查看更多>>摘要：This study focused on land cover mapping based on synthetic images, especially using the method of spatial and temporal classification as well as the accuracy validation of their results. Our experimental results indicate that the accuracy of land cover map based on synthetic imagery and actual observation has a similar standard compared with actual land cover survey data. These findings facilitate land cover mapping with synthetic data in the area where actual observation is missing. Furthermore, in order to improve the quality of the land cover mapping, this research employed the spatial and temporal Markov random field classification approach. Test results show mat overall mapping accuracy can be increased by approximately 5% after applying spatial and temporal classification. This finding contributes towards the achievement of higher quality land cover mapping of areas with missing data by using spatial and temporal information.
查看更多>>摘要：In recent years, oil spills in coastal regions have received a lot of public concern for its strong impact on the coastal ecological system. Synthetic aperture radar (SAR) is regarded as one of the most suitable sensors for oil spill monitoring for its wide-area and all-day all-weather surveillance capabilities. However, due to its special imaging mechanism, multiplicative speckle noise and dark patches caused by other physical phenomena always affect the accuracy of oil spill detection. In this work, an oil spill detection method based on dual-threshold segmentation and support vector machine was proposed. Experiments on SAR images illustrated the effectiveness of the proposed method in detecting and tracing oil spill from SAR images.
查看更多>>摘要：The red edge region of a hyperspectral vegetation reflectance curve provides important information regarding the biochemical and biophysical parameters of plants such as stress, senescence, and chlorophyll capacity. However, shifts of the red edge position (REP) to longer or shorter wavelengths have also been correlated with other factors such as water content, nitrogen, and salinity. These other factors can confuse the effect of chlorophyll on REP. The objective of this study is to define two new hyperspectral curve indices, the red valley width (RVW) and the chlorophyll absorption region (CAR) that are designed to provide less-sensitive characterizations of the chlorophyll content of vegetation in order to allow better comparisons among spatially or temporally distant populations of vegetation. The RVW and the CAR are both located in the visible near-infrared portion of the light spectrum and are derived from multiple hyperspectral curve features that have been found to be correlated with chlorophyll content, thus making them less sensitive to other biophysical and biochemical factors that can affect the REP independently. The robustness of the two new features is tested using the Leaf Optical Properties Experiment database, and the findings are used to compare two populations of saltcedar (Tamarix spp.) from a native habitat in China and an invasive habitat in the USA. Saltcedar is a highly invasive plant species in the USA but does not pose the same ecological and economic threats in its native habitat throughout Eurasia. The findings are interpreted in the context of the environmental characteristics of each region.
查看更多>>摘要：Conventional change detection approaches are mainly based on per-pixel processing, which ignore the sub-pixel spectral variation resulted from spectral mixture. Especially for medium-resolution remote sensing images used in urban land-cover change monitoring, land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution. Thus, traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably, degrading the overall accuracy of change detection. In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level, a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion. Nonlinear spectral mixture model is selected for spectral unmixing, and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition evidences. The proposed method is tested on multi-temporal Landsat Thematic Mapper and China-Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban areas. The effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods (i.e. change vector analysis and principal component analysis-based method). In particular, the proposed sub-pixel change detection approach not only provides the binary change information, but also obtains the characterization about change direction and intensity, which greatly extends the semantic meaning of the detected change targets.
查看更多>>摘要：The aim of this study is to estimate and compare soil erosion, in the Mount Elgon region, eastern Uganda, during the last decade. Possible trends and changes in erosion are linked to precipitation/climate change as well as changes in land cover. Two different versions of the Revised Universal Soil loss Equation (RUSLE) are implemented and compared, one using slope length and the other using flow accumulation to estimate the slope length and steepness factor (LS). Comparisons of the modeled soil erosion vs. field data indicate that RUSLE based on flow accumulation is preferable. The modeling is carried out for the years 2000, 2006, and 2012, and is based on ASTER remotely sensed data, digital elevation models, precipitation data from the study area, as well as existing soil maps. No significant trends in estimated soil erosion are found to be present during the last decade. Over exploitation of land is probably compensated by improved agricultural management and no significant increase in precipitation. Even if there are reports of more intense and increasing amounts of rainfall in the area, this could not be verified, neither through the analysis of climate data, nor by trends in the estimated soil loss.
查看更多>>摘要：Thermal remote sensing imagery is helpful for land cover classification and related analysis. Unfortunately, the spatial resolution of thermal infrared (TIR) band is generally coarser than that of visual near-infrared band, which limits its more precise applications. Various thermal sharpening (TSP) techniques have been developed for improving the spatial resolution of the imagery of TIR band or land surface temperature (LST). However, there is no research on the theoretical estimation of TSP error till now, which implies that the error in sharpened LST imagery is unknown and the further analysis might be not reliable. In this paper, an error estimation method based on classical linear regression theory for the linear-regression-based TSP techniques was firstly introduced. However, the scale difference between the coarse resolution and fine resolution is not considered in this method. Therefore, we further developed an improved error estimation method with the consideration of the scale difference, which employs a novel term named equivalent random sample size to reflect the scale difference. A simulation study of modified TsHARP (a typical TSP technique) shows that the improved method estimated the TSP error more accurately than classical regression theory. Especially, the phenomena that TSP error increases with the increasing resolution gap between the initial and target resolutions can be successfully predicted by the proposed method.
Belinda A. MARGONOJean-Robert B. BWANGOYPeter V. POTAPOVMatthew C. HANSEN...
60-71页
查看更多>>摘要：Wetlands play an important role in the provision of ecosystem services, ranging from the regulation of hydrological systems to carbon sequestration and biodiversity habitat. This paper reports the mapping of Indonesia's wetland cover as a single thematic class, including peatlands, freshwater wetlands, and mangroves. Expert-interpreted training data were used to identify wetland formations including areas of likely past wetland extent that have been converted to other land uses. Topographical indices (Shuttle Radar Topography Mission-derived) and optical (Landsat) and radar (PALSAR) image inputs were used to build a bagged classification tree model based on training data in order to generate a national-scale map of wetland extent at a 60 m spatial resolution. The resulting wetland map covers 21.0% (39.6 Mha) of Indonesia's land, including 25.2% of Sumatra (11.9 Mha), 22.9% of Kalimantan (12.2 Mha), and 28.9% of Papua (11.8 Mha). Results agree with existing image-interpreted products from Indonesia's Ministries of Forestry and Agriculture and Wetlands International (89% overall agreement), and with the Ministry of Forestry forest inventory data for Sumatra and Kalimantan (91% overall agreement). An internally consistent algorithm-derived national wetland extent map can be used to quantify changing rates of land conversion inside and outside of wetlands. Additionally, wetlands extent can be used to efficiently allocate field resources in national assessments of wetland sub-types such as peatlands, which are a current focus of policies aiming to reduce carbon emissions from land use change.
NETL
NSTL
Taylor & Francis