Posidonia Oceanica habitat mapping in shallow coastal waters along Losinj Island, Croatia using Geoeye-1 multispectral imagery

Seagrasses are important components of marine ecosystem. They are a primary food source for many organisms and provide shelter and nursery areas for many more. Also they stabilize sediments and act as a natural barrier against coastal erosion. But despite their valuable rule, Seagrasses are facing large threats because of human development. The stresses caused by human activities like trawling and anchoring, seabed mining, gas or mineral exploration and production, industrial chemical waste, agricultural run-off and coastal development results in a worldwide decline of seagrass meadows coverage. In this thesis, a study was carried out using Geoeye-1 satellite data acquired on July and August 2011 to extract bottom type features, i.e. seagrass (Posidonia Oceanica), sand and rock in shallow coastal waters of Losinj Island, Croatia. To conduct the study, atmospheric correction, glint removal and water column correct were done to remove the noise from the seabed reflectance but due to some quality problems (sensor calibration) with the imagery dataset prevented us to get satisfactory results from glint removal and water column correction. These techniques are based on empirical models among different band pairs and in the case of a problem in making an accurate reflectance values, their result would be unreliable. So it was decided to perform a principle component analysis to improve the spectral separability of desired classes. Then a hard supervised classification was performed to identify the spectral clusters and label them based on the training phase of the classification algorithm. But before running the classifier to compensate the attenuation effect of water body, it was decided to consider each training sample as a separate class and afterwards reclassify the results into our primary classes. At the end of the classification result were edited using a majority filter to reduce the salt and pepper effect of the classification results and the accuracy of the classification was calculated for each scene. Afterwards a mosaic was produced from the classification results. The overall accuracy of the mosaic and its kappa coefficient was calculated as 80% and 0.7 respectively which proved that the classification was successful and Geoeye-1 imagery can be used reliably to identify the extent of seagrass community in a fast and cost-effective way.Seagrasses are important components of marine ecosystem. They are a primary food source for many organisms and provide shelter and nursery areas for many more. Also they stabilize sediments and act as a natural barrier against coastal erosion. But despite their valuable rule, Seagrasses are facing large threats because of human development. The stresses caused by human activities like trawling and anchoring, seabed mining, gas or mineral exploration and production, industrial chemical waste, agricultural run-off and coastal development results in a worldwide decline of seagrass meadows coverage. In this thesis, a study was carried out using Geoeye-1 satellite data acquired on July and August 2011 to extract bottom type features, i.e. seagrass (Posidonia Oceanica), sand and rock in shallow coastal waters of Losinj Island, Croatia. To conduct the study, atmospheric correction, glint removal and water column correction were done to remove the noise from the seabed reflectance but due to some quality problems with the imagery dataset prevented us to get satisfactory results from the statistical analysis; it was decided to perform a principle component analysis (PCA) to improve the spectral separability of desired classes. Then a hard supervised classification was performed to identify the spectral clusters and label them based on the training phase of the classification algorithm. But before running the classifier to compensate the attenuation effect of water body, it was decided to consider each training sample as a separate class and afterwards reclassify the results into our primary classes. At end the classification result were edited using a majority and the accuracy of the classification was calculated for each scene. Afterwards a mosaic was produced from the classification results. The overall accuracy of the mosaic and its kappa coefficient was calculated as 80% and 0.7 respectively which proved that the classification was successful and Geoeye-1 imagery can be used reliably to identify the extent of seagrass community in a fast and cost-effective way

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Area method compared with Transect method to measure shoreline movement

Vector GIS represents shorelines as polylines that show the boundaries between land and water. This article compares two methods to measure how boundaries move among time points. The Area method converts the polylines at various time points into polygons of either land or water. The Area method measures temporal change as loss or gain of land areas. The Transect method requires subjective decisions to draw a baseline near the shorelines and then to draw transects that emanate from the baseline to intersect the shorelines. The Transect method measures temporal change as the distance between the intersection points along each transect as in the software packages AMBUR and DSAS. This article compares the conceptual foundations of the two methods. We illustrate how the Area method produces results for cases where the Transect method encounters practical difficulties. We list each method’s characteristics, so researchers can align the method with their research question

Rules to write mathematics to clarify metrics such as the land use dynamic degrees

Context: Scientists frequently describe temporal change among land categories by reporting the single land use dynamic degree and the comprehensive land use dynamic degree (CLUDD). The original intention of CLUDD was to compute the annual change percentage, which is the size of annual change expressed as a percentage of the size of the spatial extent. However, the literature’s mathematical descriptions of CLUDD have been unclear, thus readers have imagined various ways to compute CLUDD. Objectives: Our manuscript clarifies the confusion and offers rules for mathematical notation so that authors can avoid future confusion. Methods: We examine the literature to see how authors have computed and interpreted the land use dynamic degrees. We illustrate deficiencies of one version of CLUDD. Then we propose equations for the components of annual change percentage. Results: The literature shows three common misunderstandings. First, some authors add percentages of categories without accounting for the sizes of the categories. Second, other authors compute either double or half of the annual change percentage. Third, many authors interpret CLUDD as if CLUDD were the annual change percentage when they use a version of CLUDD that is not the annual change percentage. Conclusions: We recommend that the professional community use annual change percentage, its three components and Intensity Analysis to express temporal change among categories. Vague mathematical notation has created confusion concerning the land use dynamic degrees; therefore, we give rules for how to write mathematics clearly in a manner that applies to many professions