Assessment of Drought in Grasslands: Spatio – Temporal Analyses of Soil Moisture and Extreme Climate Effects in Southwestern Mongolia

Soil moisture plays an essential key role in the assessment of hydrological and meteorological droughts that may affect a wide area of the natural grassland and the groundwater resource. The surface soil moisture distribution as a function of time and space is highly relevant for hydrological, ecological, and agricultural applications, especially in water-limited or drought-prone regions. However, gauging soil moisture is challenging because of its high variability. While point-scale in-situ measurements are scarce, the remote sensing tools remain the only practical means to obtain regional and global-scale soil moisture estimates. A Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission ever designed to gauge the Earth’s surface soil moisture (SM) at the near-daily time scales. This work aims to evaluate the spatial and temporal patterns of SMOS soil moisture, determine the effect of the climate extremes on the vegetation growth cycle, and demonstrate the feasibility of using our drought model (GDI) the Gobi Drought Index. The GDI is based on the combination of SMOS soil moisture and several products from the MODIS satellite. We used this index for hydro-meteorological drought monitoring in Southwestern Mongolia. Firstly, we validated bias-corrected SMOS soil moisture for Mongolia by the in-situ soil moisture observations 2000 to 2015. Validation shows satisfactory results for assessing drought and water-stress conditions in the grasslands of Mongolia. The correlation analysis between SMOS and Normalized Difference Vegetation Index (NDVI) index in the various ecosystems shows a high correlation between the bias-corrected, monthly-averaged SMOS and NDVI data (R2 > 0.81). Further analysis of the SMOS and in situ SM data revealed a good match between spatial SM distribution and the rainfall events over Southwestern Mongolia. For example, during dry 2015, SM was decreased by approximately 30% across the forest-steppe and steppe areas. We also notice that both NDVI and rainfall can be used as indicators for grassland monitoring in Mongolia. The second part of this research, analyzed several dzud (specific type of climate winter disaster) events (2000, 2001, 2002, and 2010) related to drought, to comprehend the spatial and temporal variability of vegetation conditions in the Gobi region of Mongolia. We determined how these extreme climatic events affect vegetation cover and local grazing conditions using the seasonal aridity index (aAIZ), NDVI, and livestock mortality data. The NDVI is used as an indicator of vegetation activity and growth. Its spatial and temporal pattern is expected to reflect the changes in surface vegetation density and status induced by water-deficit conditions. The Gobi steppe areas showed the highest degree of vulnerability to climate, with a drastic decline of grassland in arid areas. We found that under certain dzud conditions, rapid regeneration of vegetation can occur. A thick snow layer acting as a water reservoir combined with high livestock losses can lead to an increase of the maximum August NDVI. The snowy winters can cause a 10 to 20-day early peak in NDVI and the following increase in vegetation growth. However, during a year with dry winter conditions, the vegetation growth phase begins later due to water deficiency and the entire year has a weaker vegetation growth. Generally, livestock loss and the reduction of grazing pressure was played a crucial role in vegetation recovery after extreme climatic events in Mongolia. At the last stage of our study, we develop an integrated Gobi drought index (GDI), derived from SMOS and LST, PET, and NDVI MODIS products. GDI can incorporate both, the meteorological and soil moisture drought patterns and sufficiently well represent overall drought conditions in the arid lands. Specifically, the monthly GDI and 1-month standardized precipitation index SPI showed significant correlations. Both of them are useful for drought monitoring in semi-arid lands. But, the SPI requires in situ data that are sparse, while the GDI is free from the meteorological network restriction. Consequently, we compared the GDI with other drought indices (VSWI, NDDI, NDWI, and in-situ SM). Comparison of these drought indices with the GDI allowed assessing the droughts’ behavior from different angles and quantified better their intensity. The GDI maps at fine-scale (< 1km) permit extending the applicability of our drought model to regional and local studies. These maps were generated from 2000 to 2018 across Southwestern Mongolia. Fine-scale GDI drought maps are currently limited to the whole territory for Mongolia but the algorithm is dynamic and can be transported to any region. The GDI drought index can be served as a useful tool for prevention services to detect extremely dry soil and vegetation conditions posing a risk of drought and groundwater resource depletion. It was able to detect the drought events that were underestimated by the National Drought Watch System in Mongolia. In summary, with the help of satellite, climatological, and geophysical data, the integrated GDI can be beneficial for vegetation drought stress characterization and can be a useful tool to monitor the effectiveness of pasture land restoration management practices for Mongolian livelihoods. The future application of the GDI can be extended to monitor potential impacts on water resources and agriculture in Mongolia, which have been impacted by long periods of drought

Using Long Time Series of Satellite Remote Sensing Data to Assess the Impact of Climate and Anthropogenic Changes in the Mesopotamian Marshes, Iraq

In the recent past, the Mesopotamia region has been rich in all forms of biological diversity, characterized by a fertile living environment and natural habitats full of rare birds, wild animals, aquatic animals, and diverse plants. Its natural abundance and geographical location have allowed it to be break or transit point for millions of migratory birds from Russia to South Africa. It is a breeding ground for many species of Persian Gulf fish. Despite all this historical, environmental and economic richness, they have been neglected as a result of the combination of a number of human and climatic factors, which in 16 years (1988-2003) has modified them to a land where vegetation, water, and biodiversity have been clearly reduced. This is a great environmental loss, not only for West Asia but for the whole world. This dissertation explores the changes in the vegetation coverage and water bodies in the Mesopotamian marshes, Iraq over more than three decades (36 years) using different sources of satellite remote sensing datasets. Firstly, we utilized Normalized Difference Vegetation Index (NDVI) from the Land Long Term Data Record (LTDR) Version 5 which has a 0.05o x 0.05o in spatial resolution and daily temporal repeat to monitor the fluctuations of vegetation together with hydrological variables such precipitation, surface temperature, and evapotranspiration. In this research, we studied the impact of climate change and anthropogenic activities on vegetation and water coverage changes. Secondly, we compared Normalized Difference Vegetation Index from various satellite sensors - Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High-Resolution Radiometer (AVHRR), and Landsat over the Mesopotamian marshlands for 17 years. We selected this time series (2002-2018) to monitor the changes in vegetation area. The time series (2002-2018) is considered as a period of rehabilitation for the Mesopotamian marshes. Thirdly, as a result of human factors and local and regional climate changes, the marshes and Iraq are in general vulnerable to face a large number of dust storms annually. According to local sources (Iraq news) and National Aeronautics and Space Administration, the time period from June 29 to July 8, 2009, is considered the longest dust storm period in Iraq during last decade. In this research, we utilized the Moderate Resolution Imagining Spectroradiometer, surface reflectance daily data to calculate the Normalized Difference Dust Index. Additionally, brightness temperature data from Aqua thermal band 31 were used to separate sand on the ground from atmospheric dust. The main reasons for the degradation of the Mesopotamian marshes were due to anthropogenic activities. In the comparison research, we found that the NDVI derived from MODIS, AVHRR and Landsat sensors are correlated with high precision. This paper investigates the utility of combining low spatial resolution with frequent temporal repeat and long-term coverage and a high spatial resolution with infrequent temporal repeat and similar long-term coverage. This study also proves that we can use the low-resolution Advance Very High- resolution Radiometer data for studies on land cover change

Análisis multitemporal del NDDI, comparación con el NDWI para determinar la sequía en la Reserva Nacional de Tumbes, Perú, 1986 - 2019

La presente investigación se realizó en la reserva nacional de Tumbes (RNTumbes) con la finalidad de determinar la existencia de sequía, así como las áreas que están bajo impacto desde sequías moderadas a sequías con comportamiento anormal. El objetivo del presente trabajo fue comparar los índices de sequía NDWI y NDDI y elaborar mapas tematicos del área en estudio. Se obtuvieron imágenes del servicio geológico de los Estados Unidos (USGS), seleccionadas desde el año 1986 al año 2019. Para la determinación de la sequía se utilizó el NDWI y el NDDI. Para el NDWI se determinaron las áreas para analizar el comportamiento de la sequía y se determinó el NDDI para su comparación con el NDWI. Los resultados de este estudio permiten concluir en: a) Con respecto a la sequía el NDWI, tuvo valores que oscilaron entre -0,5 a 0,7 en la RNTumbes, clasificándose como una sequía débil y contenido de humedad bajo; y b) El NDDI en la Reserva Nacional de Tumbes alcanzó valores entre -1189,04 y 1312,02, permitiendo una clasificación como suelos húmedos, aunque no están a su máxima capacidad de almacenaje de agua durante los años del presente análisis multitemporal.

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days

Assessing Drought Conditions using NDVI, Land Surface Temperature and Precipitation in Amathole District Municipality, Eastern Cape, Province, South Africa

The world is faced with unprecedented environmental changes, which can be linked to population growth, and economic development. Several studies have indicated that these changes are likely to accelerate in the future and cause adverse impact on the environment. To this end, the Eastern Cape Province and in particular the Amathole District Municipality (ADM) has recorded high number of climate change related disasters such as prolonged drought conditions witnessed during the winter season of 2008, 2009, 2014 and 2015 among others. To this end, this study aimed to use remote sensing imagery to assess and document drought occurrences in the ADM from 2007 to 2017. To accomplish the aim, the Normalized Difference Vegetation Index, Land Surface Temperature and Precipitation were explored to assess drought spatiotemporal occurrences. To assess the relationship between abovementioned variables, the Pearson’s correlation was used. For the analysis a total of 396 satellite imagery (MODIS NDVI and Land Surface Temperature as well as TRMM precipitation) were used. The study results revealed that different correlations exist between the three variables. The strength of correlations differed by season. Furthermore, it was revealed that the drought conditions in the district differed in the spatial distribution. The study accurately identified the drought episodes which occurred in the ADM in the years 2008, 2009, 2014, 2015 and 2016. The chosen methodology and variables proved to be suitable for analysing drought conditions offering space and temporal variation dimension, which is vital in monitoring disasters such as drought.Thesis (MSc) (Geography) -- University of Fort Hare, 202

Assessing Drought Conditions using NDVI, Land Surface Temperature and Precipitation in Amathole District Municipality, Eastern Cape, Province, South Africa

The world is faced with unprecedented environmental changes, which can be linked to population growth, and economic development. Several studies have indicated that these changes are likely to accelerate in the future and cause adverse impact on the environment. To this end, the Eastern Cape Province and in particular the Amathole District Municipality (ADM) has recorded high number of climate change related disasters such as prolonged drought conditions witnessed during the winter season of 2008, 2009, 2014 and 2015 among others. To this end, this study aimed to use remote sensing imagery to assess and document drought occurrences in the ADM from 2007 to 2017. To accomplish the aim, the Normalized Difference Vegetation Index, Land Surface Temperature and Precipitation were explored to assess drought spatiotemporal occurrences. To assess the relationship between abovementioned variables, the Pearson’s correlation was used. For the analysis a total of 396 satellite imagery (MODIS NDVI and Land Surface Temperature as well as TRMM precipitation) were used. The study results revealed that different correlations exist between the three variables. The strength of correlations differed by season. Furthermore, it was revealed that the drought conditions in the district differed in the spatial distribution. The study accurately identified the drought episodes which occurred in the ADM in the years 2008, 2009, 2014, 2015 and 2016. The chosen methodology and variables proved to be suitable for analysing drought conditions offering space and temporal variation dimension, which is vital in monitoring disasters such as drought.Thesis (MSc) (Geography) -- University of Fort Hare, 202

Spatial and temporal analysis of dust storms in Saudi Arabia and associated impacts, using Geographic Information Systems and remote sensing

Dust storm events occur in arid and semi-arid areas around the world. These result from strong surface winds and blow dust and sand from loose, dry soil surfaces into the atmosphere. Such events can have damaging effects on human health, environment, infrastructure and transport. In the first section of this PhD dissertation, focus on the suitability of the existing of five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula are examined. These are the: (a) Normalized Difference Dust Index (NDDI); (b) Brightness Temperature Difference (BTD) (Band 31–32); (c) BTD (Band 20–31); (d) Middle East Dust Index (MEDI) and (e) Reflective Solar Band (RSB). This work also develops dust detection thresholds for each index by comparing observed values for ‘dust-present’ versus ‘dust-free’ conditions, taking into account various land cover settings and analysing associated temporal trends. The results suggest the most suitable indices for identifying dust storms over different land cover types across the Arabian Peninsula are BTD31–32 and the RSB index. Methods such as NDDI and BTD20 – 31 have limitations in detecting dust over multiple land-cover types. In addition, MEDI was found to be an unsuccessful index for detecting dust storms over all types of land cover in the study area. Furthermore, this thesis explores the spatial and temporal variations of dust storms by using monthly meteorological data from 27 observation stations across Saudi Arabia during the period (2000–2016), considering the associations between dust storm frequency and temperature, precipitation and wind variables. In terms of the frequency of dust in Saudi Arabia, the results show significant spatial, seasonal and inter-annual. In the eastern part of the study area, for example, dust storm events have increased over time, especially in Al-Ahsa. There are evident relationships (p < 0.005) between dust storm occurrence and wind speed, wind direction and precipitation. This thesis also describes the impact of dust on health, and specifically on respiratory admissions to King Fahad Medical City (KFMC) for the period (February 2015 – January 2016).This study uses dust data from the World Meteorological Or-ganization (WMO) for comparing and analysing the daily weather conditions and hospital admissions. The findings indicate that the total number of emergency respiratory admissions during dust events was higher than background levels by 36% per day on average. Numbers of admissions during ‘widespread dust’ events were 19.62% per day higher than during periods of ‘blowing dust’ activity. The average number of hospital admissions for lower respiratory tract infections (LRTI) was 11.62 per day during widespread dust events and 10.36 per day during blowing dust. The average number of hospital admissions for upper respiratory tract infections (URTI) was 10.25 per day during widespread dust events and 7.87 per day during blowing dust ones. I found clear seasonal variability with a peak in the number of emergency admissions during the months of February to April. Furthermore, qualitative evidence suggests that there is a significant impact on hospital operations due to the increase in patients and pressure on staffing and hospital consumables in this period. Taken together, these findings suggest the (BTD 31–32) and (RSB) are the most suitable indices of the five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula and over different land cover. There are important spatial and temporal pattern variations, as well as seasonal and inter-annual variability, in the occurrence of dust storms in Saudi Arabia. There is also a seasonal pat-tern to the number of hospital admissions during dust events. This is research in-tended to fill the knowledge gap in the dust detection filed. Here I address the knowledge gap by evaluating the identified dust methods over the whole Arabian Peninsula and by considering different land cover. To my knowledge, this is the first study analysed the temporal trends in indices values considering dust and dust-free conditions. Previous work has only focused on 13 stations for analysing dust storms over Saudi Arabia. Therefore, this study has analysed the seasonal and inter-annual and spatial variation by using data from 27 observations in Saudi Arabia. This study addresses the relationship between dust storm frequency and the three meteorological factors (i.e. temperature, precipitation and wind variables) which have not yet been clarified in previous studies. In addition, this research fills the gap in the literature by investigating the correlation between different types of dust events such as (wide-spread dust and blowing dust) and their effects on the hospital admissions for upper and lower respiratory tract issues for pediatric in Riyadh city

Análisis multitemporal de la sequía y la deforestación y su influencia en la degradación de la Reserva Nacional de Tumbes - Perú

La presente investigación se realizó en la reserva nacional de Tumbes(RNTumbes), con la finalidad de observar la sequía , degradación, el grado de deforestación en esta reserva, se emplearon imágenes de diferentes años desde el año 1986 al año 2019, las imágenes fueron seleccionadas, algunas imágenes obtenidas de Landsat 7, fueron corregidas mediante la herramienta gap fill, para determinar la sequía se utilizó el NDDI (Índice de sequía diferencial normalizado), para determinar la vegetación forestal y la degradación se utilizó el NDVI (Índice de vegetación diferencial normalizado), se determinó las áreas para poder analizar la deforestación, se logró concluir que: a)Con respecto a la sequía el NDWI, la mayor área de la RNTumbes, tuvo valores entre 0.2-0.4 y >0.4, clasificándose el área con poca sequía y bajo contenido de humedad, respectivamente; valores de humedad que están dentro de los rangos establecidos para este índice b) que la Vegetación alta(VA), fue mayor el año 1986 alcanzando 19 142,28 ha, y los valores más bajos fueron en el año 2005 y 2010, donde el área fue de 15 401,25 y 15 094,53 ha, respectivamente; el año 2019 el área fue de 18 219,51 ha, la cual se recuperó en los últimos diez (10) años y c) El NDDI en la reserva nacional de Tumbes alcanzó valores entre -1189.04 y 1 312,02, siendo estos valores clasificados como suelos húmedos aunque no están a su máxima capacidad de almacenaje de agua, lo cual nos permite decir que en la reserva no se presentó una situación de sequía durante los años del presente análisis multitemporal como se observa en ambos índices como el NDWI y el NDDI

Multitemporal Change Detection On Urmia Lake And Its Catchment Area using Remote Sensing And Geographical Information Systems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2014Thesis (M.Sc.) -- İstanbul Technical University, Instıtute of Science and Technology, 2014Göllerde ve barajlarda bulunan su rezervleri ve bu rezervlerin izlenmesi uzun yıllardır, yerel ve küresel ölçekte en önemli çevresel konulardan biri olmuştur. Su kaynakları ve havzalarındaki değişimlerin izlenmesi, bu kaynakların yönetimi ve doğru kullanımı açısından gereklidir. Su kaynakları ve özellikle göller, küresel ısınma, kuraklık ve artan dünya nüfusunun beraberinde getirdiği insan gereksinimleri nedeniyle önem kazanmaktadır. İnsan gereksinimleri için (içme suyu gibi) kaynak sağlaması dışında, bir göldeki su rezervi, Urmiye Gölü örneğinde olduğu gibi, bir ülkenin ekonomisine katkı sağlayan önemli bir kaynak da olabilmektedir. Klasik yöntemler kullanılarak göllerde yapılan ölçümler genellikle noktasal bazlı olup, küçük çalışma alanları ile sınırlı kalmaktadır. Bu durum göz önünde bulundurulduğunda, uzaktan algılama teknikleri özellikle geniş alanlara yönelik farklı parametreler ile bilgi ve haritalar üretilmesine imkan sağladığı için su kaynaklarının izlenmesi gibi pek çok farklı çalışmada kullanılmaktadır. Bu projenin amacı; uydu görüntüleri, saha ölçümleri ve meteorolojik verileri kullanarak Uzaktan Algılama ve CBS yöntemleriyle Urmiye Gölü ve civarında olan zamansal değişimleri analiz etmektir. Buna ek olarak göldeki değişimlerin olası  nedenlerini incelemek, meteorolojik parametrelerin zamansal analizlerini yapmak ve gölün kurumasını engellemeye yönelik bilimsel öneriler ortaya koymaktır. Urmiye Gölü, İran’ın kuzey batısında, Batı Azerbaycan ve Doğu Azerbaycan arasında yer almaktadır (N 37.5° E 45.5°). İran’ın en büyük içgölü olan Urmiye Gölü dünyada Lut Gölü’nden sonra aşırı tuzluluk oranına sahip ikinci göldür. Aynı zamanda bu göl özel bir canlı türü olan Urmiye Artemia’ya da ev sahipliği yapmaktadır. Artemia dünya çapında bilinen ve tuz göllerinde bulunan bir zooplanktonik organizmadır. Bu çalışmada yapılan analizlere göre, Urmiye gölü, 1995 yılında yaklaşık 5982 km² yüzey alana sahipken 2013 yılında yaklaşık 1852 km² yüzey alana kadar düşen göl, deniz seviyesinden 1250 m yükseklikte ve en fazla 16-20 m derinliğe sahip olup ortalama derinliği ise 6 m’dir. 1995 yılında eni max. 60 km., boyu ise max. 150 km. olarak ölçülmüştür. Göl 15 km uzunluğunda toprak yol ile kuzey ve güney olmak üzere iki parçaya ayrılmıştır. Bu yolun ortası 1500 m uzunluğunda bir köprü ile bağlanmış, köprü altından, bu iki bölüm arasındaki su geçişi sağlanmıştır. Urmiye Gölü havzası 51876 km² alana sahiptir. Havzada 80.000 adetten fazla kuyunun bulunması, birçok barajın kurulmuş olması, sıcaklık ve yağmur değişimleri, ve kuraklık gibi nedenlerden dolayı tuzluluk oranı, son yıllarda göl havzasını tehdit edecek şekilde artmıştır. Yapılan bu çalışmada son otuz yıl içerisinde gölün yaklaşık %70’inin kurumuş olduğu tespit edilmiştir. Göl ve havzasında meydana gelen doğal ve yapay değişikliklerin etkilerini iki önemli nokta ile açıklamak mümkündür. İlk olarak, gölü besleyen akarsular üzerinde özellikle 2000 yılı sonrasında kurulmuş olan çok sayıda baraj, göle akarsular tarafından taşınan su miktarını azaltmıştır. Ayrıca, göl havzasında bulunan çok sayıda kuyu ve bu kuyulardan özellikle tarımsal sulama amaçlı çekilen sular, yeraltı su seviyesinde değişimlere neden olmuştur. Göl çevresindeki istasyonlardan elde edilen meteorolojik veriler incelendiğinde ise sıcaklık artışı ve yağış azalması gözlemlenmiştir. Bazı araştırmalarda bu değişimlerin küresel ısınmadan kaynaklı olduğu belirtilmiş olmasına rağmen, göl ve çevresindeki insan kaynaklı müdahalelerin de bu değişimler üzerinde etkisi olduğu gözardı edilmemelidir. Belirtilen nedenler dolayısıyla göldeki su seviyesi ve yüzey alanı azalmaktadır. Bu durum göl suyunun tuzluluk oranının artmasına neden olmaktadır. Değişen koşullar nedeni ile Urmiye gölü flamingo gibi binlerce göçmen kuşa ve Urmiye Artemia’si gibi özel türlere artık ev sahipliği yapamaz hale gelmektedir. İkinci olarak, Urmiye Gölü’nde oluşan kuraklık, İran başta olmak üzere göl çevresinde yer alan ülkelerde ekosistem ve insan hayatı için tehlike yaratmaktadır. Urmiye Gölü’nün kuruması ile oluşan iklim değişiklikleri, insan ve doğal hayat üzerinde hastalık ve göç gibi olumsuz olaylara neden olmaktadır. Benzer problem ile Aral Denizi de karşı karşıya kalmış olup bu göl için gerekli tedbirlerin alınmamış olması nedeniyle gölün büyük kısmı artık kullanılamaz haldedir. Bu durum Aral Deniz'i ve çevresindeki ülkeler için önemli bir çevresel sorun haline gelmiştir. Aral Denizi örneği dikkate alınarak benzer problemlerin yaşanmaması adına Urmiye Gölünün koruma altına alınması  son derece önemlidir. Bu çalışma esas olarak Uzaktan Algılama ve Coğrafi Bilgi Sistemlerinin (CBS) entegrasyonu ile Urmiye Gölü’ndeki 1984 ve 2014 yılları arasındaki otuz yıl içerisinde zamansal değişimleri belirlemeyi hedeflemektedir. Uydu görüntüleri, meteorolojik veriler, GPS ölçümleri, barajlar, yeralti su kaynakları, nüfus değişikliği ve arazi kullanım haritaları Urmiye Gölü’ndeki değişimleri tespit etmek amacı ile kullanılmıştır.  Çalışmada 1984 yılı ve 2014 yılı arasında elde edilen toplam 95 uydu görüntüsüyle Urmiye Gölü yakınlarında kurulmuş olan 20 sinoptik meteorolojik istasyonun kaydettiği sıcaklık, yağış, ve nem gibi farklı meteorolojik veriler temin edilerek kullanılmıştır. Bu verilere ek olarak, Batı Azerbaycan ve Doğu Azerbaycan bölgelerine ait arazi kullanım haritaları aracılığıyla, nüfus, yeraltı su kaynakları ve barajlar gölün durumunun genel değerlendirilmesi için kullanılmıştır. Çalışmanın ilk aşamasında uydu görüntüsü olarak kullanılan veri seti oluşturulmuştur. USGS arşivindeki Landsat-4, -5 TM ve Landsat-8 uydularına ait farklı yılların aynı aylarında ve mevsimlerinde elde edilmiş, düşük bulut etkisi gözlenen en iyi verilerin olduğu görüntüler seçilmiştir. Daha sonra1984-yaz, 1987-bahar, 1987-yaz, 1990-yaz, 1995-yaz, 1998-bahar, 1998-yaz, 2000-yaz, 2006-yaz, 2007-bahar, 2007-yaz, 2009-yaz, 2010-yaz, ve 2011-yaz görüntülerini içeren Landsat-5 TM uydu verileri ile 2013-bahar, 2013-yaz, ve 2014-kış Landsat-8 verileri ve 2011-bahar, ve 2012-yaz mevsimlerini içeren DMC verileri seçilerek veri seti oluşturulmuştur. Sonuç olarak, toplamda 1984-2014 yılları arasında 95 adet uydu görüntüsü ile çalışılmıştır. Görüntü ön işlemenin ilk aşamasında Landsat-5 TM uydusunun 1, 2, 3, 4, 5, 7 bantları ve Landsat-8 uydusunun 1, 2, 3, 4, 5, 6, 7 bantları birleştirerek görüntü oluşturulmuştur. İkinci aşamada görüntülerde piksellerin parlaklık değerlerinde meydana gelen hatalar ve atmosferik koşullardan meydana gelen bulut etkisini düşürmek için radyometrik ve atmosferik düzeltmeler yapılmıştır.  Görüntü ön işlemesi bittikten sonra, çalışma alanını kapsamak için 6 görüntü mozaiklenmiş ve alanı kapsayan tek bir görüntü oluşturulmuştur. Bu çalışmadaki amaçlardan bir tanesi Urmiye gölünün yüzey alanında meydana gelen değişikliklerin belirlenmesi için en uygun ve en doğru yöntemi ortaya koymaktır. Bu amaçla, görüntüler kontrollü ve kontrolsüz sınıflandırma yöntemleri kullanılarak sınıfandırılmış ve son 30 yıllık periyotta göl ve çevresinde meydana gelen değişimler karşılaştırılmıştır. Yapılan Doğruluk analizlerine göre kontrollü sınıflandırma ile daha iyi sonuçlar elde edilmiştir. Bu nedenle gölün yüzeyinde meydana gelen değişimlerin tespiti için kontrollü sınıflandırma sonuçları kullanılmıştır. Bu sonuçlara göre gölün su yüzey alanı 1995 yılında yaklaşık 5982 km² iken 2013 yılında yaklaşık 1852 km² olarak hesaplanmıştır. Aynı zamanda bu çalışmada su yüzey alanını, kıyı boyunca su kütlelerini ve sulu olmayan kütleleri ayırmak için NDVI (Normalized Difference Vegetation Index) ve MNDWI (Modified Normalized Difference Water Index) kullanılmıştır ve bu indislerden elde edilen sonuçlar kıyaslanmıştır. Gölün su yüzey alanı 1995 ve 2006 yıllar arasında yaklaşık 2000 km² azalmıştır, bu %32 oranında bir kurumanın meydana geldiğini göstermektedir. Bu tarihten sonra, 2006 ve 2013 yıllar arasında da gölün su yüzey alanı 2000 km² azalmıştır ve kontrollü sınıflandırma sonuçlarına gore gölün su yüzey alanı 2013 yılında 1853 km² bulunmuştur. Urmiye gölünün havzasında olan değişimleri tespit etmek için NDVI (Normalize Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDSI (Normalized Differential Salinity Index), SI (Salinity Index) ve NDDI (Normalized Difference Drought Index) kullanılmıştır. Bulunan Sonuçlara göre 2006 yılı, 30 yıllık periyotta yüksek toprak tuzluluğu, en az NDVI, en az NDWI ve en şiddetli kuraklığa sahip olan yıldır.  2006 yılının tersine 1987 yılı düşük toprak tuzluluğu, yüksek NDVI, yüksek NDWI ve az kuraklığa sahip bir yıl olmuştur.  Meteorolojik verilerin analizine göre 2006 ve 2010 yılları, son yıllarin en sıcak yılları olmasına rağmen, bu yıllara ait olan yağış grafiklerine bakıldığında, son yıllara göre yüksek miktarda yağış artışı gözükmektedir. Jeoistatistik analizi ve SPI (Standard Preicitation Index) sonuçlarını dikkate alındığında 1999 ve 2010 yılları arasında kuraklık gözlemlenmekte fakat bu kuraklık yılların hepsini kapsamamaktadır. Örnek olarak 2003, 2004 ve 2007 yıllarında kuraklık tespit edilmemiştir.  Gölün havzasında bulunan su kaynaklarına göre , Kuzey ve Güney Azerbeycan da toplam 103 tane baraj bulunmaktadır. Bu barajlardan 56 tanesi Urmiye Gölü havzasında yer almaktadır. Bu barajların 14 tanesi 1970-1990, ve 10 tanesi 1990-2000 ve 32 tanesi ise 2000-2014 yılları arasında inşa edilmiştir. Bu barajlar, Urmiye gölü havzasında tarım alanlarının geliştirilmesinde önemli bir rol oynamaktadır.  1999 yılında 102966 hektar tarım alanı varken 2013 yılında tarım alanları 192648 hektara kadar ulaşmıştır. 2013 istatistiklerine göre Urmiye Gölü havzasındaki barajların yıllık taşıdığı toplam su miktarı 2060.30 milyon metreküp olup bunların 1320.28 milyon metreküpü yalnızca tarım faaliyetleri için kullanılmaktadır. 2013 yılında içme suyu tüketimi ise 389.04 milyon metreküptür. Bölgede 1985 yılından 2010’a yılına kadar nüfus 1.800.000 artış göstermiş ve buna bağlı olarak İrandaki su tüketimi dünya standartlarına oranla 2 kat artmıştır. Yeraltı suları, tarım arazileri için diğer temel su kaynağıdır. Yer altı sularının çekilme miktarı, 1984-1985 yılları arasında 1534 milyon metreküpken, 2011-2012 yılları arasında 2156 milyon metreküptür. Örnek olarak yalnızca 1998-1999 yılları arasında yer altı suları çekilmesi 400 milyon metreküp artmıştır. Ulaşılabilir kaynaklar doğrultusunda 2012 yılında Urmiye Gölü havzasında toplam 74336 adet orta-derin kuyu ve 8047 adet derin kuyu bulunmaktadır. Sonuç olarak, gölün giriş suyunu temin eden kaynaklara baktığımızda, gölün havzasında olan Cığatı (Zarrinerood), Tatau (Siminerood), Soyuk Bulak Çay (Mahabad), Gadar Çay, Baranduz Çay, Şehir Çay, Roze Çay, Nazlı Çay, Zola Çay, Tesuc Çay, Acı Çay, ve Sufi Çay Urmiye Gölü’nün yaklaşık %75 giriş suyunu sağlamaktadır. Kalan %25 giriş suyu yağış, yeraltı suları ve diğer kaynaklara bağlıdır. Urmiye Gölü havzasında nüfusun artması, çok sayıda barajın yapılması, yeraltı sularının çekilmesi ve tarımsal arazının çoğalması göz önüne alındığında bölgede meydana gelen değişikliklerde iklim etkisinden daha çok insan etkisi olduğu tespit edilmiştir. Urmiye Gölü ve havzasında değişimlerin takibi için başarılı bir izleme sistemi kurulması noktasında Uzaktan algılama ve CBS entegrasyonu büyük bir önem taşımaktadır.  Bu çalışmada DMC uydu görüntüleri, İstanbul Teknik Üniversitesi (İTÜ BAP: 37016) tarafından sağlanmıştır. Landsat görüntüleri Amerika Birleşik Devletleri Jeolojik Araştırmalar sitesinin veritabanından indirilmiştir. Ayrıca meteorolojik veriler Batı Azerbaycan Meteoroloji Dairesi ve Doğu Azerbaycan Meteoroloji Dairesi’nden temin edilirken arazi haritaları da Ulusal Kartoğrafya Merkezi'nden alınmıştır. Bu çalışmada ERDAS IMAGINE 2011 ve 2013, Arcgis 10 ve 10.1,  Envi 5 ve SPI_SL_6.exe programları kullanılmıştır.Different types of environmental sources, especially water bodies play a crucial role in human life and economy. Nowadays, the significance of water bodies, especially fresh water sources like lakes is increasing since these sources are being threatened due to global warming, drought and human needs. In addition to serving as supply for human needs such as irrigation and drinking water, a water reserve in a lake and its catchment area can also be important source contributing to country’s economy and policy like the case of Urmia Lake in Iran. Urmia Lake is located in the northwest of Iran between West Azerbaijan and East Azerbaijan provinces (N 37.5° E 45.5°). Its catchment area is about 51876 km² and it is the largest inland lake of Iran and the second largest hypersaline lake in the world after Dead Sea and the habitat of Artemia Urmiana which is a unique bisexual Artemia Species. The brine shrimp Artemia is a zooplanktonic organism found in hypersaline habitats such as inland salt lakes, coastal salt pans and manmade saltworks worldwide.  Urmia Lake is divided into 2 parts including north and south parts separated by a causeway which has about 1500 m bridge allows a little water exchange between 2 parts. Due to the establishment of different dams on contrary rivers which supply Urmia Lake’s water, establishment of more than 80,000 wells in Urmia Lake’s catchment area, increased demands for irrigation in the Lake’s basin, temperature and precipitation changes, and drought, the salinity of the lake has risen remarkable during recent years, and about 70% of the lake’s area is drought. There are two important points that should be emphasized for the temperature and precipitation changes impacts on Urmia Lake and its vicinity.  Firstly, the annual amount of water the lake receives has significantly decreased as a result of establishment of dams, wells, and drought. This in turn has increased the salinity of the lake’s water, lowering the lake viability as home to thousands of migratory birds including the large flamingo populations and diminishing other assets especially Artemia Urmiana.  Secondly, it is also important to consider the results of drying Urmia Lake and its risks on human life and ecosystem in Iran and neighbor countries of Urmia Lake. Drying of Urmia Lake will impact the local and regional climate of the area and this will have severe impacts on human and environment. Hotter temperature values and water shortage as a result of complete drying of Urmia Lake may even cause diseases and migration of local people. A similar example to Urmia Lake case is Aral Sea and its vicinity, therefore lessons learned from the Aral Sea case should be taken into account for the protection of Urmia Lake.  This study focuses mainly on multi-temporal change detection on Urmia Lake and its catchment area by integration of remote sensing and geographical information systems for a thirty year period from 1984 to 2014. In addition to satellite images, meteorological data, GPS measurements, landuse maps and ground photographs were analyzed to investigate the changes on Urmia Lake and understand the causes of this environmental problem including the role and effects of  human and global warming. A total number of 95 Landsat-5 TM, Landsat-8, and DMC images obtained between 1984 and 2014 were used in this study. Also, different meteorological variables like temperature, precipitation, humidity which has been measured at 20 synoptic stations around Urmia Lake were used to interpret meteorological impacts during last years. Moreover, data collected from different sources like Landuse maps of West Azerbaijan and East Azerbaijan provinces, control points, population, dams, underground water resources were used in this study to analyze the human and climate induced impacts on drying of Urmia Lake. After preprocessing steps, 6 frames, which have taken between 1984 and 2014 are mosaiced to output study area including Urmia Lake and its catchment area. Then, Unsupervised classification and supervised classifications were done on output information and compare the changes which have been occurring during the last 30 years. According to the results of the accuracy assessment process, the overall classification accuracy and overall Kappa statistics using the supervised classification method were shown to be better than the unsupervised classification for every time period except the summer of 2011. Therefore, to analyze the water surface area of Urmia Lake using supervised classification was determined to be better than unsupervised classification. The minimum and maximum water surface areas are about 1852 (2013) and 5982 (1995) km². The water surface area of Urmia Lake decreased nearly 2000 km²  from 5982 km² in 1995 to 4058 km²  in 2006. In other words,  32% of Urmia Lake dried up during the period of 1995 until 2006. It then decreased another 2000 km² from 4058 km² in 2006 to 1852 km² in 2013.  To analyze Urmia Lake’s catchment area and change detection in Urmia Lake’s vicinity, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Differential Salinity Index (NDSI), Salinity Index (SI), and Normalized Difference Drought Index (NDDI) are used. According to the results of these indexes, 2006 can be considered as a year with the  highest soil salinity value, least NDVI, least NDWI, and most severe drought conditions. 1987 can be considered as the year with the lowest soil salinity value, highest NDVI, highest NDWI, and least drought condition. The salinity of soil and water bodies has been  increased in all parts of the study area during recent years especially in south and east parts of Urmia Lake. The air temperatures in 2006 and 2010 were the warmest while following years cooled down. In 2006 and 2010 the high temperatures were also years of increased precipitation compared to other years. By considering the results of geostatistical analysis and Standard Precipitation Index (SPI), the meteorological analysis showed changes toward a dry climatic condition from 1999 to 2010 but these changes were not regular and some years like 2003, 2004 and 2007 had normal climatic condition.  There are, in total, 103 dams in the West Azerbaijan and East Azerbaijan provinces. Of these dams 56 are located in Urmia Lake’s catchment area. 14 dams were established between 1970 and 1990, and 10 dams were made from 1990 to 2000, and 32 dams were built from 2000 to 2014. Moreover, there are additional dams which are under construction or in the study stage. These dams play a critical role in developing agriculture areas in Urmia Lake’s catchment area, but it also means an increase in irrigation and water usage. The total cultivation area using dams supplied water was about 102966 Hectare in 1999 and it increased to 192648 Hectare in 2013. Annual adjustable water volumes of all dams in Urmia lake’s catchment area was about 2060.30 million m³ in 2013 while the annual agricultural water consumption was about 1320.28 million m³. According to these statistics, cultivation areas using water supplied from dams doubled from the periods of 1970-1999 until 1999-2013. Underground water sources which include deep wells, semi deep wells, aqueducts, and water fountains are another source that provides needed water for irrigation and agricultural developing.  Discharge water from underground water sources was 1534 million m³ during 1984 to 1985 with an increase to 2156 million m³ during 2011 to 2012. Moreover, discharge water from underground water sources increased by 400 million m³ alone from 1998 to 1999. According to the available statistics from underground water sources between 1972 and 2012, there are totally 74336 semi deep wells and 8047 deep wells in Urmia Lake’s catchment area in 2012.  By considering that rivers Jighati (Zarrinerood), Tatau (Siminerood), Soyugh Bulagh chay (Mahabad), Gadar chay, Baranduz chay, Shahar chay, Roze chay, Nazlu chay, Zola chay, Tasuj chay, Aji chay, and Sufi Chay rivers provide 75% of the inflow water to Urmia Lake while underground water sources, precipitation, and flood water provide 25% of the water inflow. When comparing this climate and nature controlled inflow sources to population and agricultural activities during recent years, it seems more probable that the primary reason of the drying of Urmia Lake must be human activities such as improper water and agricultural management in the catchment area.  A good water and agricultural monitoring and management program should be designed for Urmia Lake’s catchment area to rescue and recover the Urmia Lake. Remotely sensed data in conjunction with field survey would be a valuable asset for such monitoring program. In addition, GIS technology could be effectively used to conduct spatial and temporal analysis within the lake and its catchment in order to support the decision making process. The DMC satellite images used in this study were provided by Istanbul Technical University (ITU, BAP: 37016) and Landsat images were downloaded from United States Geological Survey website. Meteorological data were also provided by West Azerbaijan Meteorological and East Azerbaijan Meteorological Offices. Landuse maps are provided by National Cartographic Center of Iran. ERDAS IMAGINE 2011, ERDAS IMAGINE 2013, ArcGIS10, ArcGIS 10.1, Envi 5, and SPI_SL_6.exe programs were used in this study.Yüksek LisansM.Sc