Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

Unmanned Aerial Systems (UAS) are now capable of gathering high-resolution data, therefore, landslides can be explored in detail at larger scales. In this research, 132 aerial photographs were captured, and 85,456 features were detected and matched automatically using UAS photogrammetry. The root mean square (RMS) values of the image coordinates of the Ground Control Points (GPCs) varied from 0.521 to 2.293 pixels, whereas maximum RMS values of automatically matched features was calculated as 2.921 pixels. Using the 3D point cloud, which was acquired by aerial photogrammetry, the raster datasets of the aspect, slope, and maximally stable extremal regions (MSER) detecting visual uniformity, were defined as three variables, in order to reason fissure structures on the landslide surface. In this research, an Adaptive Neuro Fuzzy Inference System (ANFIS) and a Logistic Regression (LR) were implemented using training datasets to infer fissure data appropriately. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The experiments exposed that high-resolution imagery is an indispensable data source to model and validate landslide fissures appropriately

Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery

Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained

Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+

In geospatial applications such as urban planning and land use management, automatic detection and classification of earth objects are essential and primary subjects. When the significant semantic segmentation algorithms are considered, DeepLabV3+ stands out as a state-of-the-art CNN. Although the DeepLabV3+ model is capable of extracting multi-scale contextual information, there is still a need for multi-stream architectural approaches and different training approaches of the model that can leverage multi-modal geographic datasets. In this study, a new end-to-end dual-stream architecture that considers geospatial imagery was developed based on the DeepLabV3+ architecture. As a result, the spectral datasets other than RGB provided increments in semantic segmentation accuracies when they were used as additional channels to height information. Furthermore, both the given data augmentation and Tversky loss function which is sensitive to imbalanced data accomplished better overall accuracies. Also, it has been shown that the new dual-stream architecture using Potsdam and Vaihingen datasets produced 88.87% and 87.39% overall semantic segmentation accuracies, respectively. Eventually, it was seen that enhancement of the traditional significant semantic segmentation networks has a great potential to provide higher model performances, whereas the contribution of geospatial data as the second stream to RGB to segmentation was explicitly shown

Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+

In geospatial applications such as urban planning and land use management, automatic detection and classification of earth objects are essential and primary subjects. When the significant semantic segmentation algorithms are considered, DeepLabV3+ stands out as a state-of-the-art CNN. Although the DeepLabV3+ model is capable of extracting multi-scale contextual information, there is still a need for multi-stream architectural approaches and different training approaches of the model that can leverage multi-modal geographic datasets. In this study, a new end-to-end dual-stream architecture that considers geospatial imagery was developed based on the DeepLabV3+ architecture. As a result, the spectral datasets other than RGB provided increments in semantic segmentation accuracies when they were used as additional channels to height information. Furthermore, both the given data augmentation and Tversky loss function which is sensitive to imbalanced data accomplished better overall accuracies. Also, it has been shown that the new dual-stream architecture using Potsdam and Vaihingen datasets produced 88.87% and 87.39% overall semantic segmentation accuracies, respectively. Eventually, it was seen that enhancement of the traditional significant semantic segmentation networks has a great potential to provide higher model performances, whereas the contribution of geospatial data as the second stream to RGB to segmentation was explicitly shown

Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery

Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained

Turkey's globally important biodiversity in crisis

WOS: 000298521500007Turkey (Turkiye) lies at the nexus of Europe, the Middle East, Central Asia and Africa. Turkey's location, mountains, and its encirclement by three seas have resulted in high terrestrial, fresh water, and marine biodiversity. Most of Turkey's land area is covered by one of three biodiversity hotspots (Caucasus, Irano-Anatolian, and Mediterranean). Of over 9000 known native vascular plant species, one third are endemic. Turkey faces a significant challenge with regard to biodiversity and associated conservation challenges due to limited research and lack of translation into other languages of existing material. Addressing this gap is increasingly relevant as Turkey's biodiversity faces severe and growing threats, especially from government and business interests. Turkey ranks 140th out of 163 countries in biodiversity and habitat conservation. Millennia of human activities have dramatically changed the original land and sea ecosystems of Anatolia, one of the earliest loci of human civilization. Nevertheless, the greatest threats to biodiversity have occurred since 1950, particularly in the past decade. Although Turkey's total forest area increased by 5.9% since 1973, endemic-rich Mediterranean maquis, grasslands, coastal areas, wetlands, and rivers are disappearing, while overgrazing and rampant erosion degrade steppes and rangelands. The current "developmentalist obsession", particularly regarding water use, threatens to eliminate much of what remains, while forcing large-scale migration from rural areas to the cities. According to current plans, Turkey's rivers and streams will be dammed with almost 4000 dams, diversions, and hydroelectric power plants for power, irrigation, and drinking water by 2023. Unchecked urbanization, dam construction, draining of wetlands, poaching, and excessive irrigation are the most widespread threats to biodiversity. This paper aims to survey what is known about Turkey's biodiversity, to identify the areas where research is needed, and to identify and address the conservation challenges that Turkey faces today. Preserving Turkey's remaining biodiversity will necessitate immediate action, international attention, greater support for Turkey's developing conservation capacity, and the expansion of a nascent Turkish conservation ethic. (C) 2011 Published by Elsevier Ltd.Scientific and Technical Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK); CSUCI; Born Free Foundation; Christensen Fund; Kafkas UniversityKafkas University; Turkey's Ministry of Environment and ForestryMinistry of Forestry & Water Affairs - Turkey; UNDP; Whitley Fund; NSFNational Science Foundation (NSF) [EF-0832858]We thank the Istanbul Technical University for hosting the December 2009 workshop that led to this paper. C.H.S. thanks The Scientific and Technical Research Council of Turkey (TUBITAK) for providing a travel grant. S.A. thanks CSUCI for support for his initial travel to Turkey. C.H.S. and S.A. thank the Born Free Foundation, the Christensen Fund, the Conservation Leadership Programme, Kafkas University, Turkey's Ministry of Environment and Forestry, the UNDP, and the Whitley Fund for their long-term support of their community-based conservation, ecological research, environmental education, and biodiversity monitoring efforts in Turkey. E.A. is supported through a NIMBioS postdoctoral fellowship, NSF Award #EF-0832858. We thank Emrah Coban for his assistance with obtaining the literature, Rachel Morrison, Elizabeth Platt and three anonymous reviewers for their helpful comments, and Stacey Anderson, Elif Batuman and Tanya Williams for their careful proofreading and wordsmithing. This paper is dedicated to the countless naturalists and conservationists who devoted their lives to study and conserve Turkey's biodiversity