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Water is the most important resource for sustaining life since it keeps the whole ecosystem alive and shapes human civilization [1,2]. Water resource management will be the most important issue in the future as it is today, especially for arid and semi-arid regions [3]. Due to the increasing population and consequent increase in food requirements, global warming and climate change, precision water resources management is of much greater importance -especially in the organization of agricultural irrigation activities [4,5]. However, effective water resource management is only possible with continuous monitoring [6,7]. Satellite remote sensing is functional technology for monitoring natural resources such as water bodies and enables a timeand cost-effective monitoring of water resources with reliable data [8][9][10].Several satellite remote sensing methods such as image classification, linear unmixing, single-band thresholding and water index are available to determine water bodies [11][12][13][14]. Since linear unmixing and image

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Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density - 5.8, 4.6, 2.3, and 1.2 pt s/m, aerial optical NAIP (National Agricultural Imagery Program) imagery - 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7-11.0% of variation) than changing NAIP image resolution (causing 6.2-8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape.

Özet Kuraklık meteorolojik kökenli bir doğal afettir ve günümüzün en önemli sorunlarından biridir. Temelinde yağış eksikliği olarak başlayan kuraklığın doğaya ve tüm canlı hayatına yıkıcı etkileri sürekli olarak artmaktadır. Bu çalışmada Çanakkale Merkez İlçe sınırlarında bulunan ve bölgenin tek su kaynağı olan Atikhisar Baraj Gölü su alanının 1984-2018 yılları arasındaki yıllık zamansal değişimi uydundan uzaktan algılama görüntüleri ile belirlenmiştir ve meteorolojik kuraklığın bu değişime etkisi araştırılmıştır. Bu kapsamda bölgedeki meteorolojik kuraklığın değişimi eklenik sapma eğrisi ve standart yağış indisi (SPI) ile incelenmiştir. Tüm meteorolojik değerlendirmeleri Ekim ayı başında başlayan 12 aylık su yılı takvimine göre yapılmıştır. Gölün alansal değişimi Landsat 8 Operational Land Imager (OLI) multispektral çok-zamanlı uydu görüntülerinden üretilen normalize edilmiş fark su indisi (NDWI) veri seti ile belirlenmiştir. Meteorolojik veriler ile uyumlu olması için, tüm görüntüler 1984-2018 yılları arasında kurak dönemin (su yılının) sonunda elde edilmiştir. Böylelikle 12 aylık su yılı sonundaki meteorolojik kuraklığın NDWI ile belirlenen su alanına etkisi araştırılmıştır. Sonuçlar baraj göl alanı değişiminde bir tek meteorolojik kuraklığın etkili olmadığını göstermektedir. SPI meteorolojik kuraklık indisi değerlerinin eklenik sapma değerlerine kıyasla yıl bazında göl alanı değişimine daha hassas olduğu ve NDWI ile belirlenen göl alanı değerleri ile çok daha yüksek ve anlamlı korelasyonlara sahip olduğu tespit edilmiştir.

The aim of this study is to demonstrate the relationship between the long years' monthly average (LYMA) land surface temperature (LST) and the LYMA air temperature (T a ), the total precipitation (P t ), and the relative humidity (RH). Data from 27 meteorological stations in the Eastern Thrace region and corresponding thermal infrared images from Landsat-5 (TM) and Landsat-7 (ETM+) were used in this study. Simple regression models were developed for each meteorological station to predict the LYMA T a , P t and RH based on the LST values. The resulting LST-based prediction models were judged based on the correlation coefficient (r) and root mean square (RMSE). The average correlation and RMSE for the LST-based T a were r = 0.959 and RMSE = 1.771 o C. The average correlation and RMSE for the LST-based P t were r = -0.863 and RMSE = 10.098 mm. The average correlation and RMSE for the LST-based RH were r = -0.932 and RMSE = 1.875%. The results indicate that LST can be a good estimator for LYMA T a , P t and RH, and LYMA T a is positively, LYMA P t and LYMA RH are negatively correlated with LYMA LST.

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Özet (flow, precipitation, temperature, etc.) of the basin increased the success of determining water components with narrow river bed.