WorldView-2 Satellite Images Research Articles

Mapping Honeysuckle Distribution in Large Forests through Use of High Resolution Satellite Images – Case Study at Great Parks of Hamilton County, Ohio, USA

A non-native invasive shrub, named Amur honeysuckle, has caused various problems to the habitats of native plants and animals. Local governments, private landowners and non-profit groups spend numerous efforts on removing these species from forests. The first step to remove the species is to know their spatial distribution. The major objective of this study is to explore how to quickly map honeysuckle distribution by using high resolution satellite images. The case study is located in Great Parks of Hamilton County (GPHC), Ohio. A World-View2 satellite image acquired on Nov. 9th 2013 was classified into several land use types, including low density honeysuckle area, medium/high density honeysuckle area, grass/crop, bare soil/construction, and water/wet land. Half of the field data that we collected in Nov. 2013 and some high resolution aerial photos (with 4 inch spatial resolution) were used to validate the classification result. The study result demonstrates that the World-View2 image delineates honeysuckle distribution fairly well, in particular, for medium/high density honeysuckle areas. As a pilot program utilizing advanced geospatial analysis, this project will provide important information for understanding the status of wildlife habitats and for implementing site-specific management in parks and nature preserves.

Assessment of Image Fusion for Göktürk-2 and Worldview-2 Satellite Images

Bu çalışmada, Göktürk-2 keskinleştirme yapılmış görüntüsünün kırsal ve kentsel bölgelerdeki başarısının değerlendirilmesi amaçlanmıştır. Çalışma alanı olarak Trabzon ilinin Sürmene ilçesi seçilmiştir.Görüntü keskinleştirmesi için 5m konumsal çözünürlüklü dört adet Multispektral(MS) banda ve bir adet 2.5 m konumsal çözünürlüklü Pankromatik (PAN) banda sahip Göktürk-2 uydu görüntüsü ve 0.5 m konumsal çözünürlüklü Worldview-2 PAN bandı kullanılmıştır. İlk olarak, görüntülerin ön işlemesi için Göktürk-2 MS ve PAN görüntüleri, Worldview-2 PAN görüntüsüne göre piksel altı Root Mean Square (RMS) değeriyle geometrik olarak düzeltilmiştir. Ardından Göktürk-2 MS ile Göktürk-2 PAN görüntüleri, ve Göktürk-2 MS ile Worldview-2 PAN görüntülerinin sekiz farklı yöntem ile keskinleştirme işlemi gerçekleştirilmiştir. Görüntü keskinleştirme işlemi için Ehler's, Gram–Schimdt (GS), Hyperspherical Colour Sphere (HCS), High-Pass Filter (HPF), Intensity Hue Saturation (IHS), Principal Component (PC), Color Normalized (CN) ve Wavelet tabanlı yöntemleri kullanılmıştır. Keskinleştirme yapılmış görüntülerin kalitesi yaygın olarak kullanılan ve, spektral ve konumsal kaliteyi ölçen metrikler ile değerlendirilmiştir. Ayrıca doku bilgisini çıkaran Gabor filtresi de bu çalışmada metrik olarak kullanılmıştır. Bunun yanında keskinleştirme yapılmış görüntülerin değerlendirilmesi için bitki ve su indisleri çıkarılmıştır. Orijinal ve keskinleştirme yapılmış görüntülerin indisleri arasındaki korelâsyon katsayıları hesaplatılmıştır. Değerlendirme sonucuna göre, kentsel alan için CN , kırsal alanda ise HPF ve HCS görüntü keskinleştirme yöntemleri ile elde edilen sonuçların spektral ve konumsal anlamda daha az bozulmaya sahip olduğu görülmüştür. Genel olarak kentsel ve kırsal alan için CN yönteminin doku bilgisini daha iyi yansıttığı ve indisler yönünden de daha yüksek korelâsyona sahip olduğu gözlenmiştir.

Shalstab mathematical model and WorldView-2 satellite images to identification of landslide-susceptible areas

Natural hazards, occurring all over the world, may become a disaster when humans and nature interact. In Brazil, landslides triggered by heavy rainfall are the most common phenomenon that affects the population. Due to the economic and social losses and deaths, the identification and monitoring of risk areas are extremely important. Therefore, this study aims to identify the landslide-susceptible areas in Vila Albertina and Britador neighborhood, located in Campos do Jordao city in Sao Paulo state, Brazil. Using the Shalstab mathematical model, which analyzes the slope stability, and satellite images from WorldView-2 sensor with data mining techniques, it was identified the most susceptible areas for this phenomenon and the main characteristics of human occupation that might induce landslides. To achieve this goal, three scenarios were simulated for each neighborhood, changing the values of the geotechnical parameters, used as input on Shalstab. The results of susceptibility areas were consistent with the reality observed in these neighborhoods and the landslide scars corroborate with the assumption that anthropic changes induce landslides. The satellite image allowed the identification of different types of human interaction and its changes in steep slope areas.

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.

COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER

Abstract. The purpose of the study was to compare performance of the classification methods, that are Rule Based (RB) classifier and Support Vector Machine (SVM), of Planetscope and Worldview-3 satellite images in order to produce land use / cover thematic maps. Six classes, which are deep water, shallow water, vegetation, agricultural area, soil and saline soil, were considered. After performing the classification process, accuracy assessment was employed based on the error matrices. The results showed that, both of the classification methods and satellite data were adequate to classify the area. Besides, classification accuracy was improved when Worldview-3 satellite and SVM method were used. The classification accuracies of RB classification of Planetscope and Worldview-3 were %87 and %94 respectively and the classification accuracies of SVM classification of Planetscope and Worldview-3 were %93 and %96 respectively.

Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes

It has been argued that even centimeter-level resolution is needed for mapping vegetation patterns in spatially heterogeneous landscapes such as northern peatlands. However, there are few systematic tests for determining what kind of spatial resolution and data combinations are needed and what the differences in mapping accuracy are when different datasets are omitted or included. We conducted 78 different object-based supervised random forest classifications on a patterned fen and its surroundings in Kaamanen, northern Finland, using remotely sensed optical imagery, topography, and vegetation height datasets from different platforms (unmanned aerial vehicle (UAV), aerial, satellite) with spatial resolution ranging from 5 cm to 3 m. We compared differences in classification performance when we altered (1) classification and segmentation input data and features calculated from the data, or (2) the segmentation scale. We constructed training data with the help of transect-based field sampling and UAV imagery and tested classification accuracy using 412 field-surveyed vegetation plots. The most accurate classifications (75.7% overall accuracy) were obtained when we segmented a 5 cm resolution UAV image with a small segmentation scale and calculated features from all datasets. Classification accuracy was 2.2 percentage points (pp) lower with the most accurate aerial image (50 cm resolution) based classification, and 7.6 pp and 11.9 pp lower with the most accurate WorldView-2 (2 m resolution) and PlanetScope (3 m resolution) satellite image based classifications respectively. Classification accuracies were low (46.7–56.0%) when we used only spectral data from one dataset. The inclusion of gray-level co-occurrence matrix textural features increased classification accuracy by 0.4–12.1 pp and inclusion of multiple datasets by 8.2–25.0 pp. Segmentation scale had a minor effect on classification accuracy (2.5–7.3 pp difference between the finest and coarsest segmentation scale); however, both too small and large segmentation scale might lead to suboptimal classification. The differences in land cover type areal coverage were relatively small between classifications with multiple datasets, but if classifications included features from only one dataset, the differences were larger. We conclude that multiple different optical, topographical, and vegetation height datasets should be used when mapping vegetation in spatially heterogeneous landscapes, and that sub-meter resolution data (e.g. UAV or aerial) are necessary for the most accurate maps. Although UAV data is not essentially needed for classification, it is useful for training dataset construction and especially helpful in areas lacking other sub-meter resolution data.

Assessment of shoreline alterations using a Digital Shoreline Analysis System: a case study of changes in the Yeşilırmak Delta in northern Turkey from 1953 to 2017.

The aim of our study was to determine the erosion rates and shoreline changes in the Yeşilırmak Delta (Delta) between the oldest available date (1953) before construction of the large-scale dams on the Yeşilırmak River in northern Turkey and 2017. To clearly illustrate the impacts of the dams on the Delta, we investigated the relationship between the dams and shoreline changes. To compute the net shoreline changes and erosion rates over these 64years, we used a Digital Shoreline Analysis System. To digitize the shoreline changes at the mouth of the Yeşilırmak River, 1:25000-scaled topographic maps from 1957, panchromatic aerial photographs from 1962, a KH-9 Hexagon satellite image from 1980, an Ikonos satellite image from 2002, and a WorldView-2 satellite image from 2017 were used. The west and east sides of the river mouth were divided into six main groups and assessed. Each group included a time series for the shorelines. The groupings were based on the time series. For each group, the net shoreline change (m) was calculated using the net shoreline movement (NSM), and erosion rate (m/year) was calculated using the linear regression rate (LRR). According to NSM computations, net shoreline change varied from - 235.04 to 379.18m. The greatest evolutions in net shoreline change were observed for group I, with a minimum of - 1101.11m. According to LRR analysis, the calculated erosion rate varied from - 6.83 to 19.06m/year. The greatest changes in erosion rate were observed for group II, with a minimum of - 30.63m/year. There is a close relationship between the construction dates of the dams in the Yeşilırmak Basin and the shoreline changes in the Delta.

Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park

The main objectives of this paper are to demonstrate the results of an ensemble learning method based on prediction results of support vector machine and random forest methods using Bayesian average. In this study, we generated susceptibility maps of forest fire using supervised machine learning method (support vector machine—SVM) and its comparison with a versatile machine learning algorithm (random forest—RF) and their ensembles. In order to achieve this, first of all, a forest fire inventory map was constructed using Serbian historical forest fire database, Moderate Resolution Imaging Spectro radiometer (MODIS), Landsat 8 OLI and Worldview-2 satellite images, field surveys, and interpretation of aerial photo images. A total of 126 forest fire locations were identified and randomly divided by a random selection algorithm into two groups, including training (70%) and validation data sets (30%). Forest fire susceptibility maps were prepared using SVM, RF, and their ensemble models using the training dataset and 14 selected different conditioning factors. Finally, to explore the performance of the mentioned models we used the values for area under the curve (AUC) of receiver operating characteristics (ROC). The results depicted that the ensemble model had an AUC = 0.848, followed by the SVM model (AUC = 0.844), and RF model (AUC = 0.834). According to achieved AUC results, it can be deduced that SVM, RF, and their ensemble method had satisfactory performance. The study was applied in the Tara National Park (West Serbia), a region of about 191.7 sq. km distinguished by a very high forest density and a large number of forest fires.

High Resolution Satellite Images for Instantaneous Shoreline Extraction Using New Enhancement Algorithms

Knowledge of a territory is an essential element in any future planning action and in appropriate territorial and environmental requalification action planning. The current large-scale availability of satellite data, thanks to very high resolution images, provides professional users in the environmental, urban planning, engineering, and territorial government sectors, in general, with large amounts of useful data with which to monitor the territory and cultural heritage. Italy is experiencing environmental emergencies, and coastal erosion is one of the greatest threats, not only to the Italian heritage and economy, but also to human life. The aim of this paper is to find a rapid way of identifying the instantaneous shoreline. This possibility could help government institutions such as regions, civil protection, etc., to analyze large areas of land quickly. The focus is on instantaneous shoreline extraction in Ortona (CH, Italy), without considering tides, using WorldView-2 satellite images (50-cm resolution in panchromatic and 2 m in multispectral). In particular, the main purpose of this paper is to compare commercial software and ACM filters to test their effectiveness.

Segmentation of High Resolution Worldview-2 Satellite Images

This paper presents the segmentation technique used to segment the Worldview-2 high resolution satellite multispectral (MS) images. First the spectral features like Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Modified Soil Adjusted Vegetation Index (MSAVI) are considered to extract the spectral features from the MS image. Next the MS image is segmented by using the over segmented k-means algorithm with novel initialization (OSKNI) method. The proposed method performs well in terms of User’s accuracy (UA), Producer’s accuracy (PA) and overall segmentation accuracy (OVA) compared to the existing k-means algorithm.

Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis

Tropical forest conservation and management can significantly benefit from information about the spatial distribution of tree species. Very-high resolution (VHR) spaceborne platforms have been hailed as a promising technology for mapping tree species over broad spatial extents. WorldView-3, the most advanced VHR sensor, provides spectral data in 16 bands covering the visible to near-infrared (VNIR, 400–1040 nm) and shortwave-infrared (SWIR, 1210–2365 nm) wavelength ranges. It also collects images at unprecedented levels of details using a panchromatic band with 0.3-m of spatial resolution. However, the potential of WorldView-3 at its full spectral and spatial resolution for tropical tree species classification remains unknown. In this study, we performed a comprehensive assessment of WorldView-3 images acquired in the dry and wet seasons for tree species discrimination in tropical semi-deciduous forests. Classification experiments were performed using VNIR individually and combined with SWIR channels. To take advantage of the sub-metric resolution of the panchromatic band for classification, we applied an individual tree crown (ITC)-based approach that employed pan-sharpened VNIR bands and gray level co-occurrence matrix texture features. We determined whether the combination of images from the two annual seasons improves the classification accuracy. Finally, we investigated which plant traits influenced species detection. The new SWIR sensing capabilities of WorldView-3 increased the average producer’s accuracy up to 7.8%, by enabling the detection of non-photosynthetic vegetation within ITCs. The combination of VNIR bands from the two annual seasons did not improve the classification results when compared to the results obtained using images from each season individually. The use of VNIR bands at their original 1.2-m spatial resolution yielded average producer’s accuracies of 43.1 ± 3.1% and 38.8 ± 3% in the wet and dry seasons, respectively. The ITC-based approach improved the accuracy to 70 ± 8% in the wet and 68.4 ± 7.4% in the dry season. Texture analysis of the panchromatic band enabled the detection of species-specific differences in crown structure, which improved species detection. The use of texture analysis, pan-sharpening, and ITC delineation is a potential approach to perform tree species classification in tropical forests with WorldView-3 satellite images.

Impact of Differences in Land Management on Natural Vegetation in Semi-Dry Areas: The Case Study of the Adi Zaboy Watershed in the Kilite Awlaelo District, Eastern Tigray Region, Ethiopia

The search for a sustainable land management has become a universal issue. It is especially necessary to discuss sustainable land management and to secure a site with enough feed supply to improve the lives of the farmers in the Ethiopian Highlands. This research studied the Adi Zaboy watershed in Tigray in order to reveal the changes in land management, assess how the different forms of land management affected the vegetation through unsupervised classification and normalized difference vegetation index (NDVI) analysis with geographic information system (GIS) 10.5 using a WorldView-2 satellite image taken in September 2016 and field investigation, and consider how to allow both environmental preservation and sustainable use of feed resources. The land management types at the research site were classified as “seasonally-closed grazing land”, “prohibited grazing and protected forest land”, and “free grazing land”. On comparing the NDVI of each type of land management, it was found that the seasonally-closed grazing land makes it highly possible to secure and supply feed resources by limiting the grazing period. The expansion of the prohibited grazing and protected forest land is likely to tighten the restriction on the use of resources. Therefore, sustainable land management to secure feed resources may be possible by securing and actively using seasonally-closed grazing land, securing feed by a cut-and-carry, and using satellite images and GIS.

Ailanthus altissima mapping from multi-temporal very high resolution satellite images

This study presents the results of multi-seasonal WorldView-2 (WV-2) satellite images classification for the mapping of Ailanthus altissima (A. altissima), an invasive plant species thriving in a protected grassland area of Southern Italy. The technique used relied on a two-stage hybrid classification process: the first stage applied a knowledge-driven learning scheme to provide a land cover map (LC), including deciduous vegetation and other classes, without the need of reference training data; the second stage exploited a data-driven classification to: (i) discriminate pixels of the invasive species found within the deciduous vegetation layer of the LC map; (ii) determine the most favourable seasons for such recognition. In the second stage, when a traditional Maximum Likelihood classifier was used, the results obtained with multi-temporal July and October WV-2 images, showed an output Overall Accuracy (OA) value of ≈91%. To increase such a value, first a low-pass median filtering was used with a resulting OA of 99.2%, then, a Support Vector Machine classifier was applied obtaining the best A. altissima User’s Accuracy (UA) and OA values of 82.47% and 97.96%, respectively, without any filtering. When instead of the full multi-spectral bands set some spectral vegetation indices computed from the same months were used the UA and OA values decreased. The findings reported suggest that multi-temporal, very high resolution satellite imagery can be effective for A. altissima mapping, especially when airborne hyperspectral data are unavailable. Since training data are required only in the second stage to discriminate A. altissima from other deciduous plants, the use of the first stage LC mapping as pre-filter can render the hybrid technique proposed cost and time effective. Multi-temporal VHR data and the hybrid system suggested may offer new opportunities for invasive plant monitoring and follow up of management decision.

Crop classification with WorldView-2 imagery using Support Vector Machine comparing texture analysis approaches and grey relational analysis in Jianan Plain, Taiwan

ABSTRACTCrop production estimation is of crucial concern in Taiwan, and the government has invested much effort including employing manpower and technologies. Artificial intelligence or data mining technology have been successfully applied on land-cover recognition of remote-sensing imagery. Most studies are employing a pixel-based classification approach to generate the thematic map of land covers. Few studies consider the spatial correlation of adjacent pixels of the same category. Mixed pixel issues usually degrade the prediction accuracy according to previous studies. It is thus the main goal of the present study to explore the spatial effect of adjacent pixels on land-cover mapping. The study region is a WorldView-2 satellite image in Jianan Plain, Taiwan, taken in 2014. Support Vector Machine is used as the underlying classifier. In addition to the eight spectral band intensities, normalized difference vegetation index and grey-level co-occurrence matrix (GLCM) textures are included as ancillary attributes. Furthermore, grey relational analysis (GRA) is employed to assist in classifying croplands. The findings of this study can be summarized as follows: (1) GLCM texture information improves classification accuracy marginally and renders slightly better thematic map, (2) GRA is used to acquire the most important factors concerning discriminating land covers, (3) grey relational grade threshold, a metric designed through GRA, can be used to locate uncertain region of a specified crop, which is possibly caused by mixed pixels.

Assessing rehabilitation of managed mangrove ecosystems using high resolution remote sensing

Mangroves are valuable ecosystems for coastal protection, carbon sequestration and storage, and they provide habitat, refuge and rearing areas for many important marine species. To control mosquito outbreaks in coastal regions, mangroves were often impounded and managed using a variety of techniques that ranged from the application of insecticides to water level manipulation. Since continuous impounding had been shown to have negative effects on mangrove vegetation, other techniques have been used to manage hydrology in impoundments. A recent technique is called rotational impoundment management (RIM) and it involves flooding impoundments in summer and spring, the reproductive season of the mosquitos. In this study, we assessed the effects of 5 years of RIM management on mangrove vegetation in an impoundment on the east coast of Florida. We compared mangrove vegetation in the RIM impoundment with an adjacent impoundment that was not managed. We created a map of leaf area index (LAI) to assess vegetation productivity and its change in the two impoundments. We classified color-infrared aerial photographs from 2008 to 2010 and a WorldView-2 satellite image from 2014 to measure the extent of mangrove vegetation types and temporal changes in the two impoundments. We found a 38% increase in cover of dense mangrove vegetation after five years for the RIM-impounded area. Classification accuracy was around 80% for all imagery. The increased growth of plants and cover of dense mangroves in the RIM impoundment was corroborated by observed leaf area index values. Overall, the study demonstrates that vegetation in the RIM impoundment is becoming denser and in the near future will probably become similar to an impoundment that is open to tidal exchange or mangrove dominated areas that are not impounded.

Corresponding regions for shadow restoration in satellite high-resolution images

ABSTRACTHigh spatial resolution images available by satellites such as Ikonos, Quickbird, and WorldView-2 provide more information for remote sensing applications, such as object detection, classification, change detection, and object mapping. The presence of shadow reduces the amount of information that can be extracted and consequently makes these applications more difficult or even impossible. In this article, a shadow restoration approach for high-resolution satellite images is proposed. The approach detects the shadow area and segments the image into regions according to the land surface type. Then, shadow restoration is carried out for each region based on the degree of correspondence between shadow and neighbouring non-shadow regions. The proposed approach is applied to study areas from Ikonos and WorldView-2 satellite images. A comparison to the standard approaches for shadow restoration is performed, and an accuracy assessment is carried out by visual inspection and land-cover classification. The results show that the enhanced shadow regions using the proposed approach have better appearances and are highly compatible with their surrounding non-shadow regions. In addition, the overall accuracy is higher than those of the standard approaches.

The role of positional errors while interpolating soil organic carbon contents using satellite imagery

Increasingly, soil surveys make use of a combination of legacy data, ancillary data and new field data. While combining the different sources of information, positional errors can play a large role. For example, the spatial discrepancy between remote sensing images and field data can depend on many factors, including the positioning accuracy of ground-based observations. The accuracy of GPS receivers for the territory of Russia is approximately 3–10 m. The aim of the study was to estimate the impact of sampling positioning accuracy on the relationship between soil organic carbon content and the infrared channel of the WorldView-2 satellite image and for mapping soil organic carbon contents in an agricultural field in the territory of the Bryansk Opolje (Russia). Intensive sampling of the topsoil took place. The positional accuracy was also measured and used to perturb the locations of the samples. The data were used to study: (i) the relationships between soil organic carbon and infrared reflectance, (ii) the variation in soil organic carbon through five different interpolation techniques, and (iii) the fraction of the fields with low soil organic matter contents. The study showed that the positional inaccuracies can have an important impact. The standardized methods to estimate the positional accuracy, perturb the locations and evaluate its impact seems to be an easy way to explore the quality of data.

Enabling efficient, large-scale high-spatial resolution wetland mapping using satellites

Global coastal and freshwater wetlands provide ecosystem services valued at over $200,000 USD per hectare per year. Despite their value, wetlands continue to be lost at alarming rates worldwide, with as much as 71% of global wetland extent being converted to other land-cover types since 1900. Targeted conservation and restoration efforts, however, have proven successful, particularly in Europe and North America. Such efforts require accurate protocols to identify, assess, and map wetlands repeatedly to enable detection of change. High-resolution (i.e. 2-meter pixel) satellite imagery has proven effective to map wetlands at higher accuracies than historical 30-meter or coarser satellite data. We describe a method to process and classify high volumes of multispectral high-resolution satellite data to update wetland maps. We show the results of a study conducted with 130 2-meter resolution WorldView-2 satellite images to map forested wetland, upland, water, bare land, and developed lands in a 6500 km2 watershed. The processing of the map was completed in under 24 h and was more accurate at identifying forested wetland (78%) and upland (64%) than three previous, widely used maps of the same area (45–65%, and 49–53%, respectively). This method offers high potential for monitoring change in coastal areas and adjacent watersheds over large geographic scales.

Evaluating glacier dynamics using temporal remote sensing images: a case study of Hunza Valley, northern Pakistan

Glaciers are the largest freshwater repository on the earth surface. Northern Pakistan is covered with some of the largest midlatitude glaciers in the world. Most of the recent studies on the region lead to contradictory results that glaciers in the Karakoram ranges have shown either advancement, retreat or stable behaviors and therefore coin the term “Karakoram anomaly.” In the present study, temporal Landsat satellite images, acquired in the years of 1977, 1999, 2001, 2007, 2009 and 2014, were used to evaluate the temporal dynamics of the selected glaciers in the Hunza Valley, northern Pakistan, including the Batura, Passu, Ghulkin and Gulmit. The glaciers outlines were extracted using NDSI and slope gradient, followed by manual editing. The Landsat-derived glacier inventories were verified through the fine-resolution WorldView-2 satellite image and field observations. Evaluating the temporal glacier inventories reveals that the selected glaciers are retreating; however, the melting rate is varying depending on the supra-glacier debris cover. The melting or advancing of glacier is also influenced by the mean annual temperature and precipitation records. The study should assist in predicting the future climate of the region.

Terrestrial CDOM in Lakes of Yamal Peninsula: Connection to Lake and Lake Catchment Properties

In this study, we analyze interactions in lake and lake catchment systems of a continuous permafrost area. We assessed colored dissolved organic matter (CDOM) absorption at 440 nm (a(440)CDOM) and absorption slope (S300–500) in lakes using field sampling and optical remote sensing data for an area of 350 km2 in Central Yamal, Siberia. Applying a CDOM algorithm (ratio of green and red band reflectance) for two high spatial resolution multispectral GeoEye-1 and Worldview-2 satellite images, we were able to extrapolate the a(λ)CDOM data from 18 lakes sampled in the field to 356 lakes in the study area (model R2 = 0.79). Values of a(440)CDOM in 356 lakes varied from 0.48 to 8.35 m−1 with a median of 1.43 m−1. This a(λ)CDOM dataset was used to relate lake CDOM to 17 lake and lake catchment parameters derived from optical and radar remote sensing data and from digital elevation model analysis in order to establish the parameters controlling CDOM in lakes on the Yamal Peninsula. Regression tree model and boosted regression tree analysis showed that the activity of cryogenic processes (thermocirques) in the lake shores and lake water level were the two most important controls, explaining 48.4% and 28.4% of lake CDOM, respectively (R2 = 0.61). Activation of thermocirques led to a large input of terrestrial organic matter and sediments from catchments and thawed permafrost to lakes (n = 15, mean a(440)CDOM = 5.3 m−1). Large lakes on the floodplain with a connection to Mordy-Yakha River received more CDOM (n = 7, mean a(440)CDOM = 3.8 m−1) compared to lakes located on higher terraces.