27 results on '"worldview-2 imagery"'
Search Results
2. Using WorldView-2 Imagery to Estimate Mangroves Density in the Porong Estuary
- Author
-
Setiawan, Agus, Realino, Bernadinus, Triyulianti, Iis, Hamzah, Faisal, Murdimanto, Ari, Putri, Mutiara Rachmat, Nugroho, Dwiyoga, Barale, Vittorio, editor, and Gade, Martin, editor
- Published
- 2019
- Full Text
- View/download PDF
3. Mapping of Benthic Habitats in Komave, Coral Coast Using WorldView-2 Satellite Imagery
- Author
-
Naidu, Roselyn, Muller-Karger, Frank, McCarthy, Mathew, and Leal Filho, Walter, Series editor
- Published
- 2018
- Full Text
- View/download PDF
4. URBAN BLUE GROUND OBJECT EXTRACTION BY USING A NEW SPECTRAL INDEX FROM WORLDVIEW-2.
- Author
-
Huaipeng Liu, Yongxin Zhang, and Huijun An
- Abstract
Blue ground objects are typical ground objects in the urban environment, and the extraction of this type of ground objects is of great significance in urban environmental monitoring. To quickly extract blue ground objects from remote sensing images, this study takes WorldView-2 data as an example through the difference operation between some key bands, accumulates the difference image and performs ratio processing, enhances the information of blue ground objects and weakens the information of other ground objects in the newly generated image. A spectral index that can extract blue ground objects is then created. After the creation of the blue ground objects spectral index, an appropriate threshold is used to extract the blue ground objects in the image. Experimental results show that the new spectral threshold method can effectively detect blue ground objects in the urban image of WorldView-2 with high extraction accuracy, simple process and fast speed. [ABSTRACT FROM AUTHOR]
- Published
- 2020
5. Towards Monitoring of Nutrient Pollution in Coastal Lake Using Remote Sensing and Regression Analysis
- Author
-
Maruf Mortula, Tarig Ali, Abdallah Bachir, Ahmed Elaksher, and Mohamed Abouleish
- Subjects
Chlorophyll-a ,eutrophication ,nutrients ,GIS ,WorldView-2 imagery ,Hydraulic engineering ,TC1-978 ,Water supply for domestic and industrial purposes ,TD201-500 - Abstract
The last few decades have witnessed a tremendous increase in nutrient levels (phosphorus and nitrogen) in coastal water leading to excessive algal growth (Eutrophication). The presence of large amounts of algae turns the water’s color into green or red, in the case of algal blooms. Chlorophyll-a is often used as an indicator of algal biomass. Due to increased human activities surrounding Dubai creek, there have been eutrophication concerns given the levels of nutrients in that creek. This study aims to map chlorophyll-a in Dubai Creek from WorldView-2 imagery and explore the relationship between chlorophyll-a and other eutrophication indicators. A geometrically- and atmospherically-corrected WorldView-2 image and in-situ data have been utilized to map chlorophyll-a in the creek. A spectral model, developed from the WorldView-2 multispectral image to monitor Chlorophyll-a concentration, yielded 0.82 R2 with interpolated in-situ chlorophyll-a data. To address the time lag between the in-situ data and the image, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were used to demonstrate the accuracy of the WorldView-2 model. The images, acquired on 20 May and 23 July 2012, were processed to extract chlorophyll-a band ratios (Band 4/Band 3) following the standard approach. Based on the availability, the 20 May image acquisition date is the closest to the middle of Quarter 2 (Q2) of the in-situ data (15 May). The 23 July 2012 image acquisition date is the closest to the WorldView-2 image date (24 July). Another model developed to highlight the relationship between spectral chlorophyll-a levels, and total nitrogen and orthophosphate levels, yielded 0.97 R2, which indicates high agreement. Furthermore, the generated models were found to be useful in mapping chlorophyll-a, total nitrogen, and orthophosphate, without the need for costly in-situ data acquisition efforts.
- Published
- 2020
- Full Text
- View/download PDF
6. EVALUASI CITRA WORLDVIEW-2 UNTUK PENDUGAAN KEDALAMAN PERAIRAN DANGKAL PULAU KELAPA-HARAPAN MENGGUNAKAN ALGORITMA RASIO BAND
- Author
-
Tarlan Subarno, V P Siregar, and S B Agus
- Subjects
WorldView-2 imagery ,water depth ,shallow water ,Geography. Anthropology. Recreation ,Geography (General) ,G1-922 - Abstract
Remote sensing technology is so advanced that recently produced satellite sensors with the capability to provide imagery options with very high spatial resolutions. One of the options is WorldView-2 that has 1.84 meter of spatial resolution. Besides, WorldView-2 also has at least five bands on visible rays. The capability of remote sensing for underwater detection through specific depths and the availability of its bands on visible rays give more appropriate options to apply logarithm bands ratio on shallow water depth estimation. This research is aimed at analyzing the capability of WorldView-2 imagery to estimate shallow water depth of Kelapa-Harapan islands by using bands ratio algorithm. There were six bands combination used in applying band 1 through band 4 of WorldView-2 imagery. The results have shown that the best combination of bands to estimate the shallow water depth in the study area is the ratio between band 1 and band 3 with the R2 value of 0.067 and the average bias of 0.66 m. The ratio between band 1 and band 4 gave the value of R2 as big as 0.55 of its regression to the field depth samples. Meanwhile, the other four bands combination ratios have shown very low correlations to the water depth in the field. © 2015 GJGP UNDIP. All rights reserved.
- Published
- 2015
- Full Text
- View/download PDF
7. Mapping vegetation functional types in urban areas with WorldView-2 imagery: Integrating object-based classification with phenology.
- Author
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Yan, Jingli, Zhou, Weiqi, Han, Lijian, and Qian, Yuguo
- Subjects
PHENOLOGY ,URBAN plants ,BIODIVERSITY ,OPEN spaces ,PUBLIC spaces - Abstract
Mapping urban vegetation is a prerequisite to accurately understanding landscape patterns and ecological services provided by urban vegetation. However, the uncertainties in fine-scale vegetation biodiversity mapping still exist in capturing vegetation functional types efficiently at fine scale. To facilitate the application of fine-scale vegetation spatial configuration used for urban landscape planning and ecosystem service valuation, we present an approach integrating object-based classification with vegetation phenology for fine-scale vegetation functional type mapping in compact city of Beijing, China. The phenological information derived from two WorldView-2 imagery scenes, acquired on 14 September 2012 and 26 November 2012, was used to aid in the classification of tree functional types and grass. Then we further compared the approach to that of using only one WorldView imagery. We found WorldView-2 imagery can be successfully applied to map functional types of urban vegetation with its high spatial resolution and relatively high spectral resolution. The application of the vegetation phenology into classification greatly improved the overall accuracy of classification from 82.3% to 91.1%. In particular, the accuracies of vegetation types was improved by from 10% to 13.26%. The approach integrating vegetation phenology with high-resolution remote sensed images provides an efficient tool to incorporate multi-temporal data into fine-scale urban classification. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
8. Mapping the Abundance of Multipurpose Agroforestry Faidherbia albida Trees in Senegal
- Author
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Lu, Tingting, Brandt, Martin, Tong, Xiaoye, Hiernaux, Pierre, Leroux, Louise, Ndao, Babacar, Fensholt, Rasmus, Lu, Tingting, Brandt, Martin, Tong, Xiaoye, Hiernaux, Pierre, Leroux, Louise, Ndao, Babacar, and Fensholt, Rasmus
- Abstract
Multi-purpose Faidherbia albida trees represent a vital component of agroforestry parklands in West Africa as they provide resources (fodder for livestock, fruits and firewood) and support water lifting and nutrient recycling for cropping. Faidherbia albida trees are characterized by their inverse phenology, growing leaf flowers and pods during the dry season, thereby providing fodder and shedding leaves during the wet season, which minimizes competition with pastures and crops for resources. Multi-spectral and multi-temporal satellite systems and novel computational methods open new doors for classifying single trees and identifying species. This study used a Multi-Layer Perception feedforward artificial neural network to classify pixels covered by Faidherbia albida canopies from Sentinel-2 time series in Senegal, West Africa. To better discriminate the Faidherbia albida signal from the background, monthly images from vegetation indices were used to form relevant variables for the model. We found that NDI54/NDVI from the period covering onset of leaf senescence (February) until end of senescence (leaf-off in June) to be the most important, resulting in a high precision and recall rate of 0.91 and 0.85. We compared our result with a potential Faidherbia albida occurrence map derived by empirical modelling of the species ecology, which deviates notably from the actual species occurrence mapped by this study. We have shown that even small differences in dry season leaf phenology can be used to distinguish tree species. The Faidherbia albida distribution maps, as provided here, will be key in managing farmlands in drylands, helping to optimize economic and ecological services from both tree and crop products.
- Published
- 2022
9. Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2 Imagery and a Hierarchical Cascade Convolutional Neural Network
- Author
-
Yongyong Fu, Ziran Ye, Jinsong Deng, Xinyu Zheng, Yibo Huang, Wu Yang, Yaohua Wang, and Ke Wang
- Subjects
marine aquaculture areas ,WorldView-2 imagery ,fully convolutional network (FCN) ,land-use and land-cover (LULC) mapping ,Science - Abstract
Marine aquaculture plays an important role in seafood supplement, economic development, and coastal ecosystem service provision. The precise delineation of marine aquaculture areas from high spatial resolution (HSR) imagery is vital for the sustainable development and management of coastal marine resources. However, various sizes and detailed structures of marine objects make it difficult for accurate mapping from HSR images by using conventional methods. Therefore, this study attempts to extract marine aquaculture areas by using an automatic labeling method based on the convolutional neural network (CNN), i.e., an end-to-end hierarchical cascade network (HCNet). Specifically, for marine objects of various sizes, we propose to improve the classification performance by utilizing multi-scale contextual information. Technically, based on the output of a CNN encoder, we employ atrous convolutions to capture multi-scale contextual information and aggregate them in a hierarchical cascade way. Meanwhile, for marine objects with detailed structures, we propose to refine the detailed information gradually by using a series of long-span connections with fine resolution features from the shallow layers. In addition, to decrease the semantic gaps between features in different levels, we propose to refine the feature space (i.e., channel and spatial dimensions) using an attention-based module. Experimental results show that our proposed HCNet can effectively identify and distinguish different kinds of marine aquaculture, with 98% of overall accuracy. It also achieves better classification performance compared with object-based support vector machine and state-of-the-art CNN-based methods, such as FCN-32s, U-Net, and DeeplabV2. Our developed method lays a solid foundation for the intelligent monitoring and management of coastal marine resources.
- Published
- 2019
- Full Text
- View/download PDF
10. Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery
- Author
-
Jingli Yan, Stijn Van der Linden, Yunyu Tian, Jo Van Valckenborgh, Veerle Strosse, and Ben Somers
- Subjects
garden landscapes ,Technology ,TREE SPECIES CLASSIFICATION ,IKONOS ,domestic gardens ,greenspace mapping ,multi-temporal stereo imagery ,vegetation types ,WORLDVIEW-2 IMAGERY ,Environmental Sciences & Ecology ,Greenspace mapping ,Vegetation types ,Remote Sensing ,MAPPING VEGETATION ,Laboratory of Geo-information Science and Remote Sensing ,PHENOLOGY ,DOMESTIC GARDENS ,Laboratorium voor Geo-informatiekunde en Remote Sensing ,Geosciences, Multidisciplinary ,Imaging Science & Photographic Technology ,Multi-temporal stereo imagery ,OBJECT-BASED CLASSIFICATION ,Science & Technology ,LANDSCAPE ,Garden landscapes ,Domestic gardens ,Geology ,PE&RC ,LAND-COVER ,Physical Sciences ,URBAN AREAS ,General Earth and Planetary Sciences ,Life Sciences & Biomedicine ,Environmental Sciences - Abstract
Domestic gardens provide residents with immediate access to landscape amenities and numerous ecological provisions. These ecological provisions have been proven to be largely determined by greenspace composition and landscape, but the fragmentation and heterogeneity of garden environments present challenges to greenspace mapping. Here, we first developed a recognition method to create a garden parcel data set in the medieval Leuven city of Belgium, based on the land use layers and agricultural land parcels. Then, we applied multi-sourced satellite imagery to evaluate the added value of spatial resolution, plant phenology and 3D structure in identifying four vegetation types. Finally, we characterized the greenspace landscapes in garden parcels. Compared with single ALOS-2 imagery, SPOT-7 imagery and Pleiades-1A imagery increased the overall accuracy by 4% and 8%, respectively. The accuracy improvement (21%) produced from multi-temporal stereo Pleiades-1A imagery strongly verified the significance of plant phenology and 3D structure in garden mapping. The average greenspace cover in garden parcels was 71% but varied from 56% in urban gardens to 82% in rural gardens. The garden greenspace landscape is fragmented by the artificial structures in urban areas but has a more aggregated size and less complex shapes in rural areas. This study calls for greater attention to be paid to gardens, and for multi-disciplinary studies conducted in collaboration with urban ecologists and landscape designers to maximize the benefits to residents of both immediate landscape amenities and ecological provisions, in the face of global environmental changes and public health risks.
- Published
- 2022
- Full Text
- View/download PDF
11. Exploring the Potential of World View-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms.
- Author
-
Yuanhui Zhu, Kai Liu, Lin Liu, Myint, Soe W., Shugong Wang, Hongxing Liu, and Zhi He
- Subjects
- *
MANGROVE forests , *MACHINE learning , *REMOTE sensing , *ARTIFICIAL neural networks , *SUPPORT vector machines - Abstract
To accurately estimate leaf area index (LAI) in mangrove areas, the selection of appropriate models and predictor variables is critical. However, there is a major challenge in quantifying and mapping LAI using multi-spectral sensors due to the saturation effects of traditional vegetation indices (VIs) for mangrove forests. World View-2 (WV2) imagery has proven to be effective to estimate LAI of grasslands and forests, but the sensitivity of its vegetation indices (VIs) has been uncertain for mangrove forests. Furthermore, the single model may exhibit certain randomness and instability in model calibration and estimation accuracy. Therefore, this study aims to explore the sensitivity of WV2 VIs for estimating mangrove LAI by comparing artificial neural network regression (ANNR), support vector regression (SVR) and random forest regression (RFR). The results suggest that the RFR algorithm yields the best results (RMSE = 0.45, 14.55% of the average LAI), followed by ANNR (RMSE = 0.49, 16.04% of the average LAI), and then SVR (RMSE = 0.51, 16.56% of the average LAI) algorithms using 5-fold cross validation (CV) using all VIs. Quantification of the variable importance shows that the VIs derived from the red-edge band consistently remain the most important contributor to LAI estimation. When the red-edge band-derived VIs are removed from the models, estimation accuracies measured in relative RMSE (RMSEr) decrease by 3.79%, 2.70% and 4.47% for ANNR, SVR and RFR models respectively. VIs derived from red-edge band also yield better accuracy compared with other traditional bands of WV2, such as near-infrared-1 and near-infrared-2 band. Furthermore, the estimated LAI values vary significantly across different mangrove species. The study demonstrates the utility of VIs of WV2 imagery and the selected machine-learning algorithms in developing LAI models in mangrove forests. The results indicate that the red-edge band of WV2 imagery can help alleviate the saturation problem and improve the accuracy of LAI estimation in a mangrove area. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
12. Mapping the Abundance of Multipurpose Agroforestry Faidherbia albida Trees in Senegal
- Author
-
Tingting Lu, Martin Brandt, Xiaoye Tong, Pierre Hiernaux, Louise Leroux, Babacar Ndao, and Rasmus Fensholt
- Subjects
F40 - Écologie végétale ,SOIL CARBON SEQUESTRATION ,Télédétection ,Distribution géographique ,WORLDVIEW-2 IMAGERY ,Science ,CONSERVATION ,TIME-SERIES ,ECOSYSTEM SERVICES ,multi-layer perception ,savanna ,species distribution model ,Agroforesterie ,LIDAR ,Cartographie de l'occupation du sol ,systèmes agroforestiers ,Faidherbia albida ,Modélisation environnementale ,RANDOM FOREST ,OLD PEANUT BASIN ,F70 - Taxonomie végétale et phytogéographie ,SPECIES CLASSIFICATION ,MANAGED NATURAL REGENERATION ,General Earth and Planetary Sciences ,U30 - Méthodes de recherche ,Phénologie - Abstract
Multi-purpose Faidherbia albida trees represent a vital component of agroforestry parklands in West Africa as they provide resources (fodder for livestock, fruits and firewood) and support water lifting and nutrient recycling for cropping. Faidherbia albida trees are characterized by their inverse phenology, growing leaf flowers and pods during the dry season, thereby providing fodder and shedding leaves during the wet season, which minimizes competition with pastures and crops for resources. Multi-spectral and multi-temporal satellite systems and novel computational methods open new doors for classifying single trees and identifying species. This study used a Multi-Layer Perception feedforward artificial neural network to classify pixels covered by Faidherbia albida canopies from Sentinel-2 time series in Senegal, West Africa. To better discriminate the Faidherbia albida signal from the background, monthly images from vegetation indices were used to form relevant variables for the model. We found that NDI54/NDVI from the period covering onset of leaf senescence (February) until end of senescence (leaf-off in June) to be the most important, resulting in a high precision and recall rate of 0.91 and 0.85. We compared our result with a potential Faidherbia albida occurrence map derived by empirical modelling of the species ecology, which deviates notably from the actual species occurrence mapped by this study. We have shown that even small differences in dry season leaf phenology can be used to distinguish tree species. The Faidherbia albida distribution maps, as provided here, will be key in managing farmlands in drylands, helping to optimize economic and ecological services from both tree and crop products.
- Published
- 2022
- Full Text
- View/download PDF
13. Optimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal, South Africa.
- Author
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Lottering, Romano and Mutanga, Onisimo
- Subjects
- *
FOREST plants , *WORLDVIEW , *PLANT species , *LEAST squares - Abstract
The objective of this study was to identify an appropriate spatial resolution for discriminating forest vegetation at subspecies level. WorldView-2 imagery was progressively resampled to coarser spatial resolutions. At a compartment level, 30 × 30-m subsets were generated across forest compartments to represent the five forest subspecies investigated in this study. From the centre of each subset, the spatial resolution of the original WorldView-2 image was resampled from 6 to 34-m, with increments of 4-m. The variance was then calculated at every resampled spatial resolution using each of the eight WorldView-2 bands. Based on the sampling theorem, the 3-m spatial resolution provided an appropriate resolution for all subspecies investigated. The WorldView-2 image was subsequently classified using the partial least squares linear discriminant analysis algorithm and the appropriate spatial resolution. An overall classification accuracy of 90% was established with an allocation disagreement of 9 and a quantity disagreement of 1. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
14. The Effect of Sunglint on Benthic Habitats Mapping in Pari Island Using Worldview-2 Imagery.
- Author
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Anggoro, Ari, Siregar, Vincentius P., and Agus, Syamsul B.
- Subjects
BENTHIC ecology ,WATER ,SUNSHINE ,CORAL reefs & islands ,SAND - Abstract
Sunglint is a specular reflection of light from water surfaces that may cause misclassification and poor accuracy for benthic habitats mapping. The aim of this research was to investigate sunglint intensity for each benthic habitats and compared the accuracy result before and after sunglint correction from worldview-2 imagery. Hedley method and analysis coefficient of variation (COV) was used to estimate and remove the glint radiance component, while Mahalanobis distance was used to classify before and after sunglint correction from those imagery. The result showed that the average of sunglint intensity on benthic habitats was 38,9%. The highest sunglint intensity effect found in coral reef class (44%) and the lowest one in sand class (34%). The overall accuracy before and after sunglint correction were 53% and 60%. Sunglint correction well to do in reducing the effect of sunglint and increase the overall accuracy till 7%. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
15. Integrating WorldView-2 imagery and terrestrial LiDAR point clouds to extract dyke swarm geometry: Implications for magma emplacement mechanisms.
- Author
-
Ni, Nina, Chen, Ninghua, Chen, Jianyu, and Liu, Mingliang
- Subjects
- *
PARTICLE swarm optimization , *LASER based sensors , *IGNEOUS rocks , *ROCKFALL , *DIKES (Geology) , *SURFACE of the earth - Abstract
Dyke geometries are useful indicators of the palaeostress field during magma emplacement. In this paper, we present a multi-scale extraction method of dyke geometries by integrating WorldView-2 (WV2) imagery and terrestrial light detection and ranging (LiDAR) data. Color composite and fusion WV2 images with 0.5-m resolution were generated by using the Gramm–Schmidt Spectral Sharpening approach, which facilitates the discrimination of dyke swarms and provides the ability to measure the orientation, exposed length, and thickness of dykes in sub-horizontal topographic exposures. A terrestrial laser scanning survey was performed on a sub-vertical exposure of dykes to obtain LiDAR data with point spacing of ~ 0.02 m at 30 m. The LiDAR data were transformed to images for extracting dyke margins based on image segmentation, then the dyke attitudes, thicknesses, and irregularity of dyke margins were measured according to the points on dyke margins. This method was applied at Sijiao Island, Zhejiang, China where late Cretaceous mafic dyke swarms are widespread. The results show that integrating WV2 imagery and terrestrial LiDAR improves the accuracy, efficiency, and objectivity in determining dyke geometries in two and three dimensions. The ENE striking dykes are dominant, and intruded the host rock (mainly granite) with sub-vertical dips. Based on the aspect ratios of the dykes, the magmatic overpressure was estimated to be less than 11.5 MPa, corresponding to a magma chamber within 6.6 km in the lithosphere. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
16. Towards Monitoring of Nutrient Pollution in Coastal Lake Using Remote Sensing and Regression Analysis
- Author
-
Mortula, Maruf, Ali, Tarig, Bachir, Abdallah, Elaksher, Ahmed, Abouleish, Mohamed, Mortula, Maruf, Ali, Tarig, Bachir, Abdallah, Elaksher, Ahmed, and Abouleish, Mohamed
- Abstract
The last few decades have witnessed a tremendous increase in nutrient levels (phosphorus and nitrogen) in coastal water leading to excessive algal growth (Eutrophication). The presence of large amounts of algae turns the water’s color into green or red, in the case of algal blooms. Chlorophyll-a is often used as an indicator of algal biomass. Due to increased human activities surrounding Dubai creek, there have been eutrophication concerns given the levels of nutrients in that creek. This study aims to map chlorophyll-a in Dubai Creek from WorldView-2 imagery and explore the relationship between chlorophyll-a and other eutrophication indicators. A geometrically and atmospherically-corrected WorldView-2 image and in-situ data have been utilized to map chlorophyll-a in the creek. A spectral model, developed from theWorldView-2 multispectral image to monitor Chlorophyll-a concentration, yielded 0.82 R² with interpolated in-situ chlorophyll-a data. To address the time lag between the in-situ data and the image, L and sat 7 Enhanced Thematic Mapper Plus (ETM+) images were used to demonstrate the accuracy of the WorldView-2 model. The images, acquired on 20 May and 23 July 2012, were processed to extract chlorophyll-a band ratios (Band 4/Band 3) following the standard approach. Based on the availability, the 20 May image acquisition date is the closest to the middle of Quarter 2 (Q2) of the in-situ data (15 May). The 23 July 2012 image acquisition date is the closest to theWorldView-2 image date (24 July). Another model developed to highlight the relationship between spectral chlorophyll-a levels, and total nitrogen and orthophosphate levels, yielded 0.97 R², which indicates high agreement. Furthermore, the generated models were found to be useful in mapping chlorophyll-a, total nitrogen, and orthophosphate, without the need for costly in-situ data acquisition efforts., American University of Sharjah
- Published
- 2021
17. STATISTICAL BUILDING ROOF RECONSTRUCTION FROM WORLDVIEW-2 STEREO IMAGERY.
- Author
-
Partovi, T., Huang, H., Krauß, T., Mayer, H., and Reinartz, P.
- Subjects
ROOF maintenance & repair ,URBAN research ,REMOTE sensing ,LIDAR ,PHOTOGRAMMETRY ,COMPUTER vision - Abstract
3D building reconstruction from point clouds is an active research topic in remote sensing, photogrammetry and computer vision. Most of the prior research has been done on 3D building reconstruction from LiDAR data which means high resolution and dense data. The interest of this work is 3D building reconstruction from Digital Surface Models (DSM) of stereo image matching of space borne satellite data which cover larger areas than LiDAR datasets in one data acquisition step and can be used also for remote regions. The challenging problem is the noise of this data because of low resolution and matching errors. In this paper, a top-down and bottom-up method is developed to find building roof models which exhibit the optimum fit to the point clouds of the DSM. In the bottom up step of this hybrid method, the building mask and roof components such as ridge lines are extracted. In addition, in order to reduce the computational complexity and search space, roofs are classified to pitched and flat roofs as well. Ridge lines are utilized to estimate the roof primitives from a building library such as width, length, positions and orientation. Thereafter, a topdown approach based on Markov Chain Monte Carlo and simulated annealing is applied to optimize roof parameters in an iterative manner by stochastic sampling and minimizing the average of Euclidean distance between point cloud and model surface as fitness function. Experiments are performed on two areas of Munich city which include three roof types (hipped, gable and flat roofs). The results show the efficiency of this method in even for this type of noisy datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
18. Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests.
- Author
-
Oumar, Zakariyyaa and Mutanga, Onisimo
- Subjects
- *
TREE farms , *IMAGE databases , *HIGH resolution spectroscopy , *INSECT-plant relationships , *LEAST squares , *PLANT health - Abstract
Abstract: This study integrated environmental variables together with high spectral resolution WorldView-2 imagery to detect and map Thaumastocoris peregrinus damage in Eucalypt plantation forests in KwaZulu-Natal, South Africa. The WorldView-2 bands, vegetation indices and environmental variables were entered separately into PLS regression models to predict T. peregrinus damage. The datasets were then integrated to test the collective strength in predicting T. peregrinus damage. Important variables were identified by variable importance (VIP) scores and were re-entered into a PLS regression model. The VIP model was then extrapolated to map the severity of damage and predicted T. peregrinus damage with an R 2 value of 0.71 and a RMSE of 3.26% on an independent test dataset. The red edge and near-infrared bands of the WorldView-2 sensor together with the temperature dataset were identified as important variables in predicting T. peregrinus damage. The results indicate the potential of integrating WorldView-2 data and environmental variables to improve the mapping and monitoring of insect outbreaks in plantation forests. The result is critical for plantation health monitoring using a new sensor which contains important vegetation wavelengths. [Copyright &y& Elsevier]
- Published
- 2014
- Full Text
- View/download PDF
19. The Effect of Sunglint on Benthic Habitats Mapping in Pari Island Using Worldview-2 Imagery
- Author
-
Ari Anggoro, Syamsul Bahri Agus, and Vincentius P. Siregar
- Subjects
Mahalanobis distance ,010504 meteorology & atmospheric sciences ,0211 other engineering and technologies ,sunglint correction ,Sunglint ,02 engineering and technology ,01 natural sciences ,worldview-2 imagery ,Benthic habitat ,Pari Island ,Radiance ,General Earth and Planetary Sciences ,Environmental science ,benthic habitats ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,General Environmental Science ,Remote sensing - Abstract
Sunglint is a specular reflection of light from water surfaces that may cause misclassification and poor accuracy for benthic habitats mapping. The aim of this research was to investigate sunglint intensity for each benthic habitats and compared the accuracy result before and after sunglint correction from worldview-2 imagery. Hedley method and analysis coefficient of variation (COV) was used to estimate and remove the glint radiance component, while Mahalanobis distance was used to classify before and after sunglint correction from those imagery. The result showed that the average of sunglint intensity on benthic habitats was 38,9%. The highest sunglint intensity effect found in coral reef class (44%) and the lowest one in sand class (34%). The overall accuracy before and after sunglint correction were 53% and 60%. Sunglint correction well to do in reducing the effect of sunglint and increase the overall accuracy till 7%.
- Published
- 2016
- Full Text
- View/download PDF
20. Towards Monitoring of Nutrient Pollution in Coastal Lake Using Remote Sensing and Regression Analysis
- Author
-
Mohamed Abouleish, Maruf Mortula, Tarig Ali, Abdallah R Bachir, and Ahmed F. Elaksher
- Subjects
Chlorophyll a ,lcsh:Hydraulic engineering ,Geography, Planning and Development ,Multispectral image ,WorldView-2 imagery ,Aquatic Science ,Biochemistry ,Algal bloom ,chemistry.chemical_compound ,lcsh:Water supply for domestic and industrial purposes ,Nutrient ,nutrients ,lcsh:TC1-978 ,Water Science and Technology ,Hydrology ,lcsh:TD201-500 ,Biomass (ecology) ,Chlorophyll-a ,eutrophication ,GIS ,chemistry ,Thematic Mapper ,Nutrient pollution ,Environmental science ,Eutrophication - Abstract
The last few decades have witnessed a tremendous increase in nutrient levels (phosphorus and nitrogen) in coastal water leading to excessive algal growth (Eutrophication). The presence of large amounts of algae turns the water’s color into green or red, in the case of algal blooms. Chlorophyll-a is often used as an indicator of algal biomass. Due to increased human activities surrounding Dubai creek, there have been eutrophication concerns given the levels of nutrients in that creek. This study aims to map chlorophyll-a in Dubai Creek from WorldView-2 imagery and explore the relationship between chlorophyll-a and other eutrophication indicators. A geometrically- and atmospherically-corrected WorldView-2 image and in-situ data have been utilized to map chlorophyll-a in the creek. A spectral model, developed from the WorldView-2 multispectral image to monitor Chlorophyll-a concentration, yielded 0.82 R2 with interpolated in-situ chlorophyll-a data. To address the time lag between the in-situ data and the image, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were used to demonstrate the accuracy of the WorldView-2 model. The images, acquired on 20 May and 23 July 2012, were processed to extract chlorophyll-a band ratios (Band 4/Band 3) following the standard approach. Based on the availability, the 20 May image acquisition date is the closest to the middle of Quarter 2 (Q2) of the in-situ data (15 May). The 23 July 2012 image acquisition date is the closest to the WorldView-2 image date (24 July). Another model developed to highlight the relationship between spectral chlorophyll-a levels, and total nitrogen and orthophosphate levels, yielded 0.97 R2, which indicates high agreement. Furthermore, the generated models were found to be useful in mapping chlorophyll-a, total nitrogen, and orthophosphate, without the need for costly in-situ data acquisition efforts.
- Published
- 2020
21. STATISTICAL BUILDING ROOF RECONSTRUCTION FROM WORLDVIEW-2 STEREO IMAGERY
- Author
-
Tahmineh Partovi, Thomas Kraus, Peter Reinartz, Helmut Mayer, Hai Huang, Stilla, U., and Heipke, C.
- Subjects
lcsh:Applied optics. Photonics ,Point cloud ,lcsh:Technology ,Building Roof Reconstruction ,Worldview-2 Imagery ,symbols.namesake ,Computer vision ,Roof ,Remote sensing ,Photogrammetrie und Bildanalyse ,lcsh:T ,Orientation (computer vision) ,business.industry ,DEM ,lcsh:TA1501-1820 ,Markov chain Monte Carlo ,Euclidean distance ,Lidar ,Geography ,Photogrammetry ,lcsh:TA1-2040 ,Simulated annealing ,symbols ,Urban Area ,Artificial intelligence ,Statistical Approach ,lcsh:Engineering (General). Civil engineering (General) ,business - Abstract
3D building reconstruction from point clouds is an active research topic in remote sensing, photogrammetry and computer vision. Most of the prior research has been done on 3D building reconstruction from LiDAR data which means high resolution and dense data. The interest of this work is 3D building reconstruction from Digital Surface Models (DSM) of stereo image matching of space borne satellite data which cover larger areas than LiDAR datasets in one data acquisition step and can be used also for remote regions. The challenging problem is the noise of this data because of low resolution and matching errors. In this paper, a top-down and bottom-up method is developed to find building roof models which exhibit the optimum fit to the point clouds of the DSM. In the bottom up step of this hybrid method, the building mask and roof components such as ridge lines are extracted. In addition, in order to reduce the computational complexity and search space, roofs are classified to pitched and flat roofs as well. Ridge lines are utilized to estimate the roof primitives from a building library such as width, length, positions and orientation. Thereafter, a topdown approach based on Markov Chain Monte Carlo and simulated annealing is applied to optimize roof parameters in an iterative manner by stochastic sampling and minimizing the average of Euclidean distance between point cloud and model surface as fitness function. Experiments are performed on two areas of Munich city which include three roof types (hipped, gable and flat roofs). The results show the efficiency of this method in even for this type of noisy datasets.
- Published
- 2018
22. EVALUASI CITRA WORLDVIEW-2 UNTUK PENDUGAAN KEDALAMAN PERAIRAN DANGKAL PULAU KELAPA-HARAPAN MENGGUNAKAN ALGORITMA RASIO BAND
- Author
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Vincentius P. Siregar, Syamsul Bahri Agus, and Tarlan Subarno
- Subjects
Global and Planetary Change ,Logarithm ,Geography, Planning and Development ,WorldView-2 imagery ,shallow water ,lcsh:Geography. Anthropology. Recreation ,lcsh:G1-922 ,Water depth ,Waves and shallow water ,Geography ,lcsh:G ,water depth ,Earth and Planetary Sciences (miscellaneous) ,Satellite ,Computers in Earth Sciences ,Underwater ,Image resolution ,lcsh:Geography (General) ,Remote sensing - Abstract
Remote sensing technology is so advanced that recently produced satellite sensors with the capability to provide imagery options with very high spatial resolutions. One of the options is WorldView-2 that has 1.84 meter of spatial resolution. Besides, WorldView-2 also has at least five bands on visible rays. The capability of remote sensing for underwater detection through specific depths and the availability of its bands on visible rays give more appropriate options to apply logarithm bands ratio on shallow water depth estimation. This research is aimed at analyzing the capability of WorldView-2 imagery to estimate shallow water depth of Kelapa-Harapan islands by using bands ratio algorithm. There were six bands combination used in applying band 1 through band 4 of WorldView-2 imagery. The results have shown that the best combination of bands to estimate the shallow water depth in the study area is the ratio between band 1 and band 3 with the R2 value of 0.067 and the average bias of 0.66 m. The ratio between band 1 and band 4 gave the value of R2 as big as 0.55 of its regression to the field depth samples. Meanwhile, the other four bands combination ratios have shown very low correlations to the water depth in the field. © 2015 GJGP UNDIP. All rights reserved.
- Published
- 2015
23. Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms
- Author
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Shugong Wang, Kai Liu, Lin Liu, Zhi He, Soe W. Myint, Yuanhui Zhu, and Hongxing Liu
- Subjects
010504 meteorology & atmospheric sciences ,Mean squared error ,0211 other engineering and technologies ,WorldView-2 imagery ,Red edge ,02 engineering and technology ,01 natural sciences ,Cross-validation ,vegetation index ,leaf area index ,red-edge band ,variable importance ,mangrove forests ,machine learning ,Leaf area index ,lcsh:Science ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Mathematics ,Remote sensing ,Vegetation ,Regression ,Random forest ,General Earth and Planetary Sciences ,lcsh:Q ,Mangrove ,Algorithm - Abstract
To accurately estimate leaf area index (LAI) in mangrove areas, the selection of appropriate models and predictor variables is critical. However, there is a major challenge in quantifying and mapping LAI using multi-spectral sensors due to the saturation effects of traditional vegetation indices (VIs) for mangrove forests. WorldView-2 (WV2) imagery has proven to be effective to estimate LAI of grasslands and forests, but the sensitivity of its vegetation indices (VIs) has been uncertain for mangrove forests. Furthermore, the single model may exhibit certain randomness and instability in model calibration and estimation accuracy. Therefore, this study aims to explore the sensitivity of WV2 VIs for estimating mangrove LAI by comparing artificial neural network regression (ANNR), support vector regression (SVR) and random forest regression (RFR). The results suggest that the RFR algorithm yields the best results (RMSE = 0.45, 14.55% of the average LAI), followed by ANNR (RMSE = 0.49, 16.04% of the average LAI), and then SVR (RMSE = 0.51, 16.56% of the average LAI) algorithms using 5-fold cross validation (CV) using all VIs. Quantification of the variable importance shows that the VIs derived from the red-edge band consistently remain the most important contributor to LAI estimation. When the red-edge band-derived VIs are removed from the models, estimation accuracies measured in relative RMSE (RMSEr) decrease by 3.79%, 2.70% and 4.47% for ANNR, SVR and RFR models respectively. VIs derived from red-edge band also yield better accuracy compared with other traditional bands of WV2, such as near-infrared-1 and near-infrared-2 band. Furthermore, the estimated LAI values vary significantly across different mangrove species. The study demonstrates the utility of VIs of WV2 imagery and the selected machine-learning algorithms in developing LAI models in mangrove forests. The results indicate that the red-edge band of WV2 imagery can help alleviate the saturation problem and improve the accuracy of LAI estimation in a mangrove area.
- Published
- 2017
24. Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2 Imagery and a Hierarchical Cascade Convolutional Neural Network
- Author
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Yibo Huang, Ke Wang, Yaohua Wang, Wu Yang, Ziran Ye, Yongyong Fu, Xinyu Zheng, and Jinsong Deng
- Subjects
Marine conservation ,Computer science ,Science ,Feature vector ,marine aquaculture areas ,WorldView-2 imagery ,fully convolutional network (FCN) ,land-use and land-cover (LULC) mapping ,Aggregate (data warehouse) ,0211 other engineering and technologies ,02 engineering and technology ,computer.software_genre ,Convolutional neural network ,Support vector machine ,Cascade ,0202 electrical engineering, electronic engineering, information engineering ,General Earth and Planetary Sciences ,020201 artificial intelligence & image processing ,Data mining ,computer ,Encoder ,021101 geological & geomatics engineering ,Communication channel - Abstract
Marine aquaculture plays an important role in seafood supplement, economic development, and coastal ecosystem service provision. The precise delineation of marine aquaculture areas from high spatial resolution (HSR) imagery is vital for the sustainable development and management of coastal marine resources. However, various sizes and detailed structures of marine objects make it difficult for accurate mapping from HSR images by using conventional methods. Therefore, this study attempts to extract marine aquaculture areas by using an automatic labeling method based on the convolutional neural network (CNN), i.e., an end-to-end hierarchical cascade network (HCNet). Specifically, for marine objects of various sizes, we propose to improve the classification performance by utilizing multi-scale contextual information. Technically, based on the output of a CNN encoder, we employ atrous convolutions to capture multi-scale contextual information and aggregate them in a hierarchical cascade way. Meanwhile, for marine objects with detailed structures, we propose to refine the detailed information gradually by using a series of long-span connections with fine resolution features from the shallow layers. In addition, to decrease the semantic gaps between features in different levels, we propose to refine the feature space (i.e., channel and spatial dimensions) using an attention-based module. Experimental results show that our proposed HCNet can effectively identify and distinguish different kinds of marine aquaculture, with 98% of overall accuracy. It also achieves better classification performance compared with object-based support vector machine and state-of-the-art CNN-based methods, such as FCN-32s, U-Net, and DeeplabV2. Our developed method lays a solid foundation for the intelligent monitoring and management of coastal marine resources.
- Published
- 2019
25. Towards Monitoring of Nutrient Pollution in Coastal Lake Using Remote Sensing and Regression Analysis.
- Author
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Mortula, Maruf, Ali, Tarig, Bachir, Abdallah, Elaksher, Ahmed, and Abouleish, Mohamed
- Subjects
WATER pollution ,POLLUTION monitoring ,REMOTE sensing ,REGRESSION analysis ,COLOR of water ,CHLOROPHYLL in water ,PHOSPHORUS in water ,ALGAL blooms - Abstract
The last few decades have witnessed a tremendous increase in nutrient levels (phosphorus and nitrogen) in coastal water leading to excessive algal growth (Eutrophication). The presence of large amounts of algae turns the water's color into green or red, in the case of algal blooms. Chlorophyll-a is often used as an indicator of algal biomass. Due to increased human activities surrounding Dubai creek, there have been eutrophication concerns given the levels of nutrients in that creek. This study aims to map chlorophyll-a in Dubai Creek from WorldView-2 imagery and explore the relationship between chlorophyll-a and other eutrophication indicators. A geometrically- and atmospherically-corrected WorldView-2 image and in-situ data have been utilized to map chlorophyll-a in the creek. A spectral model, developed from the WorldView-2 multispectral image to monitor Chlorophyll-a concentration, yielded 0.82 R
2 with interpolated in-situ chlorophyll-a data. To address the time lag between the in-situ data and the image, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were used to demonstrate the accuracy of the WorldView-2 model. The images, acquired on 20 May and 23 July 2012, were processed to extract chlorophyll-a band ratios (Band 4/Band 3) following the standard approach. Based on the availability, the 20 May image acquisition date is the closest to the middle of Quarter 2 (Q2) of the in-situ data (15 May). The 23 July 2012 image acquisition date is the closest to the WorldView-2 image date (24 July). Another model developed to highlight the relationship between spectral chlorophyll-a levels, and total nitrogen and orthophosphate levels, yielded 0.97 R2 , which indicates high agreement. Furthermore, the generated models were found to be useful in mapping chlorophyll-a, total nitrogen, and orthophosphate, without the need for costly in-situ data acquisition efforts. [ABSTRACT FROM AUTHOR]- Published
- 2020
- Full Text
- View/download PDF
26. Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2 Imagery and a Hierarchical Cascade Convolutional Neural Network.
- Author
-
Fu, Yongyong, Ye, Ziran, Deng, Jinsong, Zheng, Xinyu, Huang, Yibo, Yang, Wu, Wang, Yaohua, and Wang, Ke
- Subjects
- *
MARICULTURE , *COASTAL zone management , *SUPPORT vector machines , *OFFSHORE structures , *COASTAL development - Abstract
Marine aquaculture plays an important role in seafood supplement, economic development, and coastal ecosystem service provision. The precise delineation of marine aquaculture areas from high spatial resolution (HSR) imagery is vital for the sustainable development and management of coastal marine resources. However, various sizes and detailed structures of marine objects make it difficult for accurate mapping from HSR images by using conventional methods. Therefore, this study attempts to extract marine aquaculture areas by using an automatic labeling method based on the convolutional neural network (CNN), i.e., an end-to-end hierarchical cascade network (HCNet). Specifically, for marine objects of various sizes, we propose to improve the classification performance by utilizing multi-scale contextual information. Technically, based on the output of a CNN encoder, we employ atrous convolutions to capture multi-scale contextual information and aggregate them in a hierarchical cascade way. Meanwhile, for marine objects with detailed structures, we propose to refine the detailed information gradually by using a series of long-span connections with fine resolution features from the shallow layers. In addition, to decrease the semantic gaps between features in different levels, we propose to refine the feature space (i.e., channel and spatial dimensions) using an attention-based module. Experimental results show that our proposed HCNet can effectively identify and distinguish different kinds of marine aquaculture, with 98% of overall accuracy. It also achieves better classification performance compared with object-based support vector machine and state-of-the-art CNN-based methods, such as FCN-32s, U-Net, and DeeplabV2. Our developed method lays a solid foundation for the intelligent monitoring and management of coastal marine resources. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
27. An object-based method for mapping ephemeral river areas from worldview-2 satellite data
- Author
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Benedetto Figorito, Gabriella Balacco, Umberto Fratino, and Eufemia Tarantino
- Subjects
geography.geographical_feature_category ,Ephemeral river areas ,Object-based classification ,Worldview-2 imagery ,Flood myth ,Process (engineering) ,Ephemeral key ,Drainage basin ,Land cover ,Field (geography) ,Water scarcity ,Water resources ,Geography ,Remote sensing - Abstract
Continuous monitoring of river basins has become a significant requirement of our times. Due to increasing water scarcity and unprecedented flood calamities, assessing existing water resources and gathering timely information on water increase are nowadays essential to develop suitable strategies in water resources management. Hydrological models are being studied to increase hydrological process understanding and to support decision making in this field. River basin management models typically operate on wide territories and, given the complexity of most river basins, they are based on semi-empirical lumped parameterizations of hydrological processes. To overcome the uncertainties inherent in such models and achieve acceptable model performance, calibration techniques are indispensable. Remote sensing and satellite-based data with high temporal resolution have the potential to fill such critical information gaps. With its nine spectral bands and very high resolutions (spectral and radiometric) WorldView-2 satellite sensor (WV-2) can provide new insights in the on-going debate comparing object-oriented and spectral-based classifications for the highest accuracy. This paper proposes an efficient object-based method for land cover mapping from Worldview-2 imagery in order to assess its potentiality in acquiring detailed basic information on an ephemeral river area ( Lama di Castellaneta , Taranto, Italy), to support further studies in the field of hydrological processes modeling. The approach suggested was evaluated by estimating classification accuracy.
- Published
- 2012
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