A Comparative Study of Classical Clustering Method and Cuckoo Search Approach for Satellite Image Clustering: Application to Water Body Extraction

Image clustering is a critical and essential component of image analysis to several fields and could be considered as an optimization problem. Cuckoo Search (CS) algorithm is an optimization algorithm that simulates the aggressive reproduction strategy of some cuckoo species.In this paper, a combination of CS and classical algorithms (KM, FCM, and KHM) is proposed for unsupervised satellite image classification. Comparisons with classical algorithms and also with CS are performed using three cluster validity indices namely DB, XB, and WB on synthetic and real data sets. Experimental results confirm the effectiveness of the proposed approach

Development of Geospatial and Temporal Characteristics for Hispaniola’s Lake Azuei and Enriquillo Using Landsat Imagery

In this paper, we used Landsat imagery for water body identification to create a novel 36-year surface area extent time series for lakes Azuei (Haiti) and Enriquillo (Dominican Republic) aimed at illuminating the dramatic temporal changes of these two lakes not just at yearly but at monthly or even sub-monthly scales. We used the Normalized Difference Water Index (NDWI) to extract water features and we also used spatial differentiation and thresholding techniques to remove clouds and associated shadows from the scene that were then passed through gap filling algorithms to complete and extract the lake extent polygons. We also explored the challenges that arrive from trying to combine RS-based Digital Elevation Model data with locally collected bathymetric data to yield a seamless representation of the topographic features of the rift valley that contains the two lakes. This “bathtub” model was then meshed with the lake extent polygons to compute lake volumes, maximum depths, and geospatially referenced lake levels rating curves. We used this data to examine the lakes and their geospatial characteristics in the context of the lakes’ growth/shrinking patterns. While we did not carry out a full hydrologic analysis we attempted to illuminate how specific lake levels cause what type of flooding and especially answered the questions if (a) Lake Azuei would ever spill into Lake Enriquillo, and (b) what the maximum lake levels need to be before spilling into neighboring watersheds

Analysis of Surface Area Fluctuation of the Haramaya Lake using Remote Sensing Data

Human’s unwise and ineffective exploitation behavior has caused losses of the vital natural resources, soil and water, which will definitely leave the future of the next generation in jeopardy. As a result of human intervention and natural causes most lakes in Ethiopia are shrinking in size while others are showing increase in volume. The intensive exploitation, beyond its regeneration rate, of the Lake Haramaya for water supply and agricultural purposes by the community within and outside its catchment boundary has led to its extinction. Although, some studies have been conducted on land use/land cover dynamics, the focus given to quantification of temporal variability lake surface area and the impact of weather variability on the lake water was inadequate. Hence, this study was conducted with the prime objective of mapping/quantifying the temporal lake surface area fluctuation using time series remote sensing images and investigating the impact of weather/climate variability on the lake. After acquiring Landsat images of the years 1985, 1995, 2003, 2010 and 2016 over the dry Haramaya Lake basin (path/row 166/54), the Modified Normalized Difference Water Index (MNDWI) and the Normalized Difference Vegetation Index (NDVI) were used for enhancing and extracting the open water surface of the lake. All of the enhanced images display a trend of decreasing lake surface water area with an average shrinkage of 23.6% between the year 1985 and up to its disappearance. After 2000 the lake surface area shrinkage was at its maximum which has a direct relation with the occurrence of dry weather as a result of relatively higher temperature and low rainfall between the years 2000 to 2003.Keywords: Haramaya Lake; Image enhancement; Fluctuation; MNDWI; NDVI; Ethiopia

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Spatio-temporal analysis of coastal sediment erosion in Cape Town through remote sensing and geoinformation science

Coastal erosion can be described as the landward or seaward propagation of coastlines. Coastal processes occur over various space and time scales, limiting in-situ approaches of monitoring change. As such it is imperative to take advantage of multisensory, multi-scale and multi-temporal modern spatial technologies for multi-dimensional coastline change monitoring. The research presented here intends to showcase the synergy amongst remote sensing techniques by showcasing the use of coastal indicators towards shoreline assessment over the Kommetjie and Milnerton areas along the Cape Town coastline. There has been little progress in coastal studies in the Western Cape that encompass the diverse and dynamic aspects of coastal environments and in particular, sediment movement. Cape Town, in particular; is socioeconomically diverse and spatially segregated, with heavy dependence on its 240km of coastline. It faces sea level rise intensified by real-estate development close to the high-water mark and on reclaimed land. Spectral indices and classification techniques are explored to accommodate the complex bio-optical properties of coastal zones. This allows for the segmentation of land and ocean components to extract shorelines from multispectral Landsat imagery for a long term (1991-2021) shoreline assessment. The DSAS tool used these extracted shorelines to quantify shoreline change and was able to determine an overall averaged erosional rate of 2.56m/yr. for Kommetjie and 2.35m/yr. for Milnerton. Beach elevation modelling was also included to evaluate short term (2016-2021) sediment volumetric changes by applying Differential Interferometry to Sentinel-1 SLC data and the Waterline method through a combination of Sentinel -1 GRD and tide gauge data. The accuracy, validation and correction of these elevation models was conducted at the pixel level by comparison to an in-field RTK GPS survey used to capture the current state of the beaches. The results depict a sediment deficit in Kommetjie whilst accretion is prevalent along the Milnerton coastline. Shoreline propagation and coastal erosion quantification leads to a better understanding of geomorphology, hydrodynamic and land use influences on coastlines. This further informs climate adaptation strategies, urban planning and can support further development of interactive coastal information systems

TRIAD - Preliminary design of an operational earth resources survey system Final report

Design of operational earth resources survey syste

Activity recognition with cooperative radar systems at C and K band

Remote health monitoring is a key component in the future of healthcare with predictive and fall risk estimation applications required in great need and with urgency. Radar, through the exploitation of the micro-Doppler effect, is able to generate signatures that can be classified automatically. In this work, features from two different radar systems operating at C band and K band have been used together co-operatively to classify ten indoor human activities with data from 20 subjects with a support vector machine classifier. Feature selection has been applied to remove redundancies and find a set of salient features for the radar systems, individually and in the fused scenario. Using the aforementioned methods, we show improvements in the classification accuracy for the systems from 75 and 70% for the radar systems individually, up to 89% when fused

Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital

Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage