Using CORONA and Landsat Data for Evaluating and Mapping Long-term LULC Changes in Iraqi Kurdistan

This research implements a new approach to classifying CORONA images. A case study demonstrated that information can be extracted from CORONA images using an automated classification process instead of on-screen manual digitising. This study, of the urban growth in Iraqi Kurdistan, has shown that the timeline of a change detection analysis can be extended to include the period before Landsat missions started in 1972. Urban growth was caused by economic growth and population increase

Remote sensing-based mapping of the destruction to Aleppo during the Syrian Civil War between 2011 and 2017

Accurate assessment of damage caused by conflict can be difficult to determine from ground-based surveys, particularly in the context of violence and unsafe conditions. Earth Observation data provides a non-invasive method for rapid damage assessment over wide geographic areas. In this study we use Landsat Imagery captured between 2011 and 2017 to assess the damage in Aleppo, Syria caused by conflict during the Syrian Civil War. Extracting temporal changes in urban environments is complex and the capabilities of traditional spectral-based methodologies are limited. We examined the effectiveness of the Gray-Level Co-Occurrence Matrix (GLCM) and two texture-based metrics (correlation and homogeneity) at classifying changes in reflectance characteristics within urban environments caused by building damage and consequent changes in surface orientation. Homogeneity was a more effective texture measure than correlation (overall accuracy of 79% vs 50%). Results indicated that between 45% and 57% of Aleppo was damaged during the study period, including up to 57% of former rebel held areas and between 34% and 46% of government areas and their surrounds. We used SPOT-6 imagery for accuracy assessment. Damage to Aleppo has yet to be fully quantified and several parts of the city remain unsafe and inaccessible. The results of this study highlight the potential offered by texture analysis for mapping damage to urban areas with freely available imagery and can be readily applied to natural disasters such as earthquakes and the aftermath of extreme weather events

Effectiveness of DOS (Dark-Object Subtraction) method and water index techniques to map wetlands in a rapidly urbanising megacity with Landsat 8 data

The objectives of this work were to examine the applicability of the Dark-Object Subtraction (DOS) atmospheric correction method and water-based index techniques to map wetlands in Dhaka megacity using Landsat 8 data. With the use of both raw data and DOS- corrected imagery, the analysis revealed that DOS- corrected images performed better in discriminating wetland areas. Furthermore, the Modified Normalised Water Index (MNDWI) was the most superior technique whilst the Normalised Difference Water Index (NDWI) was the least suitable in identifying the spatial locations of wetlands in a rapidly urbanising environment such as Dhaka

CORONA Historical De-classified Products

The main focus of this second edition shifts from monitoring and management to Extreme Hydro-Climatic and Food Security Challenges: Exploiting the “Big Data.” Since the writing of the first edition of the book, so much has changed in terms ..

Environmental spatial data within dense tree cover: exploiting multi-frequency GNSS signals to improve positional accuracy

Environmental monitoring tasks over large spatial coverage often necessitate acquiring sample/reference positions using the global navigation satellite systems in order to optimise operational costs. Often, such tasks occur within dense tree coverage where the navigation signals are blocked. For tasks requiring accurate positions under limited resources, this becomes undesirable, especially if the operation is to be carried out while in motion, i.e. “on the fly” or “real-time kinematic”. Even with this realisation, numerous studies investigating the potential of combining the constellations of these navigation systems mostly focus on their structural aspects, leaving the exploitation of the multi-signal constellation under dense tree cover largely untested. Using a test experiment of a station declared unusable due to dense tree cover at Curtin University (Australia), this study evaluates whether sample positions can be improved using multi-constellation global navigation satellite systems where poor sky visibility exist due to tree coverage. Positioning improvement measures are (1) geometrical gain measured by position dilution of precision, (2) horizontal and vertical uncertainty estimates and (3) positional accuracies determined through the comparison of the obtained control positions and their known values. The results indicate significant positioning improvement when all constellations are utilised in comparison with using Global Positioning System alone in dense tree cover environments, i.e. geometrical gain of as much as 72%, horizontal precisions by about 40%, vertical precisions of up to 50% and 94% accuracy improvement. This study thus opines that utilising full global navigation satellite’s constellation would benefit environmental monitoring tasks carried out under dense tree cover

Exploiting a texture framework and high spatial resolution properties of panchromatic images to generate enhanced multi-layer products: Examples of Pleiades and historical CORONA space photographs

© 2020 Informa UK Limited, trading as Taylor & Francis Group. Remotely sensed high spatio-temporal resolution panchromatic images have been extensively used globally to visually detect and interpret changes in landscape components, create land cover maps via the on-screen manual digitization, and to pan-sharpen multi-spectral images among other uses. Despite this attractive array of uses, lack of distinct spectral signatures for panchromatic images from surface elements, e.g. landscape cover types, creates a drawback in their exploitation during any automated classification process, hence limiting their use in the field of remote sensing for land use/land cover change studies. Moreover, the complexities of some panchromatic data (e.g. CORONA) on the one hand, and the traditional texture computation approach on the other hand present additional hurdles in utilizing panchromatic images. This contribution looks at the possibility of exploiting panchromatic images (e.g. Pleiades and historical CORONA products) for remote sensing applications by (i) proposing a new approach that optimizes and generates new multi-layer datasets from panchromatic images that could be useful, e.g. in image classification analysis, (ii)exploiting the combinatorial texture approach to enhance the products generated by the framework in (i) above, and (iii) assessing the capability of the proposed method to handle complex datasets exemplified, e.g. by CORONA. To evaluate the approach, Kurdistan, Iran and Syria regions are selected for study employing the maximum likelihood classification (MLC) scheme. The MLC results indicate an increase in overall accuracy and Kappa coefficient by 32% and 0.42 (compared to raw CORONA image), and 21% and 0.28 (compared to raw Pleiades image). For Iran and Syria, compared to the raw CORONA image, the MLC results show increase by 35% and 0.47, and 42% and 0.56, respectively. Furthermore, based on the results of the accuracy assessment that show an overall accuracy of 85% and Kappa coefficient of 0.80 for Kurdistan, 94% and 0.92 for Iran, and 96% and 0.95 for Syria, the proposed method can be said to have the potential of handling complex panchromatic datasets such as CORONA

A fuzzy model integrating shoreline changes, NDVI and settlement influences for coastal zone human impact classification

© 2019 Elsevier Ltd. Current approaches for obtaining shoreline change rates suffer from inability to give a specialist interpretation of the numerical results represented by velocities (m/yr). This study proposes a fuzzy model for coastal zone human impact classification that integrates shoreline changes, NDVI, and settlement influences to enhance numerical-linguistic fuzzy classification through Geographical Information System (GIS)'s graphical visualization prowess. The model output representing scores are numbers ranging from zero to one, which are convertible into fuzzy linguistic classification variables; i.e., low, moderate, and high on the one hand. On the other hand, use of GIS through NDVI (Normalized Difference Vegetation Index) provides enhancement through graphic visualization. Using Itamaraca Island in Brazil as an example, multi-temporal satellite images are processed to provide all the required input variables. The resulting output divides the entire island into five sectors representing both quantitative and qualitative outcomes (i.e., fuzzy classification composed of both scores and maps), showcasing the capability of the proposed approach to complement shoreline change analysis through physical (map) interpretation in addition to the frequently used numbers. The proposed fuzzy model is validated using random in-situ samples and high resolution image data that has been classified by a coastal geomorphology specialist. The accuracy of the interpretation show 81% of matches are achievable compared to the results of the fuzzy model. The final results delivered by the proposed fuzzy approach show the complex behavior of the local dynamics, thereby adding useful and substantial information for environmental issues and Integrated Coastal Zone Management

Understanding vegetation variability and their ‘‘hotspots’’ within Lake Victoria Basin (LVB: 2003–2018)

© 2020 Elsevier Ltd Lake Victoria's surface area has recently been shown to have shrunk by 0.3% compared to its 1984 value, a decline that has been associated with climatic as well as anthropogenic factors. Climatic factors include, e.g., reduced rainfall, which impacts not only on the lake's water level but also on the basin's vegetation that forms the lake's catchment. Understanding the locations of vegetation changes and the driving forces of such changes, therefore, is of most critical importance to major stakeholders regarding environmental management, policies and planning. For Lake Victoria Basin (LVB; Kenya, Uganda, Tanzania, Rwanda and Burundi), human development and climatic variability/change have subjected the region to significant changes in its vegetation characteristics whose spatio-temporal patterns are, however, not well understood. To understand this variability in vegetation for the period 2003–2018, this study employs the use of remotely sensed MODIS (Moderate Resolution Imaging Spectroradiometer), CHIRPS (Climate Hazards Group InfraRed Precipitation with station data) precipitation data, Google Earth Pro imagery, Gravity Recovery and Climate Experiment (GRACE)-based Mascon's total water storage (TWS) products and the statistical PCA (Principal Component Analysis). The study aims at determining (i) ‘‘significant hotspots’’, i.e. vegetation areas within the LVB largely impacted, and (ii), the extent of which anthropogenic and climatic variability have contributed to the ‘‘hotspots’’ formation. The results indicate a total of 8 hotspots; 5 in Uganda and 1 each in Kenya, Tanzania and Rwanda. Google Earth Pro imagery of all the hotspots show the changes in anthropogenic processes as the primary driver for the long-term changes in vegetation characteristics. Conversely, the analysis of PCA and Mascon's TWS concluded that only the Tanzanian hotspot may have been driven somewhat by climate variability. Climate variability is understood to be the driver of short-term vegetation changes while the long-term effects are driven primarily by human influence

Physical dynamics of Lake Victoria over the past 34 years (1984–2018): Is the lake dying?

Understanding changes in the physical dynamics of lakes (e.g., areas and shorelines) is important to inform policies, planning and management during climate extremes (e.g., floods and droughts). For Lake Victoria, the world's second largest freshwater lake, its physical dynamics and associated changes are not well understood as evidenced, e.g., from the citations of its area 66,400 – 69,485 km2, length 300 – 412 km, width 240 – 355 km, and shorelines 3300 – 4828 km. Its sheer size and lack of research resources commitment by regional governments hamper observations. This contribution employs a suite of remotely sensed products for the past 34 years (1984–2018); Landsat, Sentinel-2, MODIS, Google Earth Pro, CHIRPS, Multivariate El’ Niño-Southern Oscillation Index and altimetry data together with the physical parameters from 37 publications (1969–2018) to (i) study the lake's dynamics and establish its current (2018) state, (ii) identify and analyse hotspots where significantly dynamic changes occur, and (iii), study the contributions of climate change and anthropogenic activities on these dynamics. Utilizing manual digitisation, MNDWI, NDVI and PCA methods, the study shows the lake's mean surface area to be 69,295 km2 (i.e., 812 km2 or 1.2% more than that of the 37 publications) and its 2018 value to be 69,216 km2 (i.e., ~733 km2 (1.1%) more than that of the 37 publications). As to whether the lake is dying, it shrunk by 203 km2 (0.3%) compared to its 1984 value, a decrease noted mainly in four hotspot Gulfs (Birinzi 40%, Winam 20%, Emin Pasha 38% and Mwanza 55%). Correspondingly, the expansion of Nalubaale Dam (2002–2006) decreased the areas by 31%, 10%, 21% and 44%, respectively. Seasonal analysis shows an increase of 9 km2 in the lake's area during the heavy rainy season (March–May) while the ENSO enlarged the area by 0.23% (2007) and 0.45% (2010). It is evident, therefore, that both climate variability/change and anthropogenic activities are exerting a toll on the tropical's largest freshwater body thereby necessitating careful exploitation and management plans

Impacts of extreme climate on Australia's green cover (2003–2018): A MODIS and mascon probe

© 2020 Elsevier B.V. Australia as a continent represents a semi-arid environment that is generally water-limited. Changes in rainfall pattern will inevitably occur due to rising temperatures caused by climate change, which has a direct impact on the distribution of Australia's vegetation (green cover). As variability in rainfall continues to increase, i.e., in frequency and/or magnitude, due to climate change, extreme climate events such as droughts are predicted to become more pervasive and severe that will have an adverse effect on vegetation. This study investigates the effects of extreme climate on Australia's green cover during 2003–2018 for the end of rainy seasons of April and October in the northern and southern parts, respectively, to (i) determine the state of vegetation and its changes, (ii) identify “hotspots”, i.e., regions that constantly experienced statistically significant decrease in NDVI, and (iii), relate changes in the identified hotspots to GRACE-hydrological changes. These are achieved through the exploitation of the statistical tools of Principal Component Analysis (PCA) and Mann-Kendel Test on Gravity Recovery and Climate Experiment (GRACE) hydrological products on the one hand, and the utilization of Australia's rainfall product and Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index (MODIS-NDVI) used here with its native spatial resolution of 0.002413∘ × 0.002413∘ on the other hand. Differences between 3-year intervals from 2003 to 2018 for both April and October datasets are used to quantify vegetation variations. Through area change analysis, the vegetation differences (2003–2018) indicate that April exhibited larger increase (13.77% of total vegetation area) than decrease (7.83%) compared to October, which experienced slightly larger decrease (9.41%) than increase (8.71%). South Australia and Western Australia emerge as “hotspots” in which vegetation statistically decreased in October, with no noticeable change in April. GRACE-based hydrological changes in both hotspots reflect a decreasing trend (2003–2009) and increasing trend (2009–2012) that peaks in 2011, which then transitions towards a gradually decreasing trend after 2012. Australia-wide climate variability (ENSO and IOD) influenced vegetation variations during the data period 2003 to 2018