首页期刊导航|International journal of applied earth observation and geoinformation
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International journal of applied earth observation and geoinformation
International Institute for Aerospace Survey and Earth Sciences
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
da Silva, Richarde MarquesSilva, Alexandro MedeirosGuimaraes Santos, Celso Augusto
11页
查看更多>>摘要：Evapotranspiration (ET) is a fundamental phenomenon within terrestrial system processes that involves water on land and in the atmosphere. ET is one of the main components of energy production on Earth, and it controls the water balance. The determination of ET is very complex and requires an experienced modeler and a substantial number of working hours. Thus, we present a novel algorithm called the automated surface energy balance algorithm for land (ASEBAL) to automate the application of all stages of the surface energy balance algorithm for land (SEBAL) to estimate ET from a long time series of MODIS orbital images. In this study, ASEBAL was applied to automate the endmember pixel selection and estimation of ET for 282 images of the Ipanema River Basin, which is located in a semiarid area of Brazil. The mean execution time was 3 min for each image, with the mean daily ET ranging from 1.63 mm day(-1) to 6.22 nm day(-1), presenting a mean ET of 3.86 mm day(-1) and a standard deviation of 0.97 mm day(-1). The comparison between the manual and automated selections of the endmember pixels performed in 37 images showed an average difference of 7% in the values of the selected pixels. A mean difference of 0.28 mm between the manual and automatic ET estimates was observed. In addition, a pixel-by-pixel comparison yielded an average R-2 of 0.82. The proposed automated pixel selection procedure remarkably reduced the execution time and eliminated the possibility of human error. Thus, ASEBAL was shown to be an efficient tool for applications with many images and was also shown to be a less complex task, with lower error incidences and much shorter execution time than the traditional method used to apply the algorithm.
查看更多>>摘要：Landsat 7 Scan Line Corrector failure compromised 22% of pixels. Novel procedures were developed estimating missing data from means of pixels present within 2(N)-sized neighbouring blocks whose positions were defined by simultaneously minimizing heterogeneity of source imagery within blocks while satisfying need for sufficiently large size that some pixels remained near defects. Regions used to estimate missing data were defined in a stepwise process in which local standard error cut-offs for accepting particular 2(N)-sized regions of source imagery as representing missing pixels were progressively raised over a series of 51 cycles. For Landsat scenes of interest in western Oregon, averages of 34.7, 68.8, 95.4, and 99.0% of gaps in synthetic data were repaired by cycles 1, 6, 16, and 30. Standard deviations of differences between estimated and real values increased from an average of 9.0 digital numbers at cycle 1 to plateaus near 17.0 by cycle 25. This procedure performed well over a wide range of imagery dates, a consequence of its use of source imagery to identify homologous regions within landscapes rather than directly estimating pixels.
Mandal, DipankarKumar, VineetBhattacharya, AvikRao, Y. S....
11页
查看更多>>摘要：Retrieval of the Plant Area Index (PAI) and wet biomass from polarimetric SAR (PolSAR) data is of paramount importance for in-season monitoring of crop growth. Notably, the joint estimation of biophysical parameters might be effective instead of an individual parameter due to their inherent relationships (possibly nonlinear). The semi-empirical water cloud model (WCM) can be suitably utilized to estimate biophysical parameters from PolSAR data. Nevertheless, instability problems could occur during the model inversion process using traditional inversion approaches. Iterative optimization (10) can have difficulty in finding the global minima while look up table (LUT) searches have a lower generalization capability. These challenges reduce the transferability of IO and LUT search inversions in computational efficiency and seldom account for the inter-correlation among the parameters. Alternatively, a machine learning regression technique with a regularization routine may provide a stable and optimum solution for ill-posed problems related to the inversion of the WCM. In the present work, the crop biophysical parameters viz. PAI and wet biomass are estimated simultaneously using the multi-target Random Forest Regression (MTRFR) technique. The accuracy of the retrieval method is analyzed using the in situ measurements and quad-pol RADARSAT-2 data acquired during the SMAPVEX16 campaign over Manitoba, Canada. The inversion process is tested with different polarization combinations of SAR data for wheat and soybean. The validation used ground measured biophysical parameters for various crops, indicating promising results with a correlation coefficient (r) in the range of 0.6-0.8. In addition, the relationship between PAI and wet biomass using the multi-target and single output model is also assessed based on in-situ measurements. The results confirm that the inter-correlation between biophysical parameters is well preserved in the MTRFR based joint inversion technique for both wheat and soybean.
查看更多>>摘要：Selecting a subset of bands from hyperspectral data can improve the discrimination of ground targets because the most distinguishing spectral features are utilized. Targets with similar spectra are particularly challenging for band selection. A band selection method using the N-dimensional Solid Spectral Angle (NSSA) was recently proposed by Tian et al. (2016) to select the most dissimilar spectral regions amongst targets, but no case studies have been conducted using data from natural targets and there are currently no guidelines for the parameter selection in the NSSA band selection method. This study uses two spectral datasets of geologic relevance (clay minerals and ultramafic rocks), each with spectrally similar materials, to establish guidelines for the selection of two parameters (k and threshold) that will enable the use of the method for practical applications. K defines the band interval (relates to feature width) from which NSSA is calculated, and the threshold defines the number of bands selected from a profile of NSSA as a function of wavelength. The first guideline consists in constraining the maximum k value based on the spectral dimensionality of the widest significant spectral feature for the materials under study. The second guideline is to use a profile of the NSSA value as a function of wavelength for each permissible k value to capture the primary wavelength regions of high NSSA values. Finally, the threshold parameter for each k is estimated from a graph of the NSSA value as a function of the number of bands. The guidelines on the parameter definition allow non-expert users to select a subset of bands while capturing both narrow and broad discriminating features.
查看更多>>摘要：Drought is a popular scientific issue in global climate change research. Accurate monitoring of drought has important implications for the sustainable development of regional agriculture in the context of increasingly complex global climate change. Deep learning is a widely used technique in the field of artificial intelligence. However, ongoing on drought monitoring using deep learning is relatively scarce. In this paper, the various hazard factors in drought development were comprehensively considered based on satellite data including Moderate Resolution Imaging Spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM) as multi-source remote sensing data. By using the deep learning technique, a comprehensive drought monitoring model was constructed and tested in Henan Province of China as an example. The results showed that the comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. There was a significant positive correlation between the drought indicators of the model output and the comprehensive meteorological drought index (CI) measured at the site scale. The consistency rate of the drought grade of the two models was 85.6% and 79.8% for the training set and the test set, respectively. The correlation coefficient between the drought index of the model and the standard precipitation evapotranspiration index (SPED was between 0.772 and 0.910 (P < 0.01), which indicated a strong level of significance. The correlation coefficient between the drought index of the model and the soil relative moisture at a 10 cm depth was greater than 0.550 (P < 0.01), and there was a good correlation between them. This study provides a new method for the comprehensive assessment of regional drought.
查看更多>>摘要：Leaf chlorophyll plays an essential role in controlling photosynthesis, physiological activities and forest health. In this study, the performance of Sentinel-2 and RapidEye satellite data and the Invertible Forest Reflectance Model (INFORM) radiative transfer model (RTM) for retrieving and mapping of leaf chlorophyll content in the Norway spruce (Picea abies) stands of a temperate forest was evaluated. Biochemical properties of leaf samples as well as stand structural characteristics were collected in two subsequent field campaigns during July 2015 and 2016 in the Bavarian Forest National Park (BFNP), Germany, parallel with the timing of the RapidEye and Sentinel-2 images. Leaf chlorophyll was measured both destructively and nondestructively using wet chemical spectrophotometry analysis and a hand-held chlorophyll content meter. The INFORM was utilised in the forward mode to generate two lookup tables (LUTs) in the spectral band settings of RapidEye and Sentinel-2 data using information obtained from the field campaigns. Before generating the LUTs, the sensitivity of the model input parameters to the spectral data from RapidEye and Sentinel-2 were examined. The canopy reflectance of the studied plots were obtained from the satellite images and used as input for the inversion of LUTs. The coefficient of determination (R-2), root mean square errors (RMSE), and the normalised root mean square errors (NRMSE), between the retrieved and measured leaf chlorophyll, were then used to examine the attained results from RapidEye and Sentinel-2 data, respectively. The use of multiple solutions and spectral subsets for the inversion process were further investigated to enhance the retrieval accuracy of foliar chlorophyll. The result of the sensitivity analysis demonstrated that the simulated canopy reflectance of Sentinel-2 is sensitive to the alternation of all INFORM input parameters, while the simulated canopy reflectance from RapidEye did not show sensitivity to leaf water content variations. In general, there was agreement between the simulated and measured reflectance spectra from RapidEye and Sentinel-2, particularly in the visible and red-edge regions. However, examining the average absolute error from the simulated and measured reflectance revealed a large discrepancy in spectral bands around the near-infrared shoulder. The relationship between retrieved and measured leaf chlorophyll content from the Sentinel-2 data had a higher coefficient of determination with a higher NRMSE (NRMSE = 0.36 mu g/cm(2), R-2 = 0.45) compared to those obtained using the RapidEye data (NRMSE = 0.31 mu g/cm(2) and R-2 = 0.39). Using the mean of the ten best solutions (retrieved chlorophyll) the retrieval error for both Sentinel-2 and RapidEye data decreased (NRMSE = 0.34, NRMSE = 0.26, respectively), as compared to only selecting the single best solution. When the Sentinel-2 red edge bands were used as the spectral subset, the retrieval error of leaf chlorophyll decreased indicating the importance of red edge, as well as properly located spectral bands, for leaf chlorophyll estimation. The chlorophyll maps produced by the inversion of the two LUTs effectively represented the variation of foliar chlorophyll in BFNP and confirmed our earlier findings on the observed stress pattern caused by insect infestation.
查看更多>>摘要：The Advanced Himawari Imager (Al-II) onboard the recently launched next generation geostationary satellite, Himawari-8, provides an opportunity to improve Land Surface Phenology (LSP) detections over the Asia-Pacific region. In this paper, we detected four phenological transition dates (PTDs) using the three-day Two-band Enhanced Vegetation Index (EVI2) time series from AHI based on the Hybrid Piecewise Logistic Model-Land Surface Phenology Detection (HPLM-LSPD) algorithm. The four PTDs are Start of Spring (SOS), End of Spring (EOS), Start of Fall (SOF) and End of Fall (SOF). We evaluated the four AHI-derived PTDs against those detected using eight-day EVI2 time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the polar-orbiting satellite Terra, and three-day Green Chromatic Coordinate (GCC) time series from the Phenological Eyes Network (PEN) at six sites in central and northern Japan. The evaluation was performed by conducting regression analyses, and calculating root mean square difference (RMSD) and bias between satellite and PEN-derived PTDs. First, the difference in the spatial variations of SOS and EOF timing between naturally vegetated areas, and urban areas and croplands indicates the anthropogenic footprints on LSP. Second, the RMSD of either AHI PTDs or MODIS PTDs against PEN PTDs were higher in the fall (i.e., SOF and EOF) than those in spring (i.e., SOS and EOS). Third, the later EOS and earlier SOF derived from satellite EVI2 relative to those derived from PEN GCC might be caused by the difference in the sensitivity of GCC and EVI2 to the increases in leaf area index (LAI) over high-LAI canopies. Fourth, the higher temporal resolution of AHI EVI2 only helped reduce the RMSD during spring compared to the RMSD for MODIS. In contrast, the RMSD for AHI PTDs and MODIS PTDs were comparable in fall. Finally, the between-sensor correlation in the spatiotemporal variability of the four PTDs was higher for SOS and EOF than those for EOS and SOF.
查看更多>>摘要：Airborne LiDAR-derived canopy height models (CHMs) have been widely applied in forestry inventory applications and have shown great advantages in obtaining forest-related parameters at different scales. Usually, first echoes are regarded as a representation of canopy surfaces during CHM generation, which may cause data pits and a lack of detail, thus resulting in negative effects for forestry applications. Therefore, we propose a canopy surface point filtering method called graph-based progressive morphological filtering (GPMF) for generating CHMs. The GPMF algorithm applies adaptive morphological filtering to exclude nonsurface points from all LiDAR points in a progressive process. The proposed method was tested in natural secondary forest stands. By comparing the performances of the GPMF method and four other CHM generation methods, namely, first-echo interpolation, Gaussian filtering, pit-filling and the highest points interpolation method, the results showed that the GPMF method produced few pits while retaining canopy details. The CHMs generated with the GPMF method were also obviously better than those generated with the other methods, as evidenced by the lowest average root mean square error (RMSE, 0.92 m) between reference points and the raster surfaces. The CHMs generated using GPMF also performed best overall in individual tree detection (average F-score = 80.57%) and tree height estimation (average RMSE = 0.21 m) among all the methods. Therefore, the proposed GPMF method can accurately represent the height of canopy surfaces and shows high potential in forestry applications.
查看更多>>摘要：Land cover dynamics in the tropical dry environments of sub-Saharan Africa are not well understood compared to humid environments, especially on a national scale. Previous studies describing land cover in the dry tropics are spatially or temporally constrained. This study presents the first long-term (1972-2016) nationwide land cover dynamics and forest connectivity analyses for Zambia. We employed 300 Landsat images and an object-based image analysis (OBIA) approach with the Random Forests (RF) classifier to map nine land covers at six time steps (1972, 1984, 1990, 2000, 2008 and 2016). Post-classification change detection was used to understand the dynamics, while landscape metrics were derived to assess the forest structural connectivity. Overall accuracies ranging from 79 to 86% were achieved. In 1972, 48% of Zambia was covered by primary forest and 16% was covered by secondary forest. By 2016, 62.74% of Zambia had undergone changes, with primary forest decreasing by 32% and secondary forest increasing by 23%. Our results showed that forests have been recovering by 0.03 to 1.3% yr(-1) (53, 000-242, 000 ha yr(-1)); however, these rates are markedly lower than deforestation rates (- 0.54 to - 3.05% yr(-1): 83, 000-453, 000 ha yr(-1)). Annual rates of change varied by land cover, with irrigated crops having the largest increase (+ 3.19 yr(-1)) and primary forest having the greatest decrease (-2.48% yr(-1)). Forest connectivity declined by 22%, with primary forest having the greatest decline, while the connectivity for secondary and plantation forest increased. We showed here that land cover in Zambia is highly dynamic with high rates of change, low forest recovery and decline in forest connectivity. These findings will aid in land use planning, reporting for the forthcoming 2020 Global Forest Resources Assessment, and carbon accounting, especially under the reduction in carbon emissions from deforestation and degradation programme (REDD +).
查看更多>>摘要：An agricultural system is a complex combination of many different components that require different types of data for analysis and modeling. Remote sensing information is an alternative source of data for areas that only have a small amount of ground truth data. The goal of this study was to evaluate whether remotely sensed data can be used for calibration of genetic specific parameters (GSPs) with the ultimate goal of yield estimation. This study used the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) with measured Leaf Area Index (LAI) for soybean fields in Parana, Brazil and Iowa, USA, to calibrate the cultivar parameters of the CSM-CROPGRO-Soybean model. Three calibration methods were performed including field-measured LAI, remotely sensed derived LAI, and remotely sensed derived Light Interception. The cultivar parameters sensitive to LAI and LI were calibrated for yield with a mean error of -4.5 kg/ha (0.1%) and with a R-2 of 0.89 for Parana. The availability of crop growth measurements for Iowa resulted in an average RMSE of 895 kg/ha (average nRMSE of 6%), and Willmott agreement index of 0.98 for time-series biomass, and an average RMSE of 941 kg/ha (average nRMSE of 15%) for pod weight. This study showed that remotely sensed LAI and LI from NDVI data can be used for calibration of GSPs with the ultimate goal of improving yield predictions based on local dynamic temporal and spatial variability.
NSTL
Elsevier