70 research outputs found

    Automatic mapping aquaculture in coastal zone from TM imagery with OBIA approach

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    IEEE GRSS; The Geographical Society of China<span class="MedBlackText">Aquaculture area monitoring is of great importance for coastal zone sustainable management and planning. This paper focuses on the development and assessment of an automatic approach for aquaculture mapping in coastal zone from TM imagery. The contribution mainly consists of three aspects: first, utilizes the Multi-scale segmentation/object relationship modeling (MSS/ORM) strategy on the object based image analysis (OBIA) of TM imagery; second, evaluates the effectiveness GLCM homogeneity texture feature on pond aquaculture area information extraction; third, compares the analysis results from three different approaches, namely pixelbased maximum likelihood classifier (MLC), One-step supervised OBIA with stand nearest neighbor (SNN) and MSS/ORM OBIA strategy. The final result shows that the MSS/ORM OBIA approach greatly improves the classification accuracy and has good potential for automatic pond aquaculture land mapping in coastal zone from TM imagery.</span

    Remote sensing technologies for the assessment of marine and coastal ecosystems

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    Abstract This chapter reviews the Remote Sensing (RS) technologies that are particularly appropriate for marine and coastal ecosystem research and management. RS techniques are used to perform analysis of water quality in coastal water bodies; to identify, characterize and analyze river plumes; to extract estuarine/coastal sandy bodies; to identify beach features/patterns; and to evaluate the changes and integrity (health) of the coastal lagoon habitats. For effective management of these ecosystems, it is essential to have satellite data available and complementary accurate information about the current state of the coastal regions, in addition to well-informed forecasts about its future state. In recent years, the use of space, air and ground-based RS strategies has allowed for the rapid data collection, Image processing (Pixel-Based and Object-Based Image Analysis (OBIA) classification) and dissemination of such information to reduce vulnerability to natural hazards, anthropic pressures, and to monitoring essential ecological processes, life support systems and biological diversityinfo:eu-repo/semantics/submittedVersio

    Evaluation of riverbank erosion based on mangrove boundary changes identification using multi-temporal satellite imagery

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    Evaluating riverbank erosion in mangrove forests is dynamic and challenging because of the complex environment that is exposed to tidal and sedimentation factor. Besides, assessing riverbank erosion in this environment requires a technique that reduces dependency on tidal and sedimentation without affecting the quality of the assessment. Hence, this study evaluated riverbank erosion based on mangrove boundary changes using multi-temporal satellite images comprising Quickbird, WorldView-2 and Pleiades-1B. The first objective of this study is to determine mangrove boundary shifting and its long-term impact towards riverbank features followed by validating the mangrove boundary shifting of satellite imagery with field measurement data, which comprise Real Time Kinematic-Global Positioning System (RTK-GPS). Next, the study assessed the rates of changes o f the riverbank erosion and accretion and the final objective developing a riverbank erosion prediction model. In this study, a change detection technique was used to identify the mangrove boundary changes of Kilim River at different timelines. The extracted mangrove boundary from satellite images for the years 2005, 2012 and 2017 were used to identify changes in the riverbank features such as line shifting, river width, erosion, and accretion. Subsequently, a vector image overlay was used to determine the mangrove boundary shifting for the corresponding years and evaluate the erosion and accretion rates using symmetrical difference and erase tool in ArcGIS software. Sequentially, Root Mean Square Error (RMSE) analysis validated the accuracy of image geo- referencing process while residual analysis was employed to validate the accuracy between satellite imagery and field measurement data comprising RTK-GPS and erosion pin data. Then, line buffering and kernel density analysis were used to develop a riverbank erosion prediction model based on three parameters, namely distance of erosion, area of erosion and direction of shifted mangrove boundary. The initial findings of this study showed that the mangrove boundary changes shifted backwards in the opposite direction from the river and the range of shifting was different according to the intensity o f boat traffic. One of the findings showed that the increasing rates of riverbank erosion ranged from 11302.019 square meters in the first epoch to 15674.721 square meters in the second epoch. Another finding illustrated the riverbank erosion prediction model which displayed several areas such as Sections A, B, I and L which are potentially facing serious riverbank erosion problems in the future in comparison to Sections C, D, E, F, G, H and K. The final finding discussed data validation between Pleiades-1B and GPS-RTK which recorded 0.305 of the r-square value whereas 0.477 was recorded as the r-square value for both Pleiades-1B and the erosion pin. The other validation comprised the second epoch of satellite image (WorldView-2 and Pleiades- 1B) and the erosion pin data which revealed the r-square of 0.9347 and showed the strong relationship between both data. As a conclusion, the findings have shown that the evaluation of the riverbank erosion based on mangrove boundary changes using multi-temporal satellite images is capable o f assisting stakeholders including the Langkawi Development Authority (LADA), Department of Irrigation and Drainage Malaysia (DID) and Marine Department Malaysia to have in-depth understanding of riverbank erosion issue that would enable them to prepare a mitigation plan in the future

    Development of A Simple Method for Detecting Mangrove Using Free Open Source Software

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    Mangrove forests are becoming attractive natural charms and make everyone to take advantage of the existence of these coastal ecosystems both directly and indirectly. However, the condition of mangrove forests is threatened by their presence due to environmental factors around them. Sustainable mangrove monitoring efforts must always be increased to support the preservation of the mangrove ecosystem. The purpose of this study is to develop a fast and easy mangrove forest identification method based on remote sensing satellite imagery data. The research location chosen was the mangrove area in Segara Anakan, Cilacap. The data image used is Landsat 8 image acquisition on December 3, 2017 with path/row 121/065 obtained from the LAPAN Pustekdata Landsat catalog. The methods used include the Optimum Index Factor (OIF) method for selecting the best channels and the supervised classification method using the Semi-Automatic Classification Plugin (SCP) contained in open source software and provides three algorithm choices for the classification process including Minimum Distance, Maximum Likelihood and Spectral Angle Mapping. The results show the combination of RGB 564 (NIR+SWIR+RED) was the best in the identification of mangrove forests and the Maximum Likelihood classification algorithm was the most optimal in distinguishing mangrove and mangrove classes from both Macro Class and Class levels. The results of the calculation of the area show the mangrove area of 7,037.16 ha. The developed method can produce information on the distribution of mangroves at research sites more quickly, easily, effectively, and efficiently

    Object-based mapping of temperate marine habitats from multi-resolution remote sensing data

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    PhD ThesisHabitat maps are needed to inform marine spatial planning but current methods of field survey and data interpretation are time-consuming and subjective. Object-based image analysis (OBIA) and remote sensing could deliver objective, cost-effective solutions informed by ecological knowledge. OBIA enables development of automated workflows to segment imagery, creating ecologically meaningful objects which are then classified based on spectral or geometric properties, relationships to other objects and contextual data. Successfully applied to terrestrial and tropical marine habitats for over a decade, turbidity and lack of suitable remotely sensed data had limited OBIA’s use in temperate seas to date. This thesis evaluates the potential of OBIA and remote sensing to inform designation, management and monitoring of temperate Marine Protected Areas (MPAs) through four studies conducted in English North Sea MPAs. An initial study developed OBIA workflows to produce circalittoral habitat maps from acoustic data using sequential threshold-based and nearest neighbour classifications. These methods produced accurate substratum maps over large areas but could not reliably predict distribution of species communities from purely physical data under largely homogeneous environmental conditions. OBIA methods were then tested in an intertidal MPA with fine-scale habitat heterogeneity using high resolution imagery collected by unmanned aerial vehicle. Topographic models were created from the imagery using photogrammetry. Validation of these models through comparison with ground truth measurements showed high vertical accuracy and the ability to detect decimetre-scale features. The topographic and spectral layers were interpreted simultaneously using OBIA, producing habitat maps at two thematic scales. Classifier comparison showed that Random Forests Abstract ii outperformed the nearest neighbour approach, while a knowledge-based rule set produced accurate results but requires further research to improve reproducibility. The final study applied OBIA methods to aerial and LiDAR time-series, demonstrating that despite considerable variability in the data, pre- and post-classification change detection methods had sufficient accuracy to monitor deviation from a background level of natural environmental fluctuation. This thesis demonstrates the potential of OBIA and remote sensing for large-scale rapid assessment, detailed surveillance and change detection, providing insight to inform choice of classifier, sampling protocol and thematic scale which should aid wider adoption of these methods in temperate MPAs.Natural Environment Research Council and Natural Englan

    Low-cost UAV monitoring: insights into seasonal volumetric changes of an oyster reef in the German Wadden Sea

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    This study aims to quantify the dimensions of an oyster reef over two years via low-cost unoccupied aerial vehicle (UAV) monitoring and to examine the seasonal volumetric changes. No current study investigated via UAV monitoring the seasonal changes of the reef-building Pacific oyster (Magallana gigas) in the German Wadden Sea, considering the uncertainty of measurements and processing. Previous studies have concentrated on classifying and mapping smaller oyster reefs using terrestrial laser scanning (TLS) or hyperspectral remote sensing data recorded by UAVs or satellites. This study employed a consumer-grade UAV with a low spectral resolution to semi-annually record the reef dimensions for generating digital elevation models (DEM) and orthomosaics via structure from motion (SfM), enabling identifying oysters. The machine learning algorithm Random Forest (RF) proved to be an accurate classifier to identify oysters in low-spectral UAV data. Based on the classified data, the reef was spatially analysed, and digital elevation models of difference (DoDs) were used to estimate the volumetric changes. The introduction of propagation errors supported determining the uncertainty of the vertical and volumetric changes with a confidence level of 68% and 95%, highlighting the significant change detection. The results indicate a volume increase of 22 m³ and a loss of 2 m³ in the study period, considering a confidence level of 95%. In particular, the reef lost an area between September 2020 and March 2021, when the reef was exposed to air for more than ten hours. The reef top elevation increased from -15.5 ± 3.6 cm NHN in March 2020 to -14.8 ± 3.9 cm NHN in March 2022, but the study could not determine a consistent annual growth rate. As long as the environmental and hydrodynamic conditions are given, the reef is expected to continue growing on higher elevations of tidal flats, only limited by air exposure. The growth rates suggest a further reef expansion, resulting in an increased roughness surface area that contributes to flow damping and altering sedimentation processes. Further studies are proposed to investigate the volumetric changes and limiting stressors, providing robust evidence regarding the influence of air exposure on reef loss

    Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms

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    Remote sensing technology is an efficient tool for various practical applications of environmental resources management. Advances in this technology include the diverse range of high quality data sources and image analysis techniques. Object-based image analysis (OBIA) and machine learning algorithms are recent advances, which this thesis evaluates. OBIA and machine learning algorithms are first tested using a combination of multiple datasets for identifying individual tree species. These datasets include Quickbird, LiDAR, and GIS derived terrain data. Improvements in tree species classification were obtained and the best data combination was terrain context (based on slope, elevation, and wetness), tree height, canopy shape, and branch density (based on LiDAR return intensity). The availability of a range of classifiers and different data pre-processing techniques adds to the complexity of image analysis. The combinations of these techniques result in a large number of potential outcomes and these need to be evaluated. Therefore, the second part of this research investigated and compared tree species classification performance for different methods (Naïve Bayes - NB , Logistic Regression - LR, Random Forest - RF, and Support Vector Machine - SVM), combined with various dimensionality reduction (DR) methods (Correlation-based feature selection filter, Information Gain, Wrapper methods, and Principal Component Analysis). When DR was used prior to classification, only the NB classifier had a significant improvement in accuracy. SVM and RF had the best classification accuracy, and this was achieved without DR. The final part of this thesis demonstrates a new method using OBIA for mapping the biomass change of mangrove forests in Vietnam between 2000 and 2011 from SPOT images. First, three different mangrove associations were identified using two levels of image segmentation followed by a SVM classifier and a range of spectral, texture and GIS information for classification. The RF regression model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy

    GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book

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    Coastal Eye: Monitoring Coastal Environments Using Lightweight Drones

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    Monitoring coastal environments is a challenging task. This is because of both the logistical demands involved with in-situ data collection and the dynamic nature of the coastal zone, where multiple processes operate over varying spatial and temporal scales. Remote sensing products derived from spaceborne and airborne platforms have proven highly useful in the monitoring of coastal ecosystems, but often they fail to capture fine scale processes and there remains a lack of cost-effective and flexible methods for coastal monitoring at these scales. Proximal sensing technology such as lightweight drones and kites has greatly improved the ability to capture fine spatial resolution data at user-dictated visit times. These approaches are democratising, allowing researchers and managers to collect data in locations and at defined times themselves. In this thesis I develop our scientific understanding of the application of proximal sensing within coastal environments. The two critical review pieces consolidate disparate information on the application of kites as a proximal sensing platform, and the often overlooked hurdles of conducting drone operations in challenging environments. The empirical work presented then tests the use of this technology in three different coastal environments spanning the land-sea interface. Firstly, I use kite aerial photography and uncertainty-assessed structure-from-motion multi-view stereo (SfM-MVS) processing to track changes in coastal dunes over time. I report that sub-decimetre changes (both erosion and accretion) can be detected with this methodology. Secondly, I used lightweight drones to capture fine spatial resolution optical data of intertidal seagrass meadows. I found that estimations of plant cover were more similar to in-situ measures in sparsely populated than densely populated meadows. Lastly, I developed a novel technique utilising lightweight drones and SfM-MVS to measure benthic structural complexity in tropical coral reefs. I found that structural complexity measures were obtainable from SfM-MVS derived point clouds, but that the technique was influenced by glint type artefacts in the image data. Collectively, this work advances the knowledge of proximal sensing in the coastal zone, identifying both the strengths and weaknesses of its application across several ecosystems.Natural Environment Research Council (NERC

    Calibration of DART Radiative Transfer Model with Satellite Images for Simulating Albedo and Thermal Irradiance Images and 3D Radiative Budget of Urban Environment

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    Remote sensing is increasingly used for managing urban environment. In this context, the H2020 project URBANFLUXES aims to improve our knowledge on urban anthropogenic heat fluxes, with the specific study of three cities: London, Basel and Heraklion. Usually, one expects to derive directly 2 major urban parameters from remote sensing: the albedo and thermal irradiance. However, the determination of these two parameters is seriously hampered by complexity of urban architecture. For example, urban reflectance and brightness temperature are far from isotropic and are spatially heterogeneous. Hence, radiative transfer models that consider the complexity of urban architecture when simulating remote sensing signals are essential tools. Even for these sophisticated models, there is a major constraint for an operational use of remote sensing: the complex 3D distribution of optical properties and temperatures in urban environments. Here, the work is conducted with the DART (Discrete Anisotropic Radiative Transfer) model. It is a comprehensive physically based 3D radiative transfer model that simulates optical signals at the entrance of imaging spectro-radiometers and LiDAR scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental (atmosphere, topography,…) and instrumental (sensor altitude, spatial resolution, UV to thermal infrared,…) configuration. Paul Sabatier University distributes free licenses for research activities. This paper presents the calibration of DART model with high spatial resolution satellite images (Landsat 8, Sentinel 2, etc.) that are acquired in the visible (VIS) / near infrared (NIR) domain and in the thermal infrared (TIR) domain. Here, the work is conducted with an atmospherically corrected Landsat 8 image and Bale city, with its urban database. The calibration approach in the VIS/IR domain encompasses 5 steps for computing the 2D distribution (image) of urban albedo at satellite spatial resolution. (1) DART simulation of satellite image at very high spatial resolution (e.g., 50cm) per satellite spectral band. Atmosphere conditions are specific to the satellite image acquisition. (2) Spatial resampling of DART image at the coarser spatial resolution of the available satellite image, per spectral band. (3) Iterative derivation of the urban surfaces (roofs, walls, streets, vegetation,…) optical properties as derived from pixel-wise comparison of DART and satellite images, independently per spectral band. (4) Computation of the band albedo image of the city, per spectral band. (5) Computation of the image of the city albedo and VIS/NIR exitance, as an integral over all satellite spectral bands. In order to get a time series of albedo and VIS/NIR exitance, even in the absence of satellite images, ECMWF information about local irradiance and atmosphere conditions are used. A similar approach is used for calculating the city thermal exitance using satellite images acquired in the thermal infrared domain. Finally, DART simulations that are conducted with the optical properties derived from remote sensing images give also the 3D radiative budget of the city at any date including the date of the satellite image acquisition
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