The marshes of southern Iraq are of great value due to their roles in the economy,environment,heritage,tourism,and agriculture.However,the region has witnessed remarkable transformations in land cover,influenced by hu...The marshes of southern Iraq are of great value due to their roles in the economy,environment,heritage,tourism,and agriculture.However,the region has witnessed remarkable transformations in land cover,influenced by human interventions and natural environmental factors.In this research,the Central Marshlands were selected for study and monitoring.These Marshes form the Mesopotamian Marshes,a vital part of the Tigris-Euphrates river system.This area 2 formerly covered an area of approximately 3,000 km and was once home to the lives of Marsh Arabs and their animals.The primary objective of this study was to compile a set of satellite images covering the same marshland region over several decades.The data used includes images captured by various Landsat missions:MSS(1975),TM(1983&1993),ETM+(2003),and the Operational Land Imager(OLI)from Landsat 8(2015).Satellite images were combined and pre-processed through steps such as layer stacking to create composite images from multiple bands.Several image classification methods were applied,and the classification results showed a significant and unprecedented increase in the percentage of water in the marsh,reaching 16%in 2003.This was combined with vegetation identification techniques,including the identification of vegetation boundaries to detect areas of dense vegetation.In addition,the relative depth of the water was measured to estimate marsh water levels,with the best result obtained in 2003.The normalized mean vegetation index(NDVI)calculated in this study had its best value in 1984 due to the spread of reeds and papyrus during this period.Papyrus is the raw material in the sugar industry,providing a significant economic boost.展开更多
Soil color changes with water content due to chemical and physical reactions,making it a potential indicator for moisture estimation.By analyzing soil surface images and comparing color variations against laboratory-m...Soil color changes with water content due to chemical and physical reactions,making it a potential indicator for moisture estimation.By analyzing soil surface images and comparing color variations against laboratory-measured water content,a rapid and cost-effective method for moisture determination can be developed.Traditional moisture measurement techniques are time-consuming,so an imaging-based approach would be highly beneficial for quick decision-making.Soil color is also influenced by factors such as particle coarseness,which creates shadows and alters perceived darkness.This research introduces a novel method to isolate true soil color by analyzing the maximum color response in image pixels,minimizing shadow effects.Several equations were derived to correlate color changes with moisture content and were validated against lab measurements to ensure accuracy and simplicity.The most effective equation can be further adapted for satellite imagery by accounting for atmospheric light scattering differences between ground and satellite sensors,enabling large-scale moisture monitoring.The derived equations can be programmed into a software tool,allowing moisture estimation from simple soil surface images.The study involved controlled experiments where soil samples at varying moisture levels were imaged to establish an empirical color-moisture relationship.This method provides a fast,economical,and practical alternative to conventional techniques.However,the approach requires further refinement to account for different soil types globally.Future work should focus on adjusting the model with variables that adapt the color-moisture relationship for diverse soils,ensuring broader applicability.Once optimized,this could significantly improve moisture assessment in agriculture,environmental monitoring,and land management.展开更多
文摘The marshes of southern Iraq are of great value due to their roles in the economy,environment,heritage,tourism,and agriculture.However,the region has witnessed remarkable transformations in land cover,influenced by human interventions and natural environmental factors.In this research,the Central Marshlands were selected for study and monitoring.These Marshes form the Mesopotamian Marshes,a vital part of the Tigris-Euphrates river system.This area 2 formerly covered an area of approximately 3,000 km and was once home to the lives of Marsh Arabs and their animals.The primary objective of this study was to compile a set of satellite images covering the same marshland region over several decades.The data used includes images captured by various Landsat missions:MSS(1975),TM(1983&1993),ETM+(2003),and the Operational Land Imager(OLI)from Landsat 8(2015).Satellite images were combined and pre-processed through steps such as layer stacking to create composite images from multiple bands.Several image classification methods were applied,and the classification results showed a significant and unprecedented increase in the percentage of water in the marsh,reaching 16%in 2003.This was combined with vegetation identification techniques,including the identification of vegetation boundaries to detect areas of dense vegetation.In addition,the relative depth of the water was measured to estimate marsh water levels,with the best result obtained in 2003.The normalized mean vegetation index(NDVI)calculated in this study had its best value in 1984 due to the spread of reeds and papyrus during this period.Papyrus is the raw material in the sugar industry,providing a significant economic boost.
文摘Soil color changes with water content due to chemical and physical reactions,making it a potential indicator for moisture estimation.By analyzing soil surface images and comparing color variations against laboratory-measured water content,a rapid and cost-effective method for moisture determination can be developed.Traditional moisture measurement techniques are time-consuming,so an imaging-based approach would be highly beneficial for quick decision-making.Soil color is also influenced by factors such as particle coarseness,which creates shadows and alters perceived darkness.This research introduces a novel method to isolate true soil color by analyzing the maximum color response in image pixels,minimizing shadow effects.Several equations were derived to correlate color changes with moisture content and were validated against lab measurements to ensure accuracy and simplicity.The most effective equation can be further adapted for satellite imagery by accounting for atmospheric light scattering differences between ground and satellite sensors,enabling large-scale moisture monitoring.The derived equations can be programmed into a software tool,allowing moisture estimation from simple soil surface images.The study involved controlled experiments where soil samples at varying moisture levels were imaged to establish an empirical color-moisture relationship.This method provides a fast,economical,and practical alternative to conventional techniques.However,the approach requires further refinement to account for different soil types globally.Future work should focus on adjusting the model with variables that adapt the color-moisture relationship for diverse soils,ensuring broader applicability.Once optimized,this could significantly improve moisture assessment in agriculture,environmental monitoring,and land management.
