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：Applying numerous features is common for complex land cover classification which makes feature selection necessary. Although the selected feature subsets may yield high overall accuracies in the case of spectrally similar classes such as wetlands, several individual accuracies are often low. A reason is that one set of features used to separate one specific class from the rest might not be appropriate for delineating another class. An additional reason is that while the overall accuracy is applied to evaluate the potential of a feature subset, it may be influenced by a few high-accuracy classes that are spectrally distinct and for which collecting enough training data is feasible. In this article, rather than simultaneously mapping all classes, they were individually classified using a different feature selection. Spectral analysis was applied to determine both the order of the classes to be mapped and a merging scheme that was applied to the remaining classes to increase the accuracy of the target class. The proposed approach was applied to wetland mapping using five pilot sites throughout Newfoundland and Labrador, Canada. The dataset available for each pilot site differed in quality and quantity. However, the proposed method accurately classified wetlands in all pilot sites, even those with limited satellite and/or field data, and outperformed the classic method by increasing the average producer and user accuracies of wetlands by up to 22% and 25%, respectively, and yielding overall accuracies up to 93%. Among wetland classes, Shallow Water was easier to be distinguished, as a result of being spectrally less similar to the rest of the wetland classes. Based on the obtained results, the proposed method can be effectively applied for classifications involving spectrally similar classes, including sea ice and crop mapping.
查看更多>>摘要：In many regions of the world, especially in developing countries, river network data are outdated or completely absent, yet such information is critical for supporting important functions such as flood mitigation efforts, land use and transportation planning, and the management of water resources. In this study a new method was developed for delineating river networks using Sentinel-1 imagery. Unsupervised classification was applied to multi-temporal Sentinel-1 data to discriminate water bodies from other land cover types then the outputs were combined to generate a single persistent water bodies product. A thinning algorithm was then used to delineate river centre lines which were converted into vector features and built into a topologically structured geometric network. The complex river system of the Niger Delta was used to compare the performance of the Sentinel-based method against alternative freely available waterbody products from USGS, ESA and OpenStreetMap and a river network derived from a SRTM DEM. From both raster-based and vector-based accuracy assessments it was found that the Sentinel-based river network products were superior to the comparator data sets by a substantial margin. The resulting geometric river network was used to perform flow routing analysis which is important for a variety of environmental management and planning applications. The approach developed in this study holds considerable potential for generating up to date, detailed river network data for the many countries globally where such data are deficient.
查看更多>>摘要：Hurricane Bob passed over the New England region in August 1991, causing significant damage to life, property, and the 'environment, making it one of the costliest hurricanes in New England history. The environmental impact of a hurricane of this magnitude warrants careful assessment to devise preventive measures and mitigation strategies to bolster water resources management programs against future events. In this paper, we show the reconstructed simultaneous impacts of Hurricane Bob on the vegetative cover of the Mattapoisett river watershed and the water quality of the Mattapoisett Harbor with the aid of remote sensing for earth observations. The water quality impacts, especially in terms of Total Organic Carbon (TOC) and Sea Surface Salinity (SSS), can be identified from variations of SSS and TOC near coastal estuaries due to the influx of freshwater from the coastal Mattapoisett River to the continent-ocean transition between natural tides and bay waves. Using the Landsat satellite images, the Normalized Difference Vegetation Index (NDVI) and water quality constitutes (TOC and SSS) were reconstructed and retrieved for the assessment of the sea-land interactions during the Hurricane Bob event in 1991. Results indicate phenomenal interactive patterns between the harbor and the coastal watershed, as well as the riverine system. TOC and NDVI, especially in the upper watershed region, can be strongly correlated with hurricane impacts according to the singular value decomposition analysis.
查看更多>>摘要：Spatial patterns are not only the foundation for the understanding of plant interactions, but also reflect the spatial processes among plant populations. The primary requirement of spatial pattern analysis is the collections of location information of individual plants. In this study, we used low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology to obtain regional high-precision remote sensing images for Haloxylon ammodendron (H. ammodendron) forest in the southwestern Gurbantunggut Desert, and extracted spatial position information to analyze the spatial patterns using Ripley's L(r) function. Applying and comparing seven spatial position extraction methods, this study showed that the index RGRI (Red-Green Ratio Index) made 83.46% accuracy in the spatial position extraction, and an accuracy of 79.48% was obtained using NGBDI (Normalized Green-Blue Difference Index), while other five location information extraction methods resulted relatively lower accuracy. Results from spatial pattern analysis indicated that the extraction by UAV remote sensing were consistent with those obtained by field measurements. The H. ammodendron population showed a random distribution within the scale of 0-15 m, which suggested that the dependence of mutual asylum between individuals was low and not important. This distribution may be caused by the intense competition of individual vegetation for soil moisture, nutrients and other resources in desert areas. This study with low-altitude UAV imagery index analysis provided an efficient approach to rapid monitoring of plant population distribution characteristics in desert areas.
查看更多>>摘要：Leaf Area Index (LAI) is a significant indicator of the forest dynamics and the ecological processes such as, the balance of global carbon exchange, the energy cycle in photosynthesis, evapotranspiration mechanisms, and water/nutrient cycling. LAI can be calculated based on the 3D point cloud data (PCD) collected by Terrestrial Light Detection and Ranging (LiDAR). The methods presented in literature calculate LAI indirectly and by applying the atudliary models and parameters such as radiative transfer modelgap fraction or the extinction coefficient. Conventionally, the LiDAR PCD is confined to a large cubic box and divided to a 3D array of voxels with uniform and arbitrary size. This generates a large number of empty voxels without any cloud points (OFF voxels). These OFF voxels are counted to calculate the gap fraction with a large error threshold. This paper addresses the mentioned drawbacks and presents a new simple and direct method for LAI calculation. In proposed method, intermediate auxiliary models and parameters are avoided. The LiDAR PCD is voxelized adaptively based on the variable local spatial point density and also the natural characteristics of the canopy. It does not require the large confining box around the tree PCD and counting of OFF voxels. In addition, the effect of varying laser beam diameter is considered in calculations. This method is applied to a Canadian boreal forest and the sensitivity of the calculated LAI to different parameters such as voxel size is analyzed.
查看更多>>摘要：Maps of mangroves have often been limited to showing the presence or absence of mangrove trees and seldom have studies shown an important indicator of ecosystem integrity such as vegetation cover. Fractional Vegetation Cover (FVC) is used to assess ecosystem health, land cover and carbon stocks, hence accurately measuring FVC is an important task for scientists and land managers. Many methods have been proposed to measure FVC and simple linear models are commonly used. We created an experiment that allowed us to: 1) acquire very detailed hyperspectral imagery (1 mm pixel size) from a simulated mangrove forest, 2) measure the effect of water depth on FVC estimations, and 3) compare the relationship of eight spectral bands and indices with FVC using linear and non-linear models. After acquiring the imagery we corrected for dark signal and a white reference, performed spectral and spatial resampling, and created linear and non-linear models across four pixel sizes. Our results suggest that 1) linear and beta models have similar performance across all pixel sizes; 2) Soil Adjusted Vegetation Index (SAW), Modified Soil Adjusted Vegetation Index2 (MSAVI2) and Enhanced Vegetation Index (EVI) perform better than the Normalized Difference Vegetation Index (NDV), and, 3) our models perform better at fine pixel sizes than coarse scales. We tested our results on high-resolution satellite imagery with similar results and, therefore, recommend using SAW, EVI or MSAVI2 when predicting FVC instead of NDVI.
查看更多>>摘要：Accurate mapping of impervious surface is essential for both urbanization monitoring and micro-ecosystem research. However, the confusion between impervious surface and bare soil is the major concern due to their high spectral similarity in optical imagery. Integration of multi-sensor images is considered to offer a better capacity for distinguishing impervious surface from background. In this paper, a new impervious surface index namely nighttime light adjusted impervious surface index (NAISI), which integrates information from Landsat and nighttime lights (NTL) data from International Space Station (NTL-ISS), is proposed. Parallel to baseline subtraction approaches, NAISI integrate the information from the first component of principal component (PC) transformation of NTL-ISS, the Soil Adjusted Vegetation Index (SAVI) and the third component of tasseled cap transform (TC3) of the Landsat data. Visual interpretation and quantitative indices (SDI, Kappa and overall accuracy) were adopted to elevate the accuracy and separability of NAISI. Comparative analysis with NTL derived light intensity, optical indices, as well as existing optical-NTL indices were conducted to examine the performance of NAISI. Results indicate that NAISI achieves a more promising capability in impervious surface mapping. This demonstrates the superiority of integration of optical and nighttime lights information for imperviousness detection.
查看更多>>摘要：The 'green infrastructure typology' (GIT) scheme is a standardised framework to map and classify urban landscapes into 34 standard classes, each defined by a specific land cover composition and spatial configuration of vegetation. Previous studies have confirmed that GIT classifications can be successfully derived from airborne remote sensing data; nonetheless, the promotion of the GIT scheme as a framework for the assessment of ecosystem services such as 'climate moderation' requires further validations using a range of study areas with different vegetation conditions, and datasets from different seasons and times of the day. This study expands on previous research and evaluates the quality of thermal delineations by examining the spatio-temporal patterns and intra-/inter-typology differences of land surface temperatures (LSTs) using Sydney as case study. Further, this paper discusses the advantages and disadvantages of the classification framework and methods for mapping and assessing the thermal conditions of green infrastructure (GI). Evidence indicates a strong spatial dependericy of LSTs that may have significant implications in the interpretation and precision of numerical or predictive models. Results for spatial clustering demonstrate that the GIT scheme can be implemented for a rapid identification of hotspots to prioritise urban areas for heat mitigation. Statistical results confirm that LST differences among GIT classes are statistically significant for different times of the day and seasons. Significant thermal contrasts were found for most GITs at daytime (86.9% in summer and 85.5% in winter) and night-time (80.9% in summer and 73.8% in winter). Temperature differences are more distinguishable in summer and daytime, due to longer solar exposure of surfaces. It was found that the cooling effects of pervious surfaces, water and trees are significantly disturbed by transmission of heat from surrounding impervious materials. Despite good thermal differentiations among GITs, a considerable intra-variability of LSTs was detected in classes with a large proportion of impervious materials with contrasting radiative properties. This causes numerous complexities and challenges that should be explored by future studies.
查看更多>>摘要：Leaf traits and subsequently leaf spectral properties depend on the leaf phenological stage and light conditions within a canopy. The PROSPECT. radiative transfer model has been extensively and successfully used to retrieve leaf traits for mature, sunlit leaves at peak vegetation growth, i.e. summer. However, research on the quantification of leaf traits using PROSPECT across the canopy vertical profile throughout the growing season is still lacking. Therefore, this study aims at examining the effect of leaf position on the performance of the PROSPECT model in modelling leaf optical properties and retrieving leaf chlorophyll content (C-ab), equivalent water thickness (EWT), and leaf mass per area (LMA) throughout the growing season. To achieve this objective, we collected 588 leaf samples from the upper and lower canopies of deciduous stands over three seasons (i.e., spring, summer and autumn) in Bavaria Forest National Park, Germany. Leaf traits including C-ab, EWT and LMA, were measured for all the samples, and their reflectance spectra were obtained using an ASD FieldSpec-3 Pro FR spectroradiometer coupled with an Integrating Sphere. We initially assessed the performance of the PROSPECT model by comparing reflectance spectra generated in forward mode against reflectance spectra measured on leaf samples collected in the field. We subsequently inverted the PROSPECT model to retrieve C-ab, EWT and LMA using the look-up-table (LUT) approach. Our results consistently demonstrated that the measured reflectance of leaf samples collected from the lower canopy had a stronger match with PROSPECT simulated reflectance spectra, especially in the NIR spectrum compared to leaf samples collected from the upper canopy throughout the growing season. This observation concurred with the pattern of C-ab and EWT retrieval accuracies across the canopy i.e. the retrieval accuracy for the lower canopy was consistently higher (NRMSE = 0.1-0.2 for C-ab; NRMSE = 0.125-0.16 for EWT) when compared to the upper canopy (NRMSE = 0.122 - 0.269 for C-ab; NRMSE = 0.162 -0.0.258 for EWT) across all seasons. In contrast, LMA retrieval accuracies for the upper canopy (NRMSE = 0.146 - 0.184) were higher compared to the lower canopy (NRMSE = 0.162 - 0.239) for all seasons except for the spring season. For all the leaf traits examined in this study, the range in retrieval accuracy between the upper and lower canopy was greater in summer (compared to other seasons). We report for the first time that although the PROSPECT model provides reasonable retrieval accuracy of C-ab, EWT and LMA, variations in leaf biochemistry and morphology through the vertical canopy profile affects the performance of the model over the growing season. Findings of this study have important implications on field sampling protocols and upscaling leaf traits to canopy and landscape level using multi-layered physical models coupled with PROSPECT.
查看更多>>摘要：Nitrogen is one of the main required nutrients for the production of citrus plants. Farmers have used the chemical analysis of leaf tissue to determine the amount of nitrogen needed in a crop. However, its possible to directly classify the leaf nitrogen content (LNC) using remote sensing data. But, the accuracy of this methodology is yet low and is unknown how to enhance it. We propose a new approach to estimate the LNC in Valencia orange trees applying spectral analysis algorithms in multispectral images of high spatial resolution. Here we show an accuracy upper than 87% in determining the LNC in Valencia orange tree. Previous research, that also used multispectral images of high spatial resolution, obtained an accuracy lower than 65%. A total of 320 spectral measurements were obtained with a field spectroradiometer and the multispectral images were acquired with a Parrot Sequoia camera mounted in an Unmanned Aerial Vehicle (UAV). We calculated the mean values of 10 spectral measurements and created 32 spectral signatures with different nitrogen content. Each spectral signature was assigned for three LNC classes; low ( <= 27 g.kg(-1)), medium ( > 27 and <= 29 g.kg(-1)) and high ( > 29 g.kg(-1)). A band simulation was performed to Parrot Sequoia images for each spectral signature. We adopted 7 spectral analysis algorithms to determine the LNC: Constrained Energy Minimization; Linear Spectral Unmixing; Mixture Tuned Matched Filtering; Minimum Distance; Orthogonal Subspace Projection; Spectral Angle Mapper (SAM) and; Spectral Information Divergence. All these algorithms were trained using the simulated spectral signatures as input data. We used the 32 spectral signatures as training data and approximately 30,000 pixels as testing data, corresponding to the identified nitrogen content in orange-trees. The performance of the algorithms was evaluated with a confusion matrix and Receiver Operating Characteristic curves. The SAM algorithm presented the highest accuracy (overall of 87.6% with a kappa coefficient of 0.75) to determine LNC in orange trees. The proposed methodology may reduce the number of leaf tissue analysis and also optimize the monitoring process of orange orchards.
NSTL
Elsevier