WorldView-2 Imagery Research Articles

Leveraging High-Resolution Satellite Imagery and Gradient Boosting for Invasive Weed Mapping

An introduced pasture grass ( Andropogon gayanus - gamba grass) is spreading through the tropical savannas of northern Australia, with detrimental ecosystem consequences including increased fire intensity. In order to monitor and manage the spread of gamba grass, a scalable solution for mapping its distribution over large areas is required. Recent developments in machine learning have proven useful for distinguishing vegetation types in satellite imagery in an automated manner. In this study, we collected field data for supervised learning of very high-resolution (0.3 m) WorldView-3 satellite imagery and tuned the hyperparameters of an extreme gradient boosting classifier to produce a viable solution for detecting the probability of gamba grass presence. To evaluate the performance of WorldView-3 imagery in discriminating gamba grass, we tested the utility of predictors derived from: 1) spectral bands; 2) textural features; 3) spectral indices; and 4) all predictors combined. Our results suggest that gamba grass presence can be mapped from space with an accuracy of up to 91% under optimal environmental conditions.

SealNet: A fully-automated pack-ice seal detection pipeline for sub-meter satellite imagery

Antarctic pack-ice seals, a group of four species of true seals (Phocidae), play a pivotal role in the Southern Ocean foodweb as wide-ranging predators of Antarctic krill (Euphausia superba). Due to their circumpolar distribution and the remoteness and vastness of their habitat, little is known about their population sizes. Estimating pack-ice seal population sizes and trends is key to understanding how the Southern Ocean ecosystem will react to threats such as climate change driven sea ice loss and krill fishing. We present a functional pack-ice seal detection pipeline using Worldview-3 imagery and a Convolutional Neural Network that counts and locates seal centroids. We propose a new CNN architecture that detects objects by combining semantic segmentation heatmaps with binary classification and counting by regression. Our pipeline locates over 30% of seals, when compared to consensus counts from human experts, and reduces the time required for seal detection by 95% (assuming just a single GPU). While larger training sets and continued algorithm development will no doubt improve classification accuracy, our pipeline, which can be easily adapted for other large-bodied animals visible in sub-meter satellite imagery, demonstrates the potential for machine learning to vastly expand our capacity for regular pack-ice seal surveys and, in doing so, will contribute to ongoing international efforts to monitor pack-ice seals.

Fine Land Cover Classification in an Open Pit Mining Area Using Optimized Support Vector Machine and WorldView-3 Imagery

Fine land cover classification in an open pit mining area (LCCOM) is essential in analyzing the terrestrial environment. However, researchers have been focusing on obtaining coarse LCCOM while using high spatial resolution remote sensing data and machine learning algorithms. Although support vector machines (SVM) have been successfully used in the remote sensing community, achieving a high classification accuracy of fine LCCOM using SVM remains difficult because of two factors. One is the lack of significant features for efficiently describing unique terrestrial characteristics of open pit mining areas and another is the lack of an optimized strategy to obtain suitable SVM parameters. This study attempted to address these two issues. Firstly, a novel carbonate index that was based on WorldView-3 was proposed and introduced into the used feature set. Additionally, three optimization methods—genetic algorithm (GA), k-fold cross validation (CV), and particle swarm optimization (PSO)—were used for obtaining the optimization parameters of SVM. The results show that the carbonate index was effective for distinguishing the dumping ground from other open pit mining lands. Furthermore, the three optimization methods could significantly increase the overall classification accuracy (OA) of the fine LCCOM by 8.40%. CV significantly outperformed GA and PSO, and GA performed slightly better than PSO. CV was more suitable for most of the fine land cover types of crop land, and PSO for road and open pit mining lands. The results of an independent test set revealed that the optimized SVM models achieved significant improvements, with an average of 8.29%. Overall, the proposed strategy was effective for fine LCCOM.

Detecting and Mapping Slag Heaps at Ancient Copper Production Sites in Oman

This study presents a new approach for detection and mapping of ancient slag heaps using 16-band multispectral satellite imagery. Understanding the distribution of slag (a byproduct of metal production) is of great importance for understanding how metallurgy shaped long-term economic and political change across the ancient Near East. This study presents results of slag mapping in Oman using WorldView-3 (WV3) satellite imagery. A semi-automated target detection routine using a mixed tuned matched filtering (MTMF) algorithm with scene-derived spectral signatures was applied to 16-band WV3 imagery. Associated field mapping at two copper production sites indicates that WorldView-3 satellite data can differentiate slag and background materials with a relatively high (>90%) overall accuracy. Although this method shows promise for future initiatives to discover and map slag deposits, difficulties in dark object spectral differentiation and underestimation of total slag coverage substantially limit its use. Resulting lower estimations of combined user’s (61%) and producer’s (45%) accuracies contextualize these limitations for slag specific classification. Accordingly, we describe potential approaches to address these challenges in future studies. As sites of ancient metallurgy in Oman are often located in areas of modern exploration and mining, detection and mapping of ancient slag heaps via satellite imagery can be helpful for discovery and monitoring of vulnerable cultural heritage sites.

Photogrammetric Bathymetry for the Canadian Arctic

Remote sensing is becoming common in the estimation of bathymetry for navigational charting through a process known as Satellite Derived Bathymetry (SDB). Most SDB techniques currently used by hydrographic offices employ an empirical approach, requiring the use of in-situ data to calibrate a relationship between spectral information and coincident depths. This article reports on a multi-site test of an alternative SDB method which uses photogrammetry to extract depths from stereo WorldView-2 imagery. In areas with heterogeneous seafloors, the empirical approach faces difficulties in establishing the relationship between colour and depth, while the photogrammetric approach uses the contrasting seafloor features for triangulation. Additionally, the photogrammetric method may be applied in areas lacking previous survey data. Five study areas in Nunavut, Canada were selected to test the robustness of the method in different environments and under different imaging conditions. Study areas were (with resulting RMSE/Bias given in metres) Coral Harbour (0.84/−0.47), Cambridge Bay (1.16/−0.15), Queen Maud Gulf (0.97/0.06), Arviat (0.99/−0.009), and Frobisher Bay, where extraction largely failed due to environmental conditions. Accuracies demonstrated here are similar to those seen using the empirical approach, suggesting that these two methods may be used in conjunction, each applied to regions where they are better suited.

Remote sensing and GIS techniques for reconstructing the military fort system on the Roman boundary (Tunisian section) and identifying archaeological sites

Southern Tunisia is one of the most significant areas of historical and archaeological interest as, since the Roman period, it has played a key role as a land bridge controlling the passage from the Saharan plain to plateaus and to the north of the mountain range between Chebika and Metlaoui. Because of the difficult geography, the investigation of ancient sites in this region is not easy and, therefore, satellite-based investigation can play an important role in the detection and documentation of archaeological sites. In this study, we combined high-resolution remote sensing (RS) imagery with in situ investigations to assess the suspected archaeological sites detected using satellite data and historical documentation. The suspected sites were confirmed according to the geometric features of the sites as they appeared in the remotely sensed imagery these sites had not been detected during earlier field investigations. In particular, we aimed to use 1-m spatial resolution WorldView-2 (WV2) imagery of the Wadi El-MelahValley (WMV) in Gafsa (also named Capsa). The satellite data were processed using statistical analysis and unsupervised classification.WV2 satellite imagery of WMV, Southwestern Tunisia, was processed within ArcGIS 10.2 to identify spatial features linked to the sites. The study outlines three different feature-extraction methods (GIS-based, unsupervised classification-based and statistical analysis-based). The satellite-based analysis and archaeological records were evaluated by RS and GIS prospecting in the area of the field survey to confirm the new discoveries. The results of remotely sensed data combined with field survey enabled us to reconstruct the military defense system consisting of is a linear defensive structure (limes) and forts related to the Imperial Roman period. Taken together, the results of our investigations provide new insights into some important sections of the Roman limes in the southern part of the empire. Other concealed remains are still to be found and explored.

Modeling stand parameters for Pinus brutia (Ten.) using airborne LiDAR data: a case study in Bergama

Traditional field measurement methods are usually time-consuming and costly. With recent developments in remote sensing, precise estimation of some key forest parameters is becoming a close reality. One of the robust technologies being utilized for this aim is light detection and ranging (LiDAR). We used LiDAR to derive tree height and canopy density metrics using pixel values, texture features, and vegetation indices obtained from WorldView-3 imagery to estimate volume per hectare, mean height, dominant height, and number of trees per hectare. A total of 58 sample plots, predominantly composed of Pinus brutia , which is one of the most abundant tree species in Turkey, were analyzed. The adjusted R 2 values obtained by the best regression models, using LiDAR-derived metrics alone, were 0.66, 0.73, 0.83, and 0.83 for the volume per hectare, number of trees per hectare, and average and dominant heights, respectively. After integrating LiDAR-derived metrics with WorldView-3 imagery band values and subsequently with vegetation indices, higher adjusted R 2 values of 0.70 and 0.77, respectively, were obtained for the volume per hectare. In contrast, incorporating texture features besides other parameters had no positive effect on the accuracy of estimation.

Using remote sensing to detect whale strandings in remote areas: The case of sei whales mass mortality in Chilean Patagonia.

We test the ability of Very High Resolution satellite (VHR) imagery to detect stranded whales using both manual and automated methods. We use the 2015 mass mortality event in the Gulf of Penas locality, central Patagonia, Chile, as an initial case study. This event was the largest known mass mortality of baleen whales, with at least 343 whales, mainly sei whales (Balaenoptera borealis), documented as stranding. However, even with such a large number of whales, due to the remote location of the gulf the strandings went unrecorded for several weeks. Aerial and boat surveys of the area were conducted two to four months after the mortality event. In this study we use 50cm resolution WorldView2 imagery to identify and count strandings from two archival images acquired just after the stranding event and two months before the aerial and ground surveys, and to test manual and automated methods of detecting stranded whales. Our findings show that whales are easily detected manually in the images but due to the heterogeneous colouration of decomposing whales, spectral indices are unsuitable for automatic detection. Our satellite counts suggest that, at the time the satellite images were taken, more whales were stranded than recorded in the aerial survey, possibly due to the non-comprehensive coverage of the aerial survey or movement of the carcases between survey acquisition. With even higher resolution imagery now available, satellite imagery may be a cost effective alternative to aerial surveys for future assessment of the extent of mass whale stranding events, especially in remote and inaccessible areas.

Detection of Archaeological Looting from Space: Methods, Achievements and Challenges

Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment.

Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%. (2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R2 of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha−1 vs. 38.95 Mg ha−1. However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests.

Random forest classification of Callicarpa nudiflora from WorldView-3 imagery based on optimized feature space

Taking the Xiushui township of Baisha county in Hainan province as the research area,the random forest algorithm with obvious advantages in feature selection and classification extraction was used to extract the information of the Callicarpa nudiflora planting in the study area. Firstly,four kinds of different characteristic variables were generated based on World View-3 data,including spectral features,principal component features,vegetation index and texture features. Secondly,the spatial distribution of the C. nudiflora in the study area was extracted by remote sensing by random forest classification algorithm. Finally,the feature space of the random forest classification algorithm was optimized based on the feature importance to obtain the best random forest classification results,and this result is compared with the classification result of the random forest algorithm of the unoptimized feature space. The results showed that:①The overall accuracy of the C. nudiflora extracted by World View-3 image was 89. 97%,and the Kappa coefficient was 0. 84,which indicates that the random forest algorithm had higher classification accuracy and better applicability in Hainan C. nudiflora recognition.② The overall accuracy of extracting C. nudiflora with the dimension reduction feature was 90. 4,and the Kappa coefficient was 0. 85,which indicates that the random forest algorithm can effectively select features. At the same time as the feature variable data mining,the precision of the information extraction of the C. nudiflora was still guaranteed,and the operation efficiency was improved. This study provides a new idea,method and technical means for information extraction of cultivated medicinal plant resources in terms of feature selection and method selection.

Rapid estimation of bathymetry from multispectral imagery without in situ bathymetry data.

Optimization-based semi-analytical methods (OSMs) and empirical methods (EMs) have been developed to derive bathymetry maps from satellite-based multispectral data of coral reefs, allowing for the management, monitoring, and protection of coral reefs. However, OSMs are often criticized due to the time-consuming requirements of iterative computations, yet they are praised for working without the need for in situ bathymetry data. EMs are praised for their time-saving characteristics and criticized for their need for in situ measurements. To estimate the water depth from multispectral data quickly without in situ bathymetry data, we provide a new EM that combines our previously developed OSM called the unmixing-based multispectral optimization process exemplar method (UMOPE) and an EM called Stumpf's ratio method (SRM). In the new method, reflectance values from a small number of sampled pixels and the corresponding water depths estimated by UMOPE are used to determine the regression parameters for SRM. Thus, SRM determines the upper limit of accuracy for the new method, and UMOPE determines the possibility of reaching the upper limit. The new method was evaluated using three types of imagery of Xisha Islands, namely, WorldView-2 imagery with three traditional visible bands (WV-2a), Landsat 8 imagery with four visible bands, and WV-2 imagery with six visible bands (WV-2b). The results show that the new method can perform as well as SRM for Landsat 8 data and WV-2b data with similar root mean square error values at different depths. The lack of a coastal band in WV-2a imagery may cause large errors for the new method in deep water regions, especially when the water-leaving reflectance is noise perturbed. We found that even though the depths estimated by UMOPE are not error free at different ranges of water depth, if the regression line between the depths estimated by UMOPE and the measured depths is near the 1:1 line, the new method can perform as well as SRM. The new method may facilitate the rapid estimation of bathymetry from free Landsat 8 data of optically shallow waters around the world without in situ bathymetry data.

Comparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery

Urban green space (UGS) plays a vital role in maintaining the ecological balance of a city and in ensuring healthy living of the city inhabitants. It is generally suggested that one-third of the city should be covered by green and to ensure this, the city administrators must have an accurate map of the existing UGS. Such a map would be useful to visualize the distribution of the existing green cover and also to find out the areas that can possibly be converted to UGS. Reported studies on UGS mapping have mostly used medium and high resolution images such as Landsat-TM, ETM+, Sentinel-2A, IKONOS, etc. However, studies on the use of very high resolution images for UGS extraction are very limited. The present study is a first attempt in utilizing the very high resolution Worldview-3 image for UGS extraction. Performance of different classification methods such as unsupervised, supervised, object based and normalized difference vegetation index (NDVI) were compared using the pan sharpened Worldview-3 image covering part of New Delhi in India. It was found that the unsupervised classification followed by manual recoding method showed superior performance with overall accuracy (OA) of 99% and κ coefficient of 0.98. Also, the OA achieved in the present study is the highest when compared to other reported studies on UGS extraction. The map of UGS revealed that almost 40% of the study area is covered by green which is more than the recommended value of 33% (one-third). In order to check the universality of the unsupervised classification approach in extracting UGS, Worldview-3 image covering Rio in Brazil was tested. It was found that an OA of 98% and κ coefficient of 0.95 were obtained which clearly indicate that the proposed approach would work very well in extracting UGS from any Worldview-3 imagery.

Semiautomated Detection and Mapping of Vegetation Distribution in the Antarctic Environment Using Spatial-Spectral Characteristics of WorldView-2 Imagery

Effective monitoring of changes in the geographic distribution of cryospheric vegetation requires high-resolution and accurate baseline maps. The rationale of the present study is to compare multiple feature extraction approaches to remotely mapping vegetation in Antarctica, assessing which give the greatest accuracy and reproducibility relative to those currently available. This study provides precise, high-resolution, and refined baseline information on vegetation distribution as is required to enable future spatiotemporal change analyses of the vegetation in Antarctica. We designed and implemented a semiautomated customized normalized difference vegetation index (NDVI) approach for extracting cryospheric vegetation by incorporating very high resolution (VHR) 8-band WorldView-2 (WV-2) satellite data. The viability of state-of-the-art target detection, spectral processing/matching, and pixel-wise supervised classification feature extraction techniques are compared with the customized NDVI approach devised in this study. An extensive quantitative and comparative assessment was made by evaluating four semiautomatic feature extraction approaches consisting of 16 feature extraction standalone methods (four customized NDVI plus 12 existing methods) for mapping vegetation on Fisher Island and Stornes Peninsula in the Larsemann Hills, situated on continental east Antarctica. The results indicated that the customized NDVI approach achieved superior performance (average bias error ranged from ~6.44 ± 1.34% to ~11.55 ± 1.34%) and highest statistical stability in terms of performance when compared with existing feature extraction approaches. Overall, the accuracy analysis of the vegetation mapping relative to manually digitized reference data (supplemented by validation with ground truthing) indicated that the 16 semi-automatic mapping methods representing four general feature extraction approaches extracted vegetated area from Fisher Island and Stornes Peninsula totalling between 2.38 and 3.72 km2 (2.85 ± 0.10 km2 on average) with bias values ranging from 3.49 to 31.39% (average 12.81 ± 1.88%) and average root mean square error (RMSE) of 0.41 km2 (14.73 ± 1.88%). Further, the robustness of the analyses and results were endorsed by a cross-validation experiment conducted to map vegetation from the Schirmacher Oasis, East Antarctica. Based on the robust comparative analysis of these 16 methods, vegetation maps of the Larsemann Hills and Schirmacher Oasis were derived by ensemble merging of the five top-performing methods (Mixture Tuned Matched Filtering, Matched Filtering, Matched Filtering/Spectral Angle Mapper Ratio, NDVI-2, and NDVI-4). This study is the first of its kind to detect and map sparse and isolated vegetated patches (with smallest area of 0.25 m2) in East Antarctica using VHR data and to use ensemble merging of feature extraction methods, and provides access to an important indicator for environmental change.

Mapping Crop Residue by Combining Landsat and WorldView-3 Satellite Imagery

A unique, multi-tiered approach was applied to map crop residue cover on the Eastern Shore of the Chesapeake Bay, United States. Field measurements of crop residue cover were used to calibrate residue mapping using shortwave infrared (SWIR) indices derived from WorldView-3 imagery for a 12-km × 12-km footprint. The resulting map was then used to calibrate and subsequently classify crop residue mapping using Landsat imagery at a larger spatial resolution and extent. This manuscript describes how the method was applied and presents results in the form of crop residue cover maps, validation statistics, and quantification of conservation tillage implementation in the agricultural landscape. Overall accuracy for maps derived from Landsat 7 and Landsat 8 were comparable at roughly 92% (+/− 10%). Tillage class-specific accuracy was also strong and ranged from 75% to 99%. The approach, which employed a 12-band image stack of six tillage spectral indices and six individual Landsat bands, was shown to be adaptable to variable soil moisture conditions—under dry conditions (Landsat 7, 14 May 2015) the majority of predictive power was attributed to SWIR indices, and under wet conditions (Landsat 8, 22 May 2015) single band reflectance values were more effective at explaining variability in residue cover. Summary statistics of resulting tillage class occurrence matched closely with conservation tillage implementation totals reported by Maryland and Delaware to the Chesapeake Bay Program. This hybrid method combining WorldView-3 and Landsat imagery sources shows promise for monitoring progress in the adoption of conservation tillage practices and for describing crop residue outcomes associated with a variety of agricultural management practices.

Determining Effective Meter-Scale Image Data and Spectral Vegetation Indices for Tropical Forest Tree Species Differentiation

We evaluate several high-resolution remote sensing data products derived from WorldView-3 satellite imagery to determine which product or product grouping would be most applicable for identifying and mapping tropical tree species. The study site, La Selva Biological Station in Costa Rica, provides the infrastructure to facilitate collection of proper ground-truth data for image validation. An objective statistical analysis demonstrated that the WorldView-3 imagery, after applying a series of spectral and illumination image corrections, was able to accurately identify selected tree species. Specifically, this study defines the image bands and image-derived spectral vegetation indices that are the most effective for tree species discrimination. We show that corrected absolute reflectivity values from the green, red, red edge, and near-infrared are good differentiators of tropical tree species crowns. We also evaluate 14 possible multiband vegetation indices and show that two new indices developed here, using WorldView-3 image bands, have the highest discriminatory power for tropical tree species. A combination of both individual band information and vegetation indices would significantly improve image-based classification of tropical forests.

Analysis of Flood Damage Assessment through WorldView-2, Quick Bird and Multispectral Satellite Imagery in Southern Punjab, Pakistan

Pakistan has faced numerous natural disasters like floods, earthquakes, landslides and environmental degradation which severely affects the Pakistan’s economy and results in various problems like causalities, diseases, water stress and severe damages (e.g., houses, public infrastructure and agricultural land erosion). There is a lack of systematic approaches to analyze pre and post damage assessment for estimation of exact loses and the total cost for rehabilitation of damaged infrastructure in an efficient way. There exist a variety of mechanisms but GIS based flood mapping is considered the most efficient to manage the flood situation. This study is focused on evaluation of flood affected areas especially in Punjab using WorldView-2, 8-band multi-spectral imagery by applying Remote Sensing (RS) and GIS techniques. The research area is comprised of Kot Addu and Muzaffargarh Districts in Punjab province of Pakistan that faced a catastrophic super flood of 2010. The WorldView-2, Quick Bird and multispectral satellite imagery are capable of making better decisions and assessment of flood effected area accurately. RS and GIS techniques can achieve the objectives and significant analyses through visual interpretations. These techniques are also used to identify the flood affected regions. The study site was examined by applying supervised classification on the basis of the training areas which were obtained during the field surveys in the study site. Supervised classification determines that 16900.96 Hectors of agriculture land was damaged while Sparse Riverine Forest had the area 44.52 hectors. The damaged built-up area was 1805.78 Hectors. RS and GIS techniques are efficient for flood mapping.

Analysis of Flood Damage Assessment through WorldView-2, Quick Bird and Multispectral Satellite Imagery in Southern Punjab, Pakistan

Pakistan has faced numerous natural disasters like floods, earthquakes, landslides and environmental degradation which severely affects the Pakistan’s economy and results in various problems like causalities, diseases, water stress and severe damages (e.g., houses, public infrastructure and agricultural land erosion). There is a lack of systematic approaches to analyze pre and post damage assessment for estimation of exact loses and the total cost for rehabilitation of damaged infrastructure in an efficient way. There exist a variety of mechanisms but GIS based flood mapping is considered the most efficient to manage the flood situation. This study is focused on evaluation of flood affected areas especially in Punjab using WorldView-2, 8-band multi-spectral imagery by applying Remote Sensing (RS) and GIS techniques. The research area is comprised of Kot Addu and Muzaffargarh Districts in Punjab province of Pakistan that faced a catastrophic super flood of 2010. The WorldView-2, Quick Bird and multispectral satellite imagery are capable of making better decisions and assessment of flood effected area accurately. RS and GIS techniques can achieve the objectives and significant analyses through visual interpretations. These techniques are also used to identify the flood affected regions. The study site was examined by applying supervised classification on the basis of the training areas which were obtained during the field surveys in the study site. Supervised classification determines that 16900.96 Hectors of agriculture land was damaged while Sparse Riverine Forest had the area 44.52 hectors. The damaged built-up area was 1805.78 Hectors. RS and GIS techniques are efficient for flood mapping.

A methodology based on GEOBIA and WorldView-3 imagery to derive vegetation indices at tree crown detail in olive orchards

Precision agriculture (PA) is becoming an essential practice for the implementation of sustainable agriculture that encompasses the efficient use of resources and a systematic crops monitoring. The increasing temporal and spatial resolution of satellite imagery, coupled with their availability and decreasing costs, create new possibilities for generating accurate datasets on different crops variables, more frequently available as ready-to-use data. The availability of very high-resolution (VHR) satellite imagery, such as the WorldView-3 (WV-3), leads to the advanced potential of satellite Remote Sensing (RS), becoming in the last decade one of the main data source in precision agriculture (PA). In the broad overview of these procedures, geographic object-based image classification (GEOBIA) techniques, gained broad interest as methods to produce geographic information in GIS-ready format. In this paper, methodologies for a semiautomatic process workflow is presented, providing olive tree crown detection in two different olive orchards in Calabria (Italy), collected by means of GEOBIA procedures, in order to investigate olive tree spectral behavior and the reliability of WW-3 derived vegetation indices (VIs). The semi-automated classification method, accomplished by imagery pre-processing steps, may constitute an operational processing chain for mapping and monitoring olive orchards at tree scale detail. Five VIs were investigated: Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index 2 (MSAVI 2), Normalized Difference Red Edge Vegetation Index (NDRE), Modified Chlorophyll Absorption Ratio Index Improved (MCARI2), and NDVI2. The obtained results were statistically tested and their accuracy assessed. Thematic accuracy ranges from 95.33% to 96% in both study areas with an overall tree detection rate of 96.8%. Statistical analysis showed that the major differences in spectral behavior, over different plots of the investigated olive orchards, are mainly due to the component of the red-infrared regions of the electromagnetic spectrum (EM), where the red-edge becomes important in assessing the state of general vigor. Moreover, the proposed methodology increases the possibility of detecting tree stress at earlier stages and the benefits of using satellite-based approaches in terms of: larger area coverage, less processing and operator interaction coupled with more spectral information, thus reducing the need to collect costly reference data sampling.

Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey

The aim of this research is to assess some stand parameters such as stand volume (SV), basal area (BA), number of trees (NT) and aboveground biomass (AGB) of pure Crimean pine forest stands in Turkey by using ground measurements and remote sensing techniques. For this purpose, 86 sample plots were collected from pure Crimean pine stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. The stand parameters of each sample area were estimated using the data obtained from the sample plots. Subsequently, we calculated the values of contrast (CON), correlation (COR), dissimilarity (DIS), entropy (ENT), homogeneity (HOM), mean (M), second moment (SM) and variance (VAR) from WorldView-2 imagery using a grey-level co-occurrence matrix method. Eight textural features and twelve different window sizes ranging from 3 × 3 to 25 × 25 were generated from blue, green, red and near-infrared bands of the WorldView-2 satellite image. For predicting the relationships between WorldView-2 textural features and stand parameters of each sample plot, regression models were developed by using multiple linear regression (MLR) analysis. Additionally, artificial neural networks (ANNs) based on the multilayer perceptron (MLP) and the radial basis function (RBF) architectures were trained by comparing various numbers of neurons and activation functions in their network types. The results showed that the MLR models had low the coefficient of determination (R 2) values (0.32 for SV, 0.35 for BA, 0.33 for NT and 0.34 for AGB), and the most of the ANNs models (MLP and RBF) were better than the regression models for estimating stand parameters. The ANNs model containing MLP and RBF for SV (R 2 = 0.40; R 2 = 0.56), for BA (R 2 = 0.34; R 2 = 0.51), for NT (R 2 = 0.34; R 2 = 0.37) and for AGB (R 2 = 0.34, R 2 = 0.57) were found the best results, respectively. Our results revealed that the ANNs models developed with WorldView-2 satellite image were beneficial to estimate stand parameters better than the MLR model in pure Crimean pine stands.