首页期刊导航|International journal of applied earth observation and geoinformation
期刊信息/Journal information
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
查看更多>>摘要：The temporal consistency of the fAPAR GEOV2 full time series (constituted by data derived from SPOT-VGT1/2 and PROBA-V) is analyzed against the single-sensor MODIS dataset, with a particular focus on the most recent fAPAR anomalies (z-scores) produced from PROBA-V in the period 2014-2017. The intercomparison highlights a systematic overestimation of GEOV2 fAPAR z-scores when compared to MODIS fAPAR, likely related to the observed positive bias (over 90% of the domain) in the PROBA-V vs. SPOT-VGT1/2 relationship. A simple two-step harmonization procedure has been proposed to remove this discrepancy, based on two separate linear corrections of SPOT-VGT1/2 (2001-2013) and PROBA-V (2014-2017) data with respect to MODIS, followed by a time lag correction. The harmonized GEOV2 time series preserves the overall dynamic of fAPAR, while removing the sensor bias and improving the consistency with MODIS data. The fAPAR anomalies from the harmonized GEOV2 time series provide unbiased estimates of z-scores that are overall well correlated (R = 0.55 +/- 0.25) with the MODIS fAPAR anomalies.
查看更多>>摘要：A considerable portion of the natural inland lakes has been gradually transformed into aquaculture ponds to meet the enormous demand for aquaculture products. The changes in ponds area can be used to measure the impact of human activities on inland lakes. However, aquaculture ponds and inland lakes are often intermingled with each other especially in the areas close to the lake shore, posing great difficulties for the extraction of aquaculture ponds from medium resolution (15-30 m) multispectral imagery, such as Landsat TM, OLI, and Geofen-1 WFV images. This study proposes a contour-based regularity measurement for water segments, which evaluates the zero-curvature portions of the boundaries, to distinguish aquaculture ponds from natural water. Water surfaces are firstly extracted from satellite images, and then boundary trace of each water segment is carried out to evaluate the geometrical feature of its contour, including perimeter, curvature and the proposed contour-based regularity. Eventually, SVM classification based on these geometrical features separates the aquaculture ponds from inland lakes. Experiments on Landsat TM, OLI, and Geofen-1 WFV images showed that the combination of perimeter, area and proposed contour-based regularity outperforms other feature combinations and produced the most accurate classification. Therefore, the proposed method can be used to extract all aquaculture ponds from all historic Landsat images to monitor the changes in inland aquaculture.
查看更多>>摘要：Field spectroscopy is an accurate, rapid and nondestructive technique for monitoring of agricultural plant characteristics. Among these, identification of grapevine varieties is one of the most important factors in viticulture and wine industry. This study evaluated the discriminatory ability of field hyperspectral data and statistical techniques in case of five common grapevine varieties in the western of Iran. A total of 3000 spectral samples were acquired at leaf and canopy levels. Then, in order to identify the best approach, two types of hyperspectral data (wavelengths from 350 to 2500 nm and 32 spectral indices), two data reduction methods (PLSR and ANOVA-PCA) and two classification algorithms (LDA and SVM) were applied in a total of 16 scenarios. Results showed that the grapevine varieties were discriminated with overall accuracy of 89.88%-100% in test sets. Among the data reduction methods, the combination of ANOVA and PCA yielded higher performance as opposed to PLSR. Accordingly, optimal wavelengths in discrimination of studied grapevine varieties were located in vicinity of 695, 752, 1148, 1606 nm and 582, 687, 1154, 1927 rim at leaf and canopy levels, respectively. Optimal spectral indices were R680, WI, SGB and RATIO975_2, DattA, Greenness at leaf and canopy levels, respectively. Also, the importance of spectral regions in discriminating studied grapevine varieties was ranked as near-infrared > mid-infrared and red edge region > visible. As a general conclusion, the canopyspectral indices-ANOVA-PCA-SVM scenario discriminated the studied species most accurately.
查看更多>>摘要：Sandification is a serious global environmental problem. The spatiotemporal dynamics of sandy barren land is an important indicator in monitoring its ecological restoration. Such monitoring requires fast and accurate identification of sandy barren land. Although several bare indexes have been developed for the mapping of barren land, these have not proven effective in the mapping of sandy barren land. In this paper, the partial normalized sandy barren index (PNSBI), a new barren index based on spectrum reflectance measurement and analysis, is proposed. The efficiency and enhanced accuracy of PNSBI at retrieving sandy barren land was validated using Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images of the Poyang Lake and Ordos regions of China. These results suggest that the proposed PNSBI achieved higher accuracy than the other bare indexes in retrieving sandy barren land from the other types of bare land. And it is benefit for the study of the spatiotemporal dynamics of sandification, water distribution, soil erosion, etc.
查看更多>>摘要：The recent launch of the Sentinel-1 A and Sentinel-1B synthetic aperture radar (SAR) satellite constellation has provided high-quality SAR data with fine spatial and temporal sampling characterizations (6(similar to)12 revisit days at 10 m spatial resolution). When combined with high-resolution optical remote sensing, this data can potentially be used for high-resolution soil moisture retrieval over vegetated areas. However, the suitability of different vegetation index (VI) types for the parameterization of vegetation water content in SAR vegetation scattering models requires further investigation. In this study, the widely-used physical-based Advanced Integral Equation Model (AIEM) is coupled with the Water Cloud Model (WCM) for the retrieval of field-scale soil moisture. Three different VIs (NDVI, EVI, and LAI) produced by two different satellite sensors (Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat) are selected to examine their impact on the parameterization of vegetation water content, and subsequently, on soil moisture retrieval accuracy. Results indicate that, despite the different sensitivity of estimated surface roughness parameters to various VIs (i.e., this sensitivity is highest when utilizing MODIS EVI and lowest in the LAI-based model), the optimum roughness parameters derived from each VI exhibit no discernible difference. Consequently, the soil moisture retrieval accuracies show no noticeable sensitivity to the choice of a particular VI. Generally, meadow and grassland sites with small differences in VIderived roughness parameters exhibit good performance in soil moisture estimation. With respect to the relative components in the coupled model, the vegetative contribution to the scattering signal exceeds that of soil at VI about 0.6 similar to 0.8 [-] in NDVI-based models and 0.4 similar to 0.6 [-] in EVI-based models. This study provides insight into the proper selection of vegetation indices during the use of SAR and optical imagery for the retrieval of high-resolution surface soil moisture.
查看更多>>摘要：Coastal habitats provide a plethora of ecosystem services, yet they undergo continuous pressure and degradation due to the human-induced climate change. Conservation and management imply continuous monitoring and mapping of their spatial distribution at first. The present study explores the capabilities of the Copernicus Sentinel-2 mission and the contribution of its coastal aerosol band 1 (443 nm) for the mapping of the dominant Mediterranean coastal marine habitats and the bathymetry in three survey sites in the East Mediterranean. The selected sites have shallow to deep habitats and a high variability of oceanographic and seabed morphological conditions. The major findings of our study demonstrate the advantages of the downscaled Sentinel-2 coastal aerosol band 1 for both optically shallow habitat and satellite-derived bathymetry mapping due to its great water penetration. The use of Sentinel-2 band 1 allows detection of Posidonia oceanica seagrass beds down to 32.2 m of depth. Sentinel-2 constellation with its 10-m spatial resolution at most of the spectral bands, 5-day revisit frequency and open data policy can be an important tool to provide crucial missing information on the spatial distribution of coastal habitats and on their bathymetry distribution, especially in data-poor and/or remote areas with large gaps in a retrospective, rapid and non-intrusive manner. As such, it becomes a crucial ally for the conservation and management of coastal habitats globally.
查看更多>>摘要：This study assessed the impact of climate change on snow resources in the Arctic. Climate was classified for the period 1950-2100 according to the Koppen-Geiger (KG) classification system using the ensemble average of NASA-NEX CMIP5 projections for the rcp 4.5 scenario. Snow Cover Frequency (SCF) in days/year was calculated from the MODIS10A1 snow product and the SCF trends were calculated across the Arctic for the MODIS period (2001-2016). Ten pollution monitoring areas in the Arctic lowlands, especially vulnerable to climate change impacts, were selected for analyzing the climate and snow regimes. In seven of the ten areas we observed significant changes in the climate during the MODIS period and these same areas also showed the largest SCF trends. At lower latitudes we observed decreasing SCF, while further north, by the shores of the Arctic seas, SCF has increased. Averaged across the Arctic we observed a 0.91 days/year decrease in SCF. Our results show that across the Arctic warmer climate classes have and will continue to replace polar tundra and cold summer regions. Based on the CMIP5 simulations, we expect the coverage of the currently dominant Arctic climate class, Cold climate with cold summers and no dry season (Dfc), to decline by about 40% by 2100 and be replaced by climate classes associated with warm (Dfb, Dsb, Dwb) and hot (Dfa, Dsa, Dwa) summers.
查看更多>>摘要：Cropland maps derived from satellite imagery have become a common source of information to estimate food production, support land use policies, and measure the environmental impacts of agriculture. Cropland classification models are typically calibrated with data collected from roadside surveys which enable the sampling of large areas at a relatively low cost. However, there is a risk of providing biased data as environmental and management gradients may not be fully captured from road networks, thereby violating the assumption of representativeness of calibration data. Despite being widely adopted, the potential biases of roadside sampling have so far not been thoroughly addressed. In this study, we looked for evidence of these biases by comparing three sampling strategies: Random sampling, Roadside sampling, and Transect sampling - a spatially constrained variant of Roadside sampling. In these three strategies, non-cropland data are randomly distributed as they can be photo-interpreted. Based on reference maps at 30 m in four study sites, we followed a Monte Carlo approach to generate multiple realizations of each sampling strategy for ten sample sizes. The effect of the sampling strategy was then assessed in terms of representativeness of the data set collected and accuracy of the resulting maps. Results showed that data sets obtained from Roadside sampling were significantly less representative than those obtained from Random sampling but the resulting maps were only marginally less accurate (2% difference). Transect sampling captured systematically less variability than Random or Roadside sampling which led to differences in accuracy as large as 15%. The effect of sample size on accuracy varied across sites but generally leveled off after reaching 3000 pixels. Augmenting the size of Transect samples improved the classification accuracy but not sufficiently to match the performance of the other sampling strategies. Finally, we found that Random and Roadside training sets with similar representativeness yield comparable accuracy. Therefore, we conclude that roadside sampling can be a viable source of training data for cropland mapping if the range of environmental and management gradients is surveyed. This underlines the importance of survey planning to identify those routes that capture most variability.
查看更多>>摘要：Vegetation cover in savannas is characterized by high spatial heterogeneity driven by natural and anthropogenic drivers acting at multiple spatial and temporal scales. This research article identifies trends in vegetation cover and productivity in response to both land management and variable precipitation through a methodological approach that incorporates vegetation transect fieldwork with satellite imagery time series analysis. The phenological cycles of semi-arid savanna vegetation were analyzed over 27 years for both a protected area and a privately-owned property in order to capture multiple vegetation cover categories within the eastern Edwards Plateau ecoregion of central Texas. Line-intercept vegetation transects were established across these two sites in 2015, with ground cover and structural vegetation measurements collected along each transect. These measurements were classified based upon distinct vegetation cover categories: woodland, intermediate, open, and open disturbed. Time series analysis of vegetation productivity utilized 111 multi-season Landsat TM5 and Landsat 8 images that captured spring, fall, and winter seasonalities across the 27-year time period spanning from 1988 to 2015, with the Soil-Adjusted Vegetation Index (SAVI) calculated as an indicator of vegetative productivity. Harmonic regression was applied to this time series in order to determine the relationship between time of year with respect to growing season and productivity level across vegetation cover types. Fixed mean harmonic amplitudes for calendar year productivity were calculated across the entire time period as well as across three natural breaks-based sub-divisions of the 27-year period. Mean amplitudes per transect were then plotted against field-truthed tree-grass ratios in order to determine the relationship between tree and grass cover and mean harmonic SAVI amplitude. Harmonic regression identified inter- and intra-annual seasonal variability across cover types, with woodland sites exhibiting much lower variability in SAVI levels than open sites. These results provide insight into the phenological response of vegetation cover types typical of semi-arid savannas to spatially and temporally variable precipitation levels and land management schemes, allowing for better informed management and monitoring strategies for these environments.
White, Joanne C.Saarinen, NinniWulder, Michael A.Kankare, Ville...
13页
查看更多>>摘要：Information regarding the nature and rate of forest recovery is required to inform forest management, monitoring, and reporting activities. Delayed establishment or return of forests has implications to harvest rotations and carbon uptake, among others, creating a need for spatially-explicit, large-area, characterizations of forest recovery. Landsat time series (LTS) has been demonstrated as a means to quantitatively relate forest recovery, noting that there are gaps in our understanding of the linkage between spectral measures of forest recovery and manifestations of forest structure and composition. Field plots provide a means to better understand the linkage between forest characteristics and spectral recovery indices. As such, from a large set of existing field plots, we considered the conditions present for the year in which the co-located pixel was considered spectrally recovered using the Years to Recovery (Y2R) metric. Y2R is a long-term metric of spectral recovery that indicates the number of years required for a pixel to return to 80% of its pre-disturbance Normalized Burn Ratio value. Absolute and relative metrics of recovery at 5 years post-disturbance were also considered. We used these three spectral recovery metrics to predict the stand development class assigned by the field crew for 284 seedling plots with an overall accuracy of 73.59%, with advanced seedling stands more accurately discriminated (omission error, OE = 15.74%) than young seedling stands (OE = 49.84%). We then used field-measured attributes (e.g. height, stem density, dominant species) from the seedling plots to classify the plots into three spectral recovery groups, which were defined using the Y2R metric: spectral recovery in (1) 1-5 years, (2) 6-10 years, or (3) 11-15 years. Overall accuracy for spectral recovery groups was 61.06%. Recovery groups 1 and 3 were discriminated with greater accuracy (producer's and user's accuracies > 66%) than recovery group 2 ( < 50%). The top field-measured predictors of spectral recovery were mean height, dominant species, and percentage of stems in the plot that were deciduous. Variability in stand establishment and condition make it challenging to accurately discriminate among recovery rates within 10 years post-harvest. Our results indicate that the long-term metric Y2R relates to forest structure and composition attributes measured in the field and that spectral development post-disturbance corresponds with expectations of structural development, particularly height, for different species, site types, and deciduous abundance. These results confirm the utility of spectral recovery measures derived from LTS data to augment landscape-level assessments of post-disturbance recovery.
NSTL
Elsevier