a new soft-classification-based C-chla, estimation method using MERIS data for the highly turbid and eutrophic Taihu Lake. We first developed a decision tree to classify Taihu Lake into three optical water types (OWTs) using MERIS reflectance data, which were quasi-synchronous (+/- 3 h) with in situ measured C-chla, data from 91 sample stations. Secondly, we used MERIS reflectance and in situ measured Ca. data in each OWT to calibrate the optimal C-chla, estimation model for each OWT. We then developed a soft classification -based C-chla estimation method, which blends the C-chla , estimation results in each OWT by a weighted average, where the weight for each MERIS spectra in each OWT is the reciprocal value of the spectral angle distance between the MERIS spectra and the centroid spectra of the OWT. Finally, the soft-classification based Cad. estimation algorithm was validated and compared with no-classification and hard-classification based methods by the leave-one-out cross-validation (LOOCV) method. The soft-classification-based method exhibited the best performance, with a correlation coefficient (R-2), average relative error (ARE), and root-mean square error (RMSE) of 0.81, 33.8%, and 7.0 mu g/L, respectively. Furthermore, the soft- classification-based method displayed smooth values at the edges of OWT boundaries, which resolved the main problem with the hard-classification-based method. The seasonal and annual variations of Cath, were computed in Taihu Lake from 2003 to 2011, and agreed with the results of previous studies, further indicating the stability of the algorithm. We therefore propose that the soft-classification-based method can be effectively used in Taihu Lake, and that it has the potential for use in other optically-similar turbid and eutrophic lakes, and using spectrally-similar satellite sensors.
      原文链接:
    • NSTL
    • Elsevier

    Performance of GNSS-R GLORI data for biomass estimation over the Landes forest

    Zribi, MehrezGuyon, DominiqueMotte, ErwanDayau, Sylvia...
    9页
    查看更多>>摘要：The Above-Ground Biomass (AGB) is a key parameter used for the modeling of the carbon cycle. The aim of this study is to make an experimental assessment of the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to forest AGB. This is based on the analysis of the data recorded during several GLORI airborne campaigns in June and July 2015, over the Landes Forest (France). Ground truth measurements of tree height, density and diameter at breast height (DBH), as well as AGB, were carried out for 100 maritime pine forest plots of various ages. The GNSS-R data were used to obtain the right-left (Gamma(RL) and right-right (Gamma(RR)) reflectivity observables, which are geo-referenced in accordance with the known positions of relevant GPS satellites and the airborne receiver. The correlations between forest AGB and the GNSS-R observables yield the highest sensitivity at high elevation angles (70 degrees-90 degrees). In this case, for (Gamma(RL).) and the reflectivity polarization ratio (PR = Gamma(RL)/Gamma(RR)) estimated with a coherent integration time Tc = 20 ms, the coefficients of determination R-2 are equal to 0.67 and 0.51, with a sensitivity of-0.051 dB/[10(6)g (Mg) ha(-1)], and - 0.053 dB/[Mg ha(-1)], respectively. The relationships between AGB and the observables are confirmed through the use of a 5-fold cross validation approach, with several different coherent integration times.
      原文链接:
    • NSTL
    • Elsevier

    Spectral mapping methods applied to LiDAR data: Application to fuel type mapping

    Huesca, MargaritaRiano, DavidUstin, Susan L.
    10页
    查看更多>>摘要：Originally developed to classify multispectral and hyperspectral images, spectral mapping methods were used to classify Light Detection and Ranging (LiDAR) data to estimate the vertical structure of vegetation for Fuel Type (FT) mapping. Three spectral mapping methods generated spatially comprehensive FT maps for Caballeros National Park (Spain): (1) Spectral Mixture Analysis (SMA), (2) Spectral Angle Mapper (SAM), and (3) Multiple Endmember Spectral Mixture Analysis (MESMA). The Vegetation Vertical Profiles (VVPs) describe the vertical distribution of the vegetation and are used to define each FT endmember in a LiDAR signature library. Two different approaches were used to define the endmembers, one based on the field data collected in 1998 and 1999 (Approach 1) and the other on exploring spatial patterns of the singular FT discriminating factors (Approach 2). The overall accuracy is higher for Approach 2 and with best results when considering a five-FT model rather than a seven-FT model. The agreement with field data of 44% for MESMA and SMA and 40% for SAM is higher than the 38% of the official Caballeros National Park FTs map. The principal spatial patterns for the different FTs were well captured, demonstrating the value of this novel approach using spectral mapping methods applied to LiDAR data. The error sources included the time gap between field data and LiDAR acquisition, the steep topography in parts of the study site, and the low LiDAR point density among others.
      原文链接:
    • NSTL
    • Elsevier

    A twenty year GIS-based assessment of environmental sustainability of land use changes in and around protected areas of a fast developing country: Spain

    Rodriguez-Rodriguez, DavidMartinez-Vega, JavierEchavarria, Pilar
    11页
    查看更多>>摘要：Spain has experienced massive recent socioeconomic changes that have had an influence on biodiversity and landscapes through land use-land cover (LULC) changes. Protected areas (PAs) seek to conserve biodiversity by establishing a legal and, sometimes, managerial regime that forbids or restricts LULC changes that are damaging to biodiversity. Here, we used CORINE Land Cover (CLC) data between 1987 and 2006 to assess differences in LULC changes and processes of change as metrics of effectiveness in four PA networks of clear legal and managerial characteristics in Spain: Nature reserves (NRs), Nature parks (NPs), Sites of Community Importance (SCIs) and Special Protection Areas (SPAs). We also compared LULC changes and processes of change around each PA network applying a modified Before-After-Control-Impact (BACI) research design with two increasingly distant control areas and two models of increased validity. The four PA networks were more environmentally sustainable than their surrounding areas although an effectiveness gradient was shown: NFU > SCIs > SPAs > NPs, suggesting little influence of PA management on LULC changes overall. Another gradient of environmental sustainability of control areas was evident: SCIs > SPAs > NPs > NRs. Proximal controls were more sustainable than distant ones. The main LULC increases inside PAs affected agro-forestry areas and transitional woodland-shrub, whereas artificial surfaces, permanently irrigated lands and burned areas prevailed in the proximal and distant controls. Three main LULC processes of change inside and around Spanish PM outstood: forest succession, land development, and new irrigated areas, the two former chiefly affecting surrounding areas and posing serious threats to effective biodiversity conservation.
      原文链接:
    • NSTL
    • Elsevier

    Hydrocarbon micro-seepage detection from airborne hyper-spectral images by plant stress spectra based on the PROSPECT model

    Huang, ShuangChen, ShengboWang, DamingZhou, Chao...
    11页
    查看更多>>摘要：Hydrocarbon micro-seepage can result in vegetation spectral anomalies. Early detection of spectral anomalies in plants stressed by hydrocarbon micro-seepage could help reveal oil and gas resources. In this study, the origin of plant spectral anomalies affected by hydrocarbon micro-seepage was measured using indoor simulation experiments. We analyzed wheat samples grown in a simulated hydrocarbon micro-seepage environment in a laboratory setting. The leaf mesophyll structure (N) values of plants in oil and gas micro-seepage regions were measured according to the content of measured biochemical parameters and spectra simulated by PROSPECT, a model for extracting hydrocarbon micro-seepage information from hyper-spectral images based on plant stress spectra. Spectral reflectance was simulated with N, chlorophyll content (Ca), water content (C,) and dry matter content (Cm). Multivariate regression equations were established using varying gasoline volume as the dependent variable and spectral feature parameters exhibiting a high rate of change as the independent variables. We derived a regression equation with the highest correlation coefficient and applied it to airborne hyper-spectral data (CASI/SASI) in Qingyang Oilfield, where extracted information regarding hydrocarbon micro-seepage was matched with known oil-producing wells.
      原文链接:
    • NSTL
    • Elsevier

    Frequency ratio modelling using geospatial data to predict Kimberlite Clan of rock emplacement zones in Dharwar Craton, India

    Prasath, H. Lakshmi RamKusuma, K. N.Chaitanya, S.Guru, Balamurugan...
    18页
    查看更多>>摘要：Kimberlite clan of rocks (KCR) comprising of mantle derived ultrabasic rocks such as Kimberlite and related Lamproites and Lamprophyres,are the primary source of diamond. Locating the KCR is first step in the diamond exploration, which is highly challenging in the field due to (i) very small spatial extent of KCR pipes (ii) high susceptibility of KCR to weathering and alteration on exposure to atmosphere, owing to their ultrabasic composition. Predictive statistical models using the geospatial data are often used to minimize the search and the present work attempts to apply the Frequency Ratio (FR) based predictive model in GIS to prepare KCR potential zone maps based on the relationship between the already explored KCR locations and the factors that favour their emplacement. Wajrakarur Kimberlite Field (WKF) in the Dharawar Craton of India, with more than 30 explored kimberlite pipes is selected as the study area. Geospatial technology has been used to generate thematic maps such as known KCR pipe locations, lineament density, lineament buffer zone, lineament intersection buffer zone, drainage anomaly buffer zone, geomorphology, and classified image showing distribution of mineral such as clay, iron oxide and calcrete, which are surface expression of KCR emplacement from various sources. Landsat 8 OLI satellite data, ASTER DEM were used in preparing the geomorphology, lineament map, and band ratio based mineral classified map. The thematic maps were converted to raster grid of 10 sq. m. FR values for each unit in each thematic map were obtained by correlating the spatial relationship between thematic map and the 25 locations of the 33 "known" KCR locations in WKF used for FR modelling. Cumulative FR value were obtained by carrying out overlay analysis of the thematic maps, which are classified into five classes by Natural Breaking method as (i)Very Low Favourable (VLF), (ii) Low Favourable (LF), (iii)Moderate favourable (MF), (iv)High Favourable (HF), and (v)Very High Favourable (VHF). The model was validated by ground verification at random sites and statistical method. During the ground visit, we observed KCR-like lithology's at four new sites that have calcrete exposure at limited spatial extent and also some pieces of ultrabasic rocks similar to the explored sites. To ascertain their chemical composition of the samples were plotted in the MgO-K2O-Al2O3 ternary diagram. All the four samples fall in the Kimberlite/Lamproite field confirming them to be KCR. The FR predictive model was also validated statistically. Total 13 locations, including 8 site out of 33 known KCR locations, one newly discovered pipe by GSI and the four locations discovered during this study were used for the validation. Statistical validation shows that 84% of model accuracy is achieved. The study reveals that Lineament Intersection, and circular drainage anomaly in 3rd order streams, lineament density are significant themes in predicting KCR emplacement zones. The study demonstrates the utility of statistical based model such as FR model in predicting the location of KCR emplacement, even with statistically insignificant distribution of KCRs and can be applied elsewhere in the world to locate the KCRs. In the process, we report discovery of four new KCR pipes in the WKF.
      原文链接:
    • NSTL
    • Elsevier

    Identification of hydrocarbon microseepage induced alterations with spectral target detection and unmixing algorithms

    Soydan, HilalKoz, AlperDuzgun, H. Sebnem
    13页
    查看更多>>摘要：Hydrocarbon micro and macro seeps alter chemical and mineral composition of the Earth's surface, providing prospects for detection with remote sensing tools. There have been several studies focusing on mapping these anomalies by utilizing ever evolving multispectral and hyperspectral imaging instruments, which has proven their capacity for mapping both hydrocarbons and hydrocarbon-induced alterations so far. These studies broadly comprise of methods like calculating band ratios, spectral angle mapping, spectral feature fitting, and principal component analysis as detection techniques. However, there is a lack of concentration on advanced signature based detection algorithms and unmixing methods for mapping surface manifestations of hydrocarbon micro seeps. Signature based detection algorithms utilize target spectra to correlate with each pixel's spectrum in order to allocate possible target locations. Unmixing methods, on the other hand, require no input spectra beforehand, aiming to resolve each pixel's spectral constituents and their corresponding abundance fractions. In this paper, the potential of all these methods in mapping microseepage related anomalies are evaluated by implementing and comparing them for Gemrik Anticline, one of the prospective hydrocarbon exploration fields in Turkey. Hence, it provides a complete knowledge on determination surface manifestations of hydrocarbon microseeps with the help of well known supervised target detection algorithms and hyperspectral unmixing algorithms. The study area is located in the Southeastern Anatolia, between the cities of Adwaman and Sanhurfa. The spectral signatures were collected with Analytical Spectral Devices Inc. (ASD) spectrometer during the field studies conducted by Avcioglu (2010), to be utilized as an input to the signature based detection algorithms as well as a reference to select the related abundance map among the outputs of unmixing methods. Advanced Space Borne Thermal Emission and Radiometer (ASTER) image of the study region, with an atmospheric correction before running the algorithms, is selected for the applications. Among the applied algorithms, Simplex Identification via Split Augmented Lagrangian (SISAL) is selected as a base of comparison, as it possess minimum calculated error metrics in the experiments. Another unmixing method, the Minimum Volume Simplex Algorithm (MVSA), and signature-based techniques, Desired Target Detection and Classification Algorithm (DTDCA) & Spectral Matched Filter (SMF) follow the success of the SISAL, respectively. The Crosta technique, which is performed as a conventional approach for experimental comparisons, has also shown its capability, succeeding these algorithms. The study provides an overall assessment for methodologies to be used for hydrocarbon microseepage mapping, which also serves guidance for further exploration studies in the region. The potential of ASTER data for hydrocarbon-induced alterations is also emphasized as a cost effective tool for the future applications.
      原文链接:
    • NSTL
    • Elsevier

    A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

    Momblanch, A.Jain, S. K.Waine, T. W.Holman, I. P....
    9页
    查看更多>>摘要：Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km(2) river basin in the Indian Himalayan region over a period of similar to 2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.
      原文链接:
    • NSTL
    • Elsevier