Remote Sensing And Regional Climate Modeling Of Impacts Of Land Cover Changes On The Climate Of The Marmara Region Of Turkey

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2008Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2008Bu çalışmada, arazi örtüsünde meydana gelen değişimlerin Marmara Bölgesi yaz iklimi üzerindeki etkisi, Landsat görüntülerinin iklim modelleme için kullanılabilirliği ve iklim modellemede kullanılan arazi örtüsü verilerinin doğruluğu araştırılmıştır. Bu amaçla, 1975 ve 2005 yılları için Landsat uydu görüntüleri kullanılarak Marmara Bölgesi arazi örtüsü verileri oluşturulmuştur. 2005 yılı arazi örtüsü verisi, bölgesel iklim modellerinde kullanılan global arazi örtüsü verisi ile kıyaslanıp, model arazi örtüsü verisindeki eksiklikler tespit edilmiştir. 1975 ve 2005 yılı arazi örtüsü verileri Weather Research and Forecasting (WRF) modelleme sistemine girdi olarak sunulup, bu verilerle model çalıştırılmıştır. Ayrıca modelin içindeki arazi kullanımı verisi kullanılarak kontrol simülasyonu gerçekleştirilmiştir. 2005 arazi örtüsü verisi ile gerçekleştirilen simülasyon sonuçları, kontrol simülasyonundan daha iyi sonuç vermiştir. Arazi kullanımı verisinin kalitesinin arttırılması daha doğru iklim simülasyon sonuçlarının alınmasına yardımcı olmuştur. Ayrıca. 1975 ve 2005 yılı arazi kullanımı ile yapılan simülasyon sonuçları karşılaştırılıp, Marmara Bölgesinde meydana gelen arazi kullanımı değişimlerinin lokal iklim üzerindeki etkisi incelenmiştir. Karşılaştırmalar sonucunda, Marmara Bölgesinde özellikle şehirleşmenin arttığı İstanbul, Bursa ve Adapazarı illerinde yaz ayı minimum ve ortalama sıcaklıklarının arttığı, rüzgar doğrultu ve şiddetlerinin değiştiği gözlemlenmiştir. Model sonuçları, arazi örtüsü verileri ve diğer ilgili tüm veriler Coğrafi Bilgi Sisteminde ortak bir çatı altında toplanarak, arazi kullanımı değişiminin iklim üzerindeki etkisi detaylı bir şekilde incelenmiştir. Çalışma sonuçları, Landsat uydu görüntülerinden üretilen arazi örtüsü verilerinin bölgesel iklim modelleme de başarıyla kullanabileceği ve bu verilerle daha doğru iklim simülasyon sonuçlarının elde edilebileceği gösterilmiştir.In this research, investigation of land cover change impact on summer climate of the Marmara Region, utilization of Landsat images in regional climate modeling and assessment the accuracy of global land cover data sets used in were employed. Land cover data of 1975 and 2005 were produced using Landsat satellite images. 2005 land cover data was compared with global land cover data used in regional climate models and deficiencies and inaccuracies in model land cover were determined. 1975 and 2005 land cover data then implemented to Weather Research and Forecasting modeling system and two experiments were conducted with these data. Besides, a control run was employed using model land cover data. The experiment conducted with 2005 land cover gave better results then control experiment. Improving the land cover data improved the climate simulation results. Another comparison was made between the results of 1975 and 2005 land cover data runs to analyze the impact of land cover change on local climate of the region. Comparison results show that minimum and average temperatures increased and wind directions and magnitudes changed as a result of urbanization increase in the Marmara Region especially in İstanbul, Bursa and Adapazari. Climate model results, land cover data and other ancillary data were collected in a Geographic Information System to determine the impact of land cover change on climate in detail. The results of this study showed that land cover data produced from Landsat images can be successfully used in regional climate modeling and more accurate climate simulation results can be obtained with these improved data.DoktoraPh

Investigation of accuracy of land cover data used in regional climate modeling

Arazi yüzeyi iklim modellerinin anahtar elemanlarından olup, yüzeydeki enerjinin hissedilebilir ve gizli ısı olarak, yüzeydeki mevcut suyun ise buharlaşma ve akış olarak bölüşülmesini kontrol etmektedir. Ayrıca, arazi yüzeyinin pürüzlülüğü ve yüzeyin cinsine bağlı olarak ısı kapasitesi ve momentum tutulma miktarı değişmektedir. Bu nedenle arazi yüzeyinin doğru ve güvenilir bir şekilde ifade edilmesi iklim çalışmaları için önemlidir. Bölgesel iklim modellerinin çoğunda, global olarak hazırlanmış olan Global Land Cover Characteristics (GLCC) arazi örtüsü verisi kullanılmaktadır. Bu çalışmada, endüstrileşme ve nüfus artışı sonucunda özellikle 1980'li yıllardan sonra arazi örtüsü değişiminin meydana geldiği Marmara Bölgesi çalışma alanı olarak seçilmiştir. Çalışmanın ilk aşamasında, 2001-2005 tarihleri arasında elde edilen Landsat7 ETM+ görüntüleri radyometrik ve atmosferik olarak düzeltilerek atmosferik parçacıklardan kaynaklanan bozulma etkileri ve sistematik hatalar elemine edilmiştir. Geometrik distorsiyonları elemine etmek, piksel bağıl konum hatalarını düzeltmek ve görüntüleri ortak bir koordinat sisteminde tanımlayabilmek amacıyla her bir görüntü geometrik olarak düzeltilmiştir. Görüntüler farklı yöntemler kullanılarak sınıflandırılmış ve çalışma bölgesi için arazi örtüsü haritası oluşturulmuştur. Oluşturulan arazi örtüsü haritası, GLCC verisi ile kıyaslanarak bu verinin Marmara Bölgesi için doğruluğu araştırılmış ve verideki eksiklikler belirlenmiştir. Çalışmanın sonucunda GLCC veri setinin güncel olmadığı ve Marmara Bölgesinin önemli bir kesimini doğru temsil etmediği tespit edilmiştir. Bölgesel iklim modelleme çalışmaları için, bu veriye alternatif olarak daha doğru ve güncel olan Landsat ETM+ görüntülerinden üretilmiş arazi örtüsü verisinin kullanılabileceği gösterilmiştir. Anahtar Kelimeler: Arazi örtüsü, arazi yüzeyi, bölgesel iklim modelleme, uzaktan algılama, Landsat7 ETM+.In this research, utilization of Landsat7 ETM+ images in regional climate modeling was investigated and the accuracy of Global Land Cover Characterization (GLCC) data set used in regional climate modeling was assessed for the Marmara Region by comparing these data with Landsat ETM+ derived land cover data. Marmara Region was selected as study area because it faced with significant land cover changes as a result of rapid industrialization and population increase especially after 1980s. The region occupies the northwest corner of Turkey with a surface area of 67 000 km² and represents approximately 8.6% of the Turkish national territory. It is the smallest but most densely populated of the seven geographical regions of Turkey. This region includes eleven cities namely Istanbul, Bursa, Kocaeli, Edirne, Balikesir, Kirklareli, Tekirdag, Canakkale, Bilecik, Sakarya and Yalova, where first three cities are industrial and commercial centers of Turkey. Landsat7 ETM+ images obtained between 2001 and 2005 were used to derive land cover data of the Marmara Region. Since 2005 Landsat ETM+ frame includes the significantly changed areas of the Marmara Region like Istanbul and Bursa, it is assumed that created land cover data is representing the year of 2005. In addition to satellite images, forest maps, 1/25 000 scaled topographic maps, ground surveys and photographs were used to assist geometric correction and classification procedure. At the first stage of the research, all images were atmospherically and radiometrically corrected to minimize contamination effects of atmospheric particles (scattering and absorption effects due to the atmosphere) and systematic errors. Then, geometric correction was performed for each image to eliminate geometric distortions, correct errors in the relative positions of pixels, and define images in a common coordinate system. A new approach, semivariograms, was introduced to select appropriate band combinations for studying different land cover classes. After these corrections, images were classified using different classification methods to identify different land cover types. United Stated Geological Survey (USGS) Land Use and Land Cover Classification Legend was used in the study. Several pilot areas were created and classification employed separately for these areas to minimize the spectral mixing of various classes such as barren, crop and urban and increase the classification accuracy. The classification results were aggregated to 1 km to form final land cover data and classification accuracy assessment was performed on final land cover data. At the second stage of the research, 2005 land cover data obtained from Landsat7 ETM+ images was compared with the GLCC data set to analyze the accuracy of these data for the Marmara Region. These data have been used in many regional climate models like Regional Atmospheric Modeling System (RAMS), the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) and Weather Research and Forecasting (WRF). It was obtained from 1-km Advanced Very High Resolution Radiometer satellite images spanning April 1992 through March 1993 with an unsupervised classification technique and accuracy assessment of data set was not performed globally. Land surface is a key determinant in climate system and it controls the partitioning of available energy at the surface between sensible and latent heat, the partitioning of available water between evaporation and runoff. Therefore it must be represented accurately and precisely. Land cover products used in most climate models were initially compiled from maps and ground surveys till the global scale land cover products generated from remote sensing images became avaible. These remotely sensed derived global land cover products like GLCC, University of Maryland land cover classification and Global Land Cover 2000 have been implemented into various land surface schemes and climate models. However, no land cover data set is 100% accurate, even if developed from the most advanced satellite images. The results of comparison analyses between Landsat derived land cover and GLCC show that GLCC data is not up-to-date and have deficiencies and inaccuries in some parts of the Marmara Region. GLCC  is not representing urban areas accurately in İstanbul, Adapazari, Bursa and İzmit. These data also have problems in coastal part of İstanbul European side and show some forest areas as crop areas. This research results show that land cover data obtained from Landsat ETM+ images can be successfully and accurately represent the study region therefore it is an alternative source of up-to-date and accurate land cover data for regional climate modeling. Keywords: Land cover, land surface, regional climate modeling,remote sensing Landsat7 ETM+

Identification of Earthquake Induced Damage Areas Using Fourier Transform and SPOT HRVIR Pan Images

A devastating earthquake with a magnitude of Mw 7.4 occurred on the North Anatolian Fault Zone (NAFZ) of Turkey on August 17, 1999 at 00:01:39 UTC (3:01 a.m. local time). The aim of this study is to propose a new approach to automatically identify earthquake induced damage areas which can provide valuable information to support emergency response and recovery assessment procedures. This research was conducted in the Adapazari inner city, covering a 3 × 3 km area, where 11,373 buildings collapsed as a result of the earthquake. SPOT high resolution visible infrared (HRVIR) Pan images obtained before (25 June 1999) and after (4 October 1999) the earthquake were used in the study. Five steps were employed to conduct the research and these are: (i) geometric and radiometric correction of satellite images, (ii) Fast Fourier Transform (FFT) of pre- and post-earthquake images and filtering the images in frequency domain, (iii) generating difference image using Inverse Fast Fourier Transform (IFFT) pre- and post- earthquake images, (iv) application of level slicing to difference image to identify the earthquake-induced damages, (v) accuracy assessment of the method using ground truth obtained from a 1/5,000 scale damage map. The total accuracy obtained in the research is 80.19 %, illustrating that the proposed method can be successfully used to automatically identify earthquake-induced damage areas

Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes

Soil salinization is one of the severe land-degradation problems due to its adverse effects on land productivity. Each year several hectares of lands are degraded due to primary or secondary soil salinization, and as a result, it is becoming a major economic and environmental concern in different countries. Spatio-temporal mapping of soil salinity is therefore important to support decisionmaking procedures for lessening adverse effects of land degradation due to the salinization. In that sense, satellite-based technologies provide cost effective, fast, qualitative and quantitative spatial information on saline soils. The main objective of this work is to highlight the recent remote sensing (RS) data and methods to assess soil salinity that is a worldwide problem. In addition, this study indicates potential linkages between salt-affected land and the prevailing climatic conditions of the case study areas being examined. Web of science engine is used for selecting relevant articles. "Soil salinity" is used as the main keyword for finding "articles" that are published from January 1, 2007 up to April 30, 2018. Then, 3 keywords; "remote sensing", "satellite" and "aerial" were used to filter the articles. After that, 100 case studies from 27 different countries were selected. Remote sensing based researches were further overviewed regarding to their location, spatial extent, climate regime, remotely sensed data type, mapping methods, sensing approaches together with the reason of salinity for each case study. In addition, soil salinity mapping methods were examined to present the development of different RS based methods with time. Studies are shown on the Köppen-Geiger climate classification map. Analysis of the map illustrates that 63% of the selected case study areas belong to arid and semi-arid regions. This finding corresponds to soil characteristics of arid regions that are more susceptible to salinization due to extreme temperature, high evaporation rates and low precipitation

Agricultural land abandonment in Bulgaria: a long-term remote sensing perspective, 1950–1980

Agricultural land abandonment is a globally significant threat to the sustenance of economic, ecological, and social balance. Although the driving forces behind it can be multifold and versatile, rural depopulation and urbanization are significant contributors to agricultural land abandonment. In our chosen case study, focusing on two locations, Ruen and Stamboliyski, within the Plovdiv region of Bulgaria, we use aerial photographs and satellite imagery dating from the 1950s until 1980, in connection with official population census data, to assess the magnitude of agricultural abandonment for the first time from a remote sensing perspective. We use multi-modal data obtained from historical aerial and satellite images to accurately identify Land Use Land Cover changes. We suggest using the rubber sheeting method for the geometric correction of multi-modal data obtained from aerial photos and Key Hole missions. Our approach helps with precise sub-pixel alignment of related datasets. We implemented an iterative object-based classification approach to accurately map LULC distribution and quantify spatio-temporal changes from historical panchromatic images, which could be applied to similar images of different geographical regions

Land use and land cover mapping using deep learning based segmentation approaches and VHR Worldview-3 images

Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies

Research of the relationship between melatonin and BMAL 1 proteins after cerebral ischemia

Canlılar çevrelerinde meydan gelen değişikliklere adapte olabilmek için davranışsal ve fizyolojik süreçlerini düzenlerler. Canlılardaki bu süreçler sirkadyen ritimle düzenlenmekte ve çevresel değişikliklere senkronize olmaktadır. Bu senkronizasyon canlılara bazen avantaj bazen de dezavantaj sağlamaktadır. Dezavantajlarla beraber nörodejeneratif, hastalıklar meydana gelmektedir. Sirkadyen ritme bağlı olarak bireylerde meydana gelen beyin felci vakaları da değişiklik göstermektedir. Buna bağlı olarak beyin felci insidansına bakıldığında insidansın gündüz saatlerinde daha fazla olduğu görülmektedir. Fakat beyin felci ve sirkadyen ritim arasındaki ilişkiden sorumlu mekanizmaların nasıl ilerlediği henüz tam olarak bilinememektedir. Bu tez çalışmasında, beyin felci sonrası sirkadyen ritim proteinlerinden Brain and muscle arly hydrocarbon receptor nuclear antigen-1(Bmal 1)'in melatonin ile ilişkisinin ortaya konulması amaçlanmıştır. Bu amaçla in vivo ortamda beyin felci oluşturulan farelerde ve in vitro ortamda beyin felci modeli oluşturularak i) apoptotik hücre ölümü, ii) nöronal sağkalım, iii) sirkadyen ritimde rol oynayan sinyal iletim yolakları, iv) Akt sinyal iletim yolağındaki değişiklikler ve v) Bmal 1'in ifadesinin arttırılmasıyla hücre içerisinde etkilenen moleküler yolaklar değerlendirilmiştir. İskemik beyin felci modeli uygulanan farelerde iskemi sonrası melatonin tedavisi ile apoptotik hücre sayısının azaldığı, nöronal sağkalım oranının arttığı, Bmal 1, Clock, PerII ve p-Akt protein seviyelerinde artış olduğu; öte yandan Akt sinyal yolağının baskılanması ile bu proteinlerin seviyelerinde azalma olduğu görülmüştür. Elde edilen souçlar Bmal 1proteini ve melatonin arasındaki ilişkinin PI3K/Akt hücresel sağkalım sinyal yolağı üzerinden gerçekleştiğine işaret etmektedir. Çalışmadan elde edilen sonuçlar sirkadyen ritmin beyin felci sonuçları üzerine etkisini moleküler düzeyde göstermektedir ve ileride beyin felcinin patolojik süreçlerinin daha detaylı anlaşılmasına ve beyin felci tedavisinde yeni hedef molekül geliştirilmesine katkıda bulunması beklenmektedir.Living organisms organize their behavioral and physiological processes in order to adapt to the changes that occurs around them. In living organisms, these processes organized by circadian rhythm and synchronized with environmental changes. This synchronizing provide sometimes advantages and sometimes disadvantages. Neurodegenerative, metabolic, cardiovascular diseases consist of disadvantages. Based on the circadian rhythm, cerebral ischemia cases vary among individuals. Therefore the incidence of cerebral ischemia is seen to be higher during daytime hours. However, how these circadian rhythm mechanisms are related with cerebral ischemia is not clear. In this thesis, it is aimed to reveal the relationship between Bmal 1, circadian rhythm protein, and melatonin after cerebral ischemia. For this purpose, i) apoptotic cell death, ii) neuronal survival, iii) signaling pathways that play a role in the circadian rhythm, iv) Akt signaling pathway, v) the effects of overexpression of Bmal 1 protein in vivo and in vitro conditions have been researched in order to understand possible effects of circadian rhythm related protein Bmal 1 on cerebral ischemia. As a result of in vivo and in vitro experiments, it was found that melatonin treatment decreased number of apoptotic cells, increased rate of neuronal survival, and resulted in higher expression levels of circadian rhythm related proteinsBmal 1, Clock, PerII and survival ptotein p-Akt., while suppression of Akt signaling pathway leads to a decreased in the levels of these proteins relative to treatment after cerebral ischemia. According to data, we purpose the relationship between Bmal 1 protein and melatonin is mediated through the PI3K/Akt cellular survival signaling pathway

Investigation of the role of BMAL1 in acute and subacute period traumatic brain injury

Travmatik beyin hasarı, her yıl dünya çapında 50 milyondan fazla bireyi etkilemekle beraber bireylerde kalıcı ve geçici fiziksel etkilerin, nörolojik ve psikolojik sorunların da başlıca nedenlerinden biridir. İnsidansı yüksek olmasına rağmen şu anda Amerikan Gıda ve İlaç Dairesi (FDA) onaylı bir tedavi metodu bulunmamaktadır. Bmal1, sirkadiyen ritmin düzenlemesinde merkezi rol oynayan bir transkripsiyon faktörüdür. Parkinson, beyin felci gibi nörodejeneratif hastalıklarda etkili olan Bmal1'in, beyin hasarı ile meydana gelen mekanizmaların oluşmasını engellemeye yardımcı olduğu düşünülmektir. Bmal1'in travma sonrası meydana gelen moleküler mekanizmalar üzerine etkisi henüz tam aydınlatılamamıştır. Bu tezde Bmal1'in travmatik beyin hasarı patofizyolojisindeki rolünün gösterilmesi amaçlanmıştır. Bu amaçla lentivirüslerle Bmal1 ifadesi arttırılan veya azaltılan farelere soğuk ile indüklenen travmatik beyin hasarı uygulanmıştır. Bmal1 ifadesinin arttılmasının nöronal sağkalımı arttırdığı, apoptotik hücre ölümünü, hasar hacmini ve beyin ödemini azalttığı; Bmal1 ifadesinin azaltılmasının ise nöronal sağkalımı azalttığı, apoptotik hücre ölümünü, hasar hacmini ve beyin ödemini arttırdığı gösterilmiştir. Bmal1'in travmatik beyin hasarı sonrası p-Akt, p-Erk 1/2, p-SAPK/JNK 1/2, Bax, Bcl-XL, eNOS gibi hücresel sağkalımda, stres yanıtlarında, apoptotik mekanizmalarda görev alan ve Gap43, Brevican, Versican gibi aksonal büyüme ile ilgili proteinlere istatiksel anlamlı bir etkisinin olduğu, depresyon, lokomotor aktivite ve motor koordinasyon üzerine ise herhangi istatiksel anlamlı bir etkisi olmadığı gösterilmiştir. Elde edilen sonuçların literatürde lentiviral vektörler aracılığıyla protein ifadesi arttırılmış ve azaltılmış olan Bmal1'in hem akut dönem hem de subakut dönem travmatik beyin hasarı sonrası etkisinin aydınlatılmasına katkı sağlaması beklenilmektedir.Traumatic brain injury affects more than 50 million individuals worldwide each year, and is one of the main causes of permanent and temporary physical effects, neurological and psychological problems in individuals. Despite the incidence is high, there is currently no specific treatment method approved by the US Food and Drug Administration (FDA). Bmal1 is a transcription factor that plays a central role in the regulation of the circadian rhythm. Bmal1 is known to play a role in neurodegenerative diseases such as Parkinson's and stroke, it is thought to help prevent the formation of mechanisms that occur with brain damage. The effect of Bmal1 on the molecular mechanisms that occur after trauma has not yet been fully elucidated. In this thesis, it is aimed to show the role of Bmal1 in the pathophysiology of traumatic brain injury. For this purpose, cold-induced traumatic brain injury was applied to mice which have increasing or decreasing Bmal1 expression induced by lentiviruses. According to the results, increased Bmal1 expression increased neuronal survival, decreased apoptotic cell death, injury volume, and brain edema; it has been observed that decreasing Bmal1 expression decreases neuronal survival, increases apoptotic cell death, injury volume and brain edema. A statistically significant effect of Bmal1 on proteins that play a role in cellular survival, stress responses and apoptotic mechanisms such as p-Akt, p-Erk 1/2, p-SAPK/JNK 1/2, Bax, Bcl-XL, eNOS after traumatic brain injury. It has been shown that it has a statistically significant effect on axonal growth-related proteins such as Gap43, Brevican, Versican, but has no statistically significant effect on depression, locomotor activity and motor coordination. It is expected that the results obtained will contribute to the elucidation of the effect of Bmal1, whose protein expression is increased and decreased by lentiviral vectors in the literature, after both acute and subacute traumatic brain injury

Deep neural network ensembles for remote sensing land cover and land use classification

With the advancement of satellite technology, a considerable amount of very high-resolution imagery has become available to be used for the Land Cover and Land Use (LCLU) classification task aiming to categorize remotely sensed images based on their semantic content. Recently, Deep Neural Networks (DNNs) have been widely used for different applications in the field of remote sensing and they have profound impacts; however, improvement of the generalizability and robustness of the DNNs needs to be progressed further to achieve higher accuracy for a variety of sensing geometries and categories. We address this problem by deploying three different Deep Neural Network Ensemble (DNNE) methods and creating a comparative analysis for the LCLU classification task. DNNE enables improvement of the performance of DNNs by ensuring the diversity of the models that are combined. Thus, enhances the generalizability of the models and produces more robust and generalizable outcomes for LCLU classification tasks. The experimental results on NWPU-RESISC45 and AID datasets demonstrate that utilizing the aggregated information from multiple DNNs leads to an increase in classification performance, achieves state-of-the-art, and promotes researchers to make use of DNNE