High Spatial Resolution WorldView-2 Research Articles

The Influence of CLBP Window Size on Urban Vegetation Type Classification Using High Spatial Resolution Satellite Images

Urban vegetation can regulate ecological balance, reduce the influence of urban heat islands, and improve human beings’ mental state. Accordingly, classification of urban vegetation types plays a significant role in urban vegetation research. This paper presents various window sizes of completed local binary pattern (CLBP) texture features classifying urban vegetation based on high spatial-resolution WorldView-2 images in areas of Shanghai (China) and Lianyungang (Jiangsu province, China). To demonstrate the stability and universality of different CLBP window textures, two study areas were selected. Using spectral information alone and spectral information combined with texture information, imagery is classified using random forest (RF) method based on vegetation type, showing that use of spectral information with CLBP window textures can achieve 7.28% greater accuracy than use of only spectral information for urban vegetation type classification, with accuracy greater for single vegetation types than for mixed ones. Optimal window sizes of CLBP textures for grass, shrub, arbor, shrub-grass, arbor-grass, and arbor-shrub-grass are 3 × 3, 3 × 3, 11 × 11, 9 × 9, 9 × 9, 7 × 7 for urban vegetation type classification. Furthermore, optimal CLBP window size is determined by the roughness of vegetation texture.

A novel index for assessment of riparian strip efficiency in agricultural landscapes using high spatial resolution satellite imagery

Riparian strips are used worldwide to protect riverbanks and water quality in agricultural zones because of their numerous environmental benefits. A metric called Riparian Strip Quality Index, which is based on the percentage area of riparian vegetation, is used to evaluate their ecological condition. This index measures the potential capacity of riparian strips to filter sediments, retain pollutants, and provide shelter for terrestrial and aquatic species. This research aims to improve this metric by integrating the ability of riparian strips to intercept surface runoff, which is the major cause of water pollution and erosion in productive areas. In Canada and the Nordic countries, rapid surface drainage from snow melt and spring rains is often practiced to avoid production delays and losses. This reduces the efficiency of riparian buffer strips by promoting soil erosion due to concentrated runoff. A new proposed metric called Riparian Strip Efficiency Index (RSEI), incorporates not only land cover information, but topographic and hydrologic variables to model the intensity and spatial distribution of runoff streamflow, and the capability of riparian strips to retain sediments and pollutants. The research is performed over the La Chevrotière River Basin in the Portneuf municipality in Québec (Canada) using hydrological modeling, land cover and topographic data extracted from very high spatial resolution WorldView-2 imagery as a unique source of inputs. The results show that RSEI provides a better characterization of the ecosystem services of riparian strips in terms of pollutants filtration and prevention of soil erosion in agricultural areas. RSEI will allow a better management of agricultural practices such as drainage and land leveling. Further, it will provide to land managers information to monitor environmental changes and to prioritize intervention areas, which ultimately targets to ensure optimal allocation of private or public funds toward the most inefficient and threatened riparian strips.

Mapping of the corals around Hendorabi Island (Persian Gulf), using WorldView-2 standard imagery coupled with field observations

High spatial resolution WorldView-2 (WV2) satellite imagery coupled with field observations have been utilized for mapping the coral reefs around Hendorabi Island in the northern Persian Gulf. In doing so, three standard multispectral bands (red, green, and blue) were selected to produce a classified map for benthic habitats. The in-situ observations were included photo-transects taken by snorkeling in water surface and manta tow technique. The satellite image has been classified using support vector machine (SVM) classifier by considering the information obtained from field measurements as both training and control points data. The results obtained from manta tow demonstrated that the mean total live hard coral coverage was 29.04% ± 2.44% around the island. Massive corals poritiids (20.70%) and branching corals acroporiids (20.33%) showed higher live coral coverage compared to other corals. Moreover, the map produced from satellite image illustrated the distribution of habitats with 78.1% of overall accuracy.

Detection of gullies in Fort Riley military installation using LiDAR derived high resolution DEM

Intensive use of military vehicles in military installations create conditions favorable for gully formation. Gullies impede the access of vehicle, restrict the continuation of training, and lead to significant damage to vehicle and risk the life of soldiers. Therefore, it is critical to correctly identify the locations of gullies for continuous training mission. In this study, Fort Riley (FR) military installation was chosen as the study area. LiDAR derived 1 m resolution digital elevation model (DEM) acquired on 2010 was used to map the gullies. A procedure that measures local topographic position, i.e., difference from mean elevation (DFME) along with its integration to the land surface having high surface curvature values was employed. Two high spatial resolution WorldView-2 images of 2010 and field gully data collected in 2010 were utilized for accuracy assessment. Results showed that: (1) A total of 237 small and 166 large gullies were detected and most of them dominated the central west and northwest parts of the installation; (2) Based on the visual interpretation in the WorldView-2 images, there was no statistically significant difference between the detected and observed numbers of gullies; (3) Gullies measured in the field were well detected with an overall accuracy of 78%.

Detection and mapping the spatial distribution of bracken fern weeds using the Landsat 8 OLI new generation sensor

Bracken fern is an invasive plant that presents serious environmental, ecological and economic problems around the world. An understanding of the spatial distribution of bracken fern weeds is therefore essential for providing appropriate management strategies at both local and regional scales. The aim of this study was to assess the utility of the freely available medium resolution Landsat 8 OLI sensor in the detection and mapping of bracken fern at the Cathedral Peak, South Africa. To achieve this objective, the results obtained from Landsat 8 OLI were compared with those derived using the costly, high spatial resolution WorldView-2 imagery. Since previous studies have already successfully mapped bracken fern using high spatial resolution WorldView-2 image, the comparison was done to investigate the magnitude of difference in accuracy between the two sensors in relation to their acquisition costs. To evaluate the performance of Landsat 8 OLI in discriminating bracken fern compared to that of Worldview-2, we tested the utility of (i) spectral bands; (ii) derived vegetation indices as well as (iii) the combination of spectral bands and vegetation indices based on discriminant analysis classification algorithm. After resampling the training and testing data and reclassifying several times (n=100) based on the combined data sets, the overall accuracies for both Landsat 8 and WorldView-2 were tested for significant differences based on Mann-Whitney U test. The results showed that the integration of the spectral bands and derived vegetation indices yielded the best overall classification accuracy (80.08% and 87.80% for Landsat 8 OLI and WorldView-2 respectively). Additionally, the use of derived vegetation indices as a standalone data set produced the weakest overall accuracy results of 62.14% and 82.11% for both the Landsat 8 OLI and WorldView-2 images. There were significant differences {U (100)=569.5, z=−10.8242, p<0.01} between the classification accuracies derived based on Landsat OLI 8 and those derived using WorldView-2 sensor. Although there were significant differences between Landsat and WorldView-2 accuracies, the magnitude of variation (9%) between the two sensors was within an acceptable range. Therefore, the findings of this study demonstrated that the recently launched Landsat 8 OLI multispectral sensor provides valuable information that could aid in the long term continuous monitoring and formulation of effective bracken fern management with acceptable accuracies that are comparable to those obtained from the high resolution WorldView-2 commercial sensor.

Remote-Sensed Mapping of Sargassum spp. Distribution around Rottnest Island, Western Australia, Using High-Spatial Resolution WorldView-2 Satellite Data

ABSTRACT Hoang, T.C.; O'Leary, M.J., and Fotedar, R.K., 2016. Remote-sensed mapping of Sargassum spp. distribution around Rottnest Island, Western Australia, using high-spatial resolution WorldView-2 satellite data. Satellite remote sensing is one of the most efficient techniques for marine habitat studies in shallow coastal waters, especially in clear waters where field observations can be easily carried out. However, such in situ observations have certain limitations: they are time consuming, have a limited ability to capture spatial variability, and require an interdisciplinary approach between marine biologists and remote-sensing specialists. The main objective of this study was to survey and map Sargassum beds around Rottnest Island, Western Australia, through a combination of high spatial resolution WorldView-2 imagery, using a validated depth invariant index model for water-column correction, and in-field observations. The combination of field survey data and four classification methods resulted in...

Geological structural interpretation of Qiangduo area in Tibet based on multi-source remote sensing data

Usually remote sensing data are set corresponding parameters to solve specific problems such as resource and environment; nevertheless,the geological structures from the regional scale to the hand specimen are divided into different scales,and hence a single kind of remote sensing data cannot meet the multi-scale geological structure interpretation. To solve this problem,the authors took Qiangduo area in Tibet as the study area,utilized the advantages of ETM+,ASTER,World View2 and DEM data of the study area to interpret the geological structures at two different levels from ETM+data with 30 m resolution and ASTER data with 15 m resolution to World View2 data with 0. 5 m resolution,and obtained a good result. Firstly,the interpretation of structural framework from ETM+data was carried out by interpretation keys. At the same time,the ASTER band calculation was used to indirectly reflect structural information which could verify the results of the ETM+. Furthermore,the high spatial resolution World View2 data were integrated to analyze the structure. Finally,on the basis of the field validation,the interpretation results were revised. The geological structure interpretation results of Qiangduo area show that the integrated application of multi-source remote sensing data can improve the accuracy of structure interpretation and achieve a good practical application effect in a short period of time.

Fusion of high spatial resolution WorldView-2 imagery and LiDAR pseudo-waveform for object-based image analysis

High spatial resolution (HSR) imagery and high density LiDAR data provide complementary horizontal and vertical information. Therefore, many studies have focused on fusing the two for mapping geographic features. It has been demonstrated that the synergetic use of LiDAR and HSR imagery greatly improves classification accuracy. This is especially true with waveform LiDAR data since they provide more detailed vertical profiles of geographic objects than discrete-return LiDAR data. Fusion of discrete-return LiDAR and HSR imagery mostly takes place at the object level due to the superiority of object-based image analysis (OBIA) for classifying HSR imagery.However, the fusion of the waveform LiDAR and HSR imagery at the object level has not been adequately studied. To fuse LiDAR waveform and image objects, the waveform for the objects derived from image segmentation are needed. However, the footprints of existing waveform are usually of fixed size and fixed shape, while those of building are of different size and shape. In order to obtain waveforms with footprints that match those of image objects, we proposed synthesizing object-based pseudo-waveforms using discrete-returns LiDAR data by utilizing count or intensity based histogram over the footprints of the objects. The pseudo-waveforms were then fused with the object-level spectral histograms from HSR WorldView-2 imagery to classify the image objects using a Kullback–Leibler divergence-based curve matching approach.The fused dataset achieved an overall classification accuracy of 97.58%, a kappa coefficient of 0.97, and producer’s accuracies and user’s accuracies all larger than 90%. The use of the fused dataset improved the overall accuracy by 7.61% over the use of HSR imagery alone, and McNemar’s test indicated that such improvement was statistically significant (p<0.001). This study demonstrates the great potential of pseudo-waveform in improving object-based image analysis. This is especially true since currently the majority of commercial LiDAR data are of discrete return while waveform data are still not widely available.

Detection of Alteration Induced by Onshore Gas Seeps from ASTER and WorldView-2 Data

Hydrocarbon seeps cause chemical and mineralogical changes at the surface, which can be detected by remote sensing. This paper aims at the detection of mineral alteration induced by gas seeps in a marly limestone formation, SW Iran. For this purpose, the multispectral Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the high spatial resolution WorldView-2 (WV-2) data were utilized for mapping surficial rock alteration. In addition, the potential of Visible Near Infrared (VNIR) bands of the WV-2 and its high spatial resolution for mapping alterations was determined. Band ratioing, principal component analysis (PCA), data fusion and the boosted regression trees (BRT) were applied to enhance and classify the altered and unaltered marly limestone formation. The alteration zones were identified and mapped by remote sensing analyses. Integrating the WV-2 into the ASTER data improved the spatial accuracy of the BRT classifications. The results showed that the BRT classification of the multiple band imagery (created from ASTER and WV-2) using regions of interest (ROIs) around field data provides the best discrimination between altered and unaltered areas. It is suggested that the WV-2 dataset can provide a potential tool along higher spectral resolution data for mapping alteration minerals related to hydrocarbon seeps in arid and semi-arid areas.

High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors

Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The study site was Veale Gardens in Adelaide, Australia. Image processing was applied on five images captured from February 2012 to February 2013 using ERDAS Imagine. From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences) was selected. Normalized Difference Vegetation Index (NDVI) values were derived for each category of landscape cover, namely trees, shrubs, turf grasses, impervious pavements, and water bodies. Urban landscape evapotranspiration rates for Veale Gardens were estimated through field monitoring using observational-based landscape coefficients. The relationships between remotely sensed NDVIs for the entire Veale Gardens and for individual NDVIs of different vegetation covers were compared with field measured urban landscape evapotranspiration rates. The water stress conditions experienced in January 2013 decreased the correlation between ET and NDVI with the highest relationship of ET-Landscape NDVI (Landscape Normalized Difference Vegetation Index) for shrubs (r2 = 0.66) and trees (r2 = 0.63). However, when the January data was excluded, there was a significant correlation between ET and NDVI. The highest correlation for ET-Landscape NDVI was found for the entire Veale Gardens regardless of vegetation type (r2 = 0.95, p &gt; 0.05) and the lowest one was for turf (r2 = 0.88, p &gt; 0.05). In support of the feasibility of ET estimation by WV2 over a longer period, an algorithm recently developed that estimates evapotranspiration rates based on the Enhanced Vegetation Index (EVI) from MODIS was employed. The results revealed a significant positive relationship between ETMODIS and ETWV2 (r2 = 0.9857, p &gt; 0.05). This indicates that the relationship between NDVI using high resolution WorldView-2 imagery and ground-based validation approaches could provide an effective predictive tool for determining ET rates from unstressed mixed urban landscape plantings.

Leaf Area Index (LAI) Estimation in Boreal Mixedwood Forest of Ontario, Canada Using Light Detection and Ranging (LiDAR) and WorldView-2 Imagery

Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the precision required by forest managers for tactical planning. This paper focuses on estimating LAI from: (i) height and density metrics derived from Light Detection and Ranging (LiDAR); (ii) spectral vegetation indices (SVIs), in particular the Normalized Difference Vegetation Index (NDVI); and (iii) a combination of these methods. For the Hearst Forest of Northern Ontario, in situ measurements of LAI were derived from digital hemispherical photographs (DHPs) while remote sensing variables were derived from low density LiDAR (i.e., 1 m−2) and high spatial resolution WorldView-2 data (2 m). Multiple Linear Regression (MLR) models were generated using these variables. Results from these analyses demonstrate: (i) moderate explanatory power (i.e., R2 = 0.53) for LiDAR height and density metrics that have proven to be related to canopy structure; (ii) no relationship when using SVIs; and (iii) no significant improvement of LiDAR models when combining them with SVI variables. The results suggest that LiDAR models in boreal forest environments provide satisfactory estimations of LAI, even with narrow ranges of LAI for model calibration. Models derived from low point density LiDAR in a mixedwood boreal environment seem to offer a reliable method of estimating LAI at high spatial resolution for decision makers in the forestry community. This method can be easily incorporated into simultaneous modeling efforts for forest inventory variables using LiDAR.

Multisensor imagery analysis for mapping and assessment of 12 January 2010 earthquake-induced building damage in Port-au-Prince, Haiti

On 12 January 2010, a devastating earthquake of magnitude 7 struck the densely populated Haitian capital city of Port-au-Prince and several other towns along the bay of Port-au-Prince. The severely impacted areas lie in close proximity to the epicentre of the earthquake. The earthquake impacted more than 5 million people, of which more than 230,000 people lost their lives and approximately 1.2 million people were left homeless. In this study, a new multisensor imagery-based analysis approach is presented for detection, delineation, and mapping of collapsed and severely damaged buildings. The thrust of the approach is on improving the accuracy of pixel-based analysis performed on the post-earthquake Advanced Land Imager by utilizing very high spatial resolution WorldView-2 imagery acquired immediately after the earthquake. The study also highlights some of the challenges and constraints of traditional pixel-based analysis performed on medium-resolution imagery for detection, delineation, and mapping of earthquake-induced damage to buildings in a spectrally complex high-density urban environment.

Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data

Impervious surface quantification is important for many planning and management applications because of the impacts that impervious surfaces have on a range of environmental resources such as groundwater. This research proposes an integrated method to quantify impervious surfaces at multiple spatial scales via a synthesis of several machine learning approaches. In this study, we 1) proposed a hierarchical classification method to detect impervious surfaces through a fusion of optimized artificial immune networks (OPTINC) and decision trees at high spatial resolution, 2) evaluated the method using multi-sensor data (i.e., high spatial resolution WorldView-2 and LiDAR data) to map impervious surfaces, 3) tested the applicability of the binary impervious surface maps to quantify sub-pixel imperviousness from Landsat TM data of a larger region using regression trees at moderate spatial resolution, and 4) examined the model sensitivity of regression trees to training sample size for impervious surface quantification. OPTINC and decision trees successfully identified impervious surfaces at high resolution (overall accuracy > 90%). The regression tree predicted imperviousness from the TM data with a moderate success ( R 2 = 0.64 and MAE = 14.2%). Although the regression tree appeared robust when training sample size was sufficiently large, it was not stable with small sample size (e.g., < 100).