- Zakho, Kurdistan region-Iraq
Yaseen T Mustafa
University of Zakho, Faculty of Science, Faculty Member
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Soil organic matter (SOM) is an essential component of soil fertility that plays a vital role in the preservation of healthy ecosystems. This study aimed to produce an SOM-level map of the Batifa region in northern Iraq. Random forest... more
Soil organic matter (SOM) is an essential component of soil fertility that plays a vital role in the preservation of healthy ecosystems. This study aimed to produce an SOM-level map of the Batifa region in northern Iraq. Random forest (RF) and extreme gradient boosting (XGBoost) models were used to predict the SOM spatial distribution. A total of 96 soil samples were collected from the surface layer (0–30 cm) of both cropland and soil areas in Batifa. In addition, remote sensing data were obtained from Landsat 8, including bands 1–7, 10, and 11. Supplementary variables such as the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), brightness index (BI), and digital elevation model (DEM) were employed as tools to predict SOM levels across the region. To evaluate the accuracy of the RF and XGBoost models in predicting SOM levels, statistical metrics, including mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2...
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Groundwater availability in the Zakho Basin faces significant challenges due to political issues, border stream control, climate change, urbanization, land use changes, and poor administration, leading to declining groundwater quantity... more
Groundwater availability in the Zakho Basin faces significant challenges due to political issues, border stream control, climate change, urbanization, land use changes, and poor administration, leading to declining groundwater quantity and quality. To address these issues, this study utilized the Analytic Hierarchy Process (AHP) and geospatial techniques to identify potential groundwater sites in Zakho. The study assigned weights normalized through the AHP eigenvector and created a final index using the weighted overlay method and specific criteria such as slope, flow accumulation, drainage density, lineament density, geology, well data, rainfall, and soil type. Validation through the receiver operating characteristic (ROC) curve (AUC = 0.849) and coefficient of determination (R2 = 0.81) demonstrated the model’s accuracy. The results showed that 17% of the area had the highest potential as a reliable groundwater source, 46% represented high-to-moderate potential zones, and 37% had l...
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The concept of ecosystem service (ES) was originally developed to illustrate the benefits that natural ecosystems generate for society and to raise awareness for biodiversity and ecosystem conservation. In recent years, geographical... more
The concept of ecosystem service (ES) was originally developed to illustrate the benefits that natural ecosystems generate for society and to raise awareness for biodiversity and ecosystem conservation. In recent years, geographical information system (GIS) has become a powerful tool for mapping (ES) within a landscape, which visualizes spatial and temporal patterns and changes in ecosystems and their services.Mapping (ES) is necessary for the progress of strategies that will guarantee their future supply and to support the policies in a more effective way. The comprehensive literature review were conducted from international databases such as Elsevier, Springer, Wiley, and Google Scholar. We used the key terms including ‘mapping’, ‘maps’, ‘ES or ecosystem service, ‘ecosystem functions’, ‘landscape functions’, ‘evaluation of ES’, and ‘assessment of services’. in order to identify mapping ecosystem services and their challenges and opportunities. In total, 65 research papers were fou...
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Convolutional neural networks (CNNs) have become a popular choice for various image classification applications. However, the multi-layer perceptron mixer (MLP-Mixer) architecture has been proposed as a promising alternative, particularly... more
Convolutional neural networks (CNNs) have become a popular choice for various image classification applications. However, the multi-layer perceptron mixer (MLP-Mixer) architecture has been proposed as a promising alternative, particularly for large datasets. Despite its advantages in handling large datasets and models, MLP-Mixer models have limitations when dealing with small datasets. This study aimed to quantify and evaluate the uncertainty associated with MLP-Mixer models for small datasets using Bayesian deep learning (BDL) methods to quantify uncertainty and compare the results to existing CNN models. In particular, we examined the use of variational inference and Monte Carlo dropout methods. The results indicated that BDL can improve the performance of MLP-Mixer models by 9.2 to 17.4% in term of accuracy across different mixer models. On the other hand, the results suggest that CNN models tend to have limited improvement or even decreased performance in some cases when using B...
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The resection in 3D space is a common problem in surveying engineering and photogrammetry based on observed distances, angles, and coordinates. This resection problem is nonlinear and comprises redundant observations which is normally... more
The resection in 3D space is a common problem in surveying engineering and photogrammetry based on observed distances, angles, and coordinates. This resection problem is nonlinear and comprises redundant observations which is normally solved using the least-squares method in an iterative approach. In this paper, we introduce a vigorous angular based resection method that converges to the global minimum even with very challenging starting values of the unknowns. The method is based on deriving oblique angles from the measured horizontal and vertical angles by solving spherical triangles. The derived oblique angles tightly connected the rays enclosed between the resection point and the reference points. Both techniques of the nonlinear least square adjustment either using the Gauss-Newton or Levenberg – Marquardt are applied in two 3D resection experiments. In both numerical methods, the results converged steadily to the global minimum using the proposed angular resection even with im...
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In recent years, advance oxidation processes (AOPs) have been widely interested for treatment of industrial wastewater and organic matter. At among, Electrofenton has been proposed as a strong oxidative method. So, the aim of this work... more
In recent years, advance oxidation processes (AOPs) have been widely interested for treatment of industrial wastewater and organic matter. At among, Electrofenton has been proposed as a strong oxidative method. So, the aim of this work was purification of colored aqueous containing crystal violet by electrofenton process and steel mesh electrodes. All regents and methods were prepared from analytical grad and standard methods. The amounts of crystal violet were determined by colorimetric using a spectrophotometer at a maximum wavelength about 586 nm. The main parameters such as pH, applied current, dye concentration, reaction time and supporting electrolyte dose were investigated. Experimental data analysis was also performed using excel software. The results of this study showed that the better dye degradation is occurred in acidic pH (pH3), contact time of 5 minutes, initial concentration of crystal violet 50 mg/l, applied current 0.8 A and an electrolyte level about 0.1 g/L of Na...
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Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to... more
Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine ...
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Biophysical parameter values such as LAI have proved useful in a number of environmental applications. An approach is presented for producing the spatio-temporal estimation of leaf area index (LAI) of a heterogeneous forest using Moderate... more
Biophysical parameter values such as LAI have proved useful in a number of environmental applications. An approach is presented for producing the spatio-temporal estimation of leaf area index (LAI) of a heterogeneous forest using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. This is performed by decomposing MODIS LAI for a heterogeneous forest using the Linear Mixture Model (LMM) and the information about the class fraction from an aerial image. Results showed that the decomposed MODIS LAI values were estimated well with maximum and minimum RMSE of 0.37, and 0.17, respectively. We concluded that our approach can be used to decompose MODIS LAI successfully for any heterogeneous forest.
Monitoring and analyzing forests and trees are required task to manage and establish a good plan for the forest sustainability. To achieve such a task, information and data collection of the trees are requested. The fastest way and... more
Monitoring and analyzing forests and trees are required task to manage and establish a good plan for the forest sustainability. To achieve such a task, information and data collection of the trees are requested. The fastest way and relatively low cost technique is by using satellite remote sensing. In this study, we proposed an approach to identify and map 15 tree species in the Mangish sub - district, Kurdistan region-Iraq. Image-objects (IOs) were used as the tree species mapping unit. This is achieved using the shadow index, normalized difference vegetation index and texture measurements. Four classification methods (Maximum Likelihood, Mahalanobis Distance, Neural Network, and Spectral Angel Mapper) were used to classify IOs using selected IO features derived from WV-2 imagery. Results showed that overall accuracy was increased 5-8% using the Neural Network method compared with other methods with a Kappa coefficient of 69%. This technique gives reasonable results of various tree...
Leaf Area index (LAI) is widely used in global environmental and climatic change research. The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery provide LAI product with high temporal resolution. However, the coarse... more
Leaf Area index (LAI) is widely used in global environmental and climatic change research. The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery provide LAI product with high temporal resolution. However, the coarse spatial support of this data limited the application, e.g. the spatial heterogeneity of land cover. In this study, we proposed an approach to decompose MODIS LAI for a heterogeneous forest using the Linear Mixture Model (LMM) and the information of the class fraction from an aerial image. Results showed that the decomposed MODIS LAI values were estimated well with maximum and minimum RMSE of 0.37, and 0.17, respectively. We concluded that our approach can be used to decompose MODIS LAI successfully for any heterogeneous forest.
Estimating the contribution of the forests to carbon sequestration is commonly done by applying forest growth models. Such models inherently use field observations such as leaf area index (LAI), whereas a relevant information is also... more
Estimating the contribution of the forests to carbon sequestration is commonly done by applying forest growth models. Such models inherently use field observations such as leaf area index (LAI), whereas a relevant information is also available from remotely sensed images. This paper aims to improve the LAI estimated from the forest growth model (Physiological Principles Predicting growth (3-PG)) by combining these values with the LAI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. A Bayesian networks (BNs) approach addresses the bias in the 3-PG model and the noise of the MODIS images. A novel inference strategy within the BN has been developed in this paper to take care of the different structures of the inaccuracies in the two data sources. The BN is applied to the Speulderbos forest in The Netherlands, where the detailed data were available. This paper shows that the outputs obtained with the BN were more accurate than either the 3-PG or ...
ABSTRACT An approach is presented for improving the spatial estimation of leaf area index (LAI) of a heterogeneous forest by integrating the Physiological Principles Predicting Growth (3-PG) model output with the Moderate Resolution... more
ABSTRACT An approach is presented for improving the spatial estimation of leaf area index (LAI) of a heterogeneous forest by integrating the Physiological Principles Predicting Growth (3-PG) model output with the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. LAI was chosen as the variable of interest because leaf area is the exchange surface between the photosynthetically active component of the canopy and the atmosphere. A novel inference strategy within the Gaussian Bayesian networks (GBNs) has been developed in this work to take care of the different structures of the inaccuracies in the two data sources. The Linear Mixture Model (LMM) was used to decompose MODIS pixels using class fraction derived from an aerial and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. In this way spatially heterogeneous output was produced. Results showed that the spatial output obtained with the GBN was more accurate than both the spatial 3-PG model output and the satellite estimate, as the root mean square error reduced from 1.21 to 0.57, and the relative error from 20.25% to 7.73%. In this work, we conclude that the GBNs can improve the spatial estimation of the LAI values of a heterogeneous forest by integrating a spatial 3-PG forest growth model with satellite data.
Forest sustainability requires an effective programme such as monitoring, managing, analyzing and classifying tree species through collecting required information. For such a purpose, field-based assessment provides the needed... more
Forest sustainability requires an effective programme such as monitoring, managing, analyzing and classifying tree species through collecting required information. For such a purpose, field-based assessment provides the needed information. However, this method is costly, time consuming, and
therefore assessment frequency is low. This often allows undesirable forest information to develop that do not coincide with management objectives. Satellite remote sensing and its techniques provide an efficient methodology and potentially low-cost alternative to field-based assessment. However, these
techniques require the development of methods; such as semi-automatic tree detection and classification. These methods help to easily and accurately extract the required information in a fastest period. In this study, we investigated the potential of the newly developed high resolution satellite sensor, 8-band WorldView-2 (WV-2) imagery for identifying and mapping tree species in the Zawita sub-district, Duhok, Kurdistan region-Iraq. We performed object-based classification method to
identify and map of Sixteen tree species including: calabrain pine (Pinus brutia), Almand (prunus duclis), Azarole hawthorn (Crataegus azarolus), Judas tree (cercis siliquastrum L), oriental plane (platanus orientalis), white poplar, silver (populus alba), white willow (Salix alba), Valonia Oak (Quercus aegilops), Gall Oak (Quercus Infectoria), common walnut (juglans Nigra), chinaberry (Melia azedarach), Tera Binth (pistacia khinjuk Stocks), syrian ash (fraxinus syriaca), White Mulberry (Morus alba), common fig (ficus carica), oleaster (Elaeagnus ngustifolia). The accuracy assessment is achieved based on the random selection of validation samples, which showed an overall classification accuracy of 77% with Kappa coefficient of 69%. Based on the results of this study we concluded that the WV-2 sensor; with high spatial resolution and additional bands (coastal, yellow, red-edge and NIR2), is attributed as a
proper satellite imagery for forest trees classification.
therefore assessment frequency is low. This often allows undesirable forest information to develop that do not coincide with management objectives. Satellite remote sensing and its techniques provide an efficient methodology and potentially low-cost alternative to field-based assessment. However, these
techniques require the development of methods; such as semi-automatic tree detection and classification. These methods help to easily and accurately extract the required information in a fastest period. In this study, we investigated the potential of the newly developed high resolution satellite sensor, 8-band WorldView-2 (WV-2) imagery for identifying and mapping tree species in the Zawita sub-district, Duhok, Kurdistan region-Iraq. We performed object-based classification method to
identify and map of Sixteen tree species including: calabrain pine (Pinus brutia), Almand (prunus duclis), Azarole hawthorn (Crataegus azarolus), Judas tree (cercis siliquastrum L), oriental plane (platanus orientalis), white poplar, silver (populus alba), white willow (Salix alba), Valonia Oak (Quercus aegilops), Gall Oak (Quercus Infectoria), common walnut (juglans Nigra), chinaberry (Melia azedarach), Tera Binth (pistacia khinjuk Stocks), syrian ash (fraxinus syriaca), White Mulberry (Morus alba), common fig (ficus carica), oleaster (Elaeagnus ngustifolia). The accuracy assessment is achieved based on the random selection of validation samples, which showed an overall classification accuracy of 77% with Kappa coefficient of 69%. Based on the results of this study we concluded that the WV-2 sensor; with high spatial resolution and additional bands (coastal, yellow, red-edge and NIR2), is attributed as a
proper satellite imagery for forest trees classification.
Research Interests:
Monitoring and analyzing forests and trees are required task to manage and establish a good plan for the forest sustainability. To achieve such a task, information and data collection of the trees are requested. The fastest way and... more
Monitoring and analyzing forests and trees are required task to manage and establish a good plan for the forest
sustainability. To achieve such a task, information and data collection of the trees are requested. The fastest way and
relatively low cost technique is by using satellite remote sensing. In this study, we proposed an approach to identify and
map 15 tree species in the Mangish sub-district, Kurdistan Region-Iraq. Image-objects (IOs) were used as the tree
species mapping unit. This is achieved using the shadow index, normalized difference vegetation index and texture
measurements. Four classification methods (Maximum Likelihood, Mahalanobis Distance, Neural Network, and Spectral
Angel Mapper) were used to classify IOs using selected IO features derived from WorldView-2 imagery. Results showed
that overall accuracy was increased 5-8% using the Neural Network method compared with other methods with a Kappa
coefficient of 69%. This technique gives reasonable results of various tree species classifications by means of applying
the Neural Network method with IOs techniques on WorldView-2 imagery.
sustainability. To achieve such a task, information and data collection of the trees are requested. The fastest way and
relatively low cost technique is by using satellite remote sensing. In this study, we proposed an approach to identify and
map 15 tree species in the Mangish sub-district, Kurdistan Region-Iraq. Image-objects (IOs) were used as the tree
species mapping unit. This is achieved using the shadow index, normalized difference vegetation index and texture
measurements. Four classification methods (Maximum Likelihood, Mahalanobis Distance, Neural Network, and Spectral
Angel Mapper) were used to classify IOs using selected IO features derived from WorldView-2 imagery. Results showed
that overall accuracy was increased 5-8% using the Neural Network method compared with other methods with a Kappa
coefficient of 69%. This technique gives reasonable results of various tree species classifications by means of applying
the Neural Network method with IOs techniques on WorldView-2 imagery.
Research Interests:
Monitoring and mapping of urban trees are essential to provide urban forestry authorities with timely and consistent information. Modern techniques increasingly facilitate these tasks, but require the development of semi-automatic tree... more
Monitoring and mapping of urban trees are essential to provide urban forestry authorities with timely and consistent information. Modern techniques increasingly facilitate these tasks, but require the development of semi-automatic tree detection and classification methods. In this article, we propose an approach to delineate and map the crown of 15 tree species in the city of Duhok, Kurdistan Region of Iraq using WorldView-2 (WV-2) imagery. A tree crown object is identified first and is subsequently delineated as an image object (IO) using vegetation indices and texture measurements. Next, three classification methods: Maximum Likelihood, Neural Network, and Support Vector Machine were used to classify IOs using selected IO features. The best results are obtained with Support Vector Machine classification that gives the best map of urban tree species in Duhok. The overall accuracy was between 60.93% to 88.92% and κ-coefficient was between 0.57 to 0.75. We conclude that fifteen tree species were identified and mapped at a satisfactory accuracy in urban areas of this study.
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Scientific and academic researches and studies trying to present a multi-range of techniques and methods focusing on groundwater pollution, potentials, assessment, and prediction, Groundwater is the most important resource of fresh water... more
Scientific and academic researches and studies trying to present a multi-range of techniques and methods focusing on groundwater pollution, potentials, assessment, and prediction, Groundwater is the most important resource of fresh water now and many researchers trying to cover all about this resource to get sustainable development. This review aims to create an overview of groundwater analysis and forecasting methods. The study is based on the need to select and group research papers into best-defined methodological categories. The article gives an overview of recent advancements in groundwater potential zone analysis approaches, as well as ongoing research objectives based on that overview. This review has overviewed papers and researches been published last decade 2010 -2020 have been done depending on the data sources from the global online database, which could obtain many papers and research studying the groundwater potential zones and other aspects related to groundwater. Th...
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The integration of remote sensing techniques and Geographic Information System has a wide use to quantify the spatial and temporal distribution of vegetation cover. Over the last decade, a remarkable change was noticed in both climate and... more
The integration of remote sensing techniques and Geographic Information System has a wide use to quantify the spatial and temporal distribution of vegetation cover. Over the last decade, a remarkable change was noticed in both climate and vegetation cover in Duhok. The Modified Soil Adjusted Vegetation Index (MSAVI2) was extracted from Landsat satellite images over the 20 years (2000 to 2019). For analyzing the vegetation changes, the terrain data including elevation, slope, and aspect and climate data temperature and precipitation are used. The result shows that from 2000–2019, the average mean MSAVI2 is 0.361 and the trend increased in 77.9% of the study area. The northern and northeastern areas of the study area revealed a significant increase in vegetation, while in the low land areas it is decreased. The amount of precipitation and temperature degree affect the spatiotemporal distribution of vegetation cover. The MSAVI2 showed a positive relationship with precipitation and temp...
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We are living within the century, when humankind created the capacity for measurement of features, phenomena and impacts across the Earth on a planetary scale. This is due to the new technologies that consistently advance our world,... more
We are living within the century, when humankind created the capacity for measurement of features, phenomena and impacts across the Earth on a planetary scale. This is due to the new technologies that consistently advance our world, guiding, positioning and visualizing solutions guide us through this latest digital disruption. Accordingly, our generation has an unprecedented capacity with our technologies and data to look across time, on a planetary scale to address issues that are relevant to the future of Earth and how can be utilized. Earth observations through satellites have provided incomparable information about the Earth System and its components. This talk provides an overview of the state of the art and applications of satellite imageries including a brief history of satellites. Some prominent examples will be presented, including monitoring, change detection and some other applications. These technological developments within the umbrella of a remote sensing system help with continuous observation of dynamic processes over the Earth's surface.
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In recent decades, floods have been the most common, complex, and destructive natural calamities worldwide. Hence, for inclusive flood risk assessment, creating flood susceptibility mapping to demarcate flood-vulnerable zones is... more
In recent decades, floods have been the most common, complex, and destructive natural calamities worldwide. Hence, for inclusive flood risk assessment, creating flood susceptibility mapping to demarcate flood-vulnerable zones is fundamental for decision makers. To assess flood-prone locations in the Akre, Iraqi Kurdistan Region, fundamental for susceptibility mapping was undertaken using geographic information systems, remote sensing, and an analytic hierarchy process model. To assess flood susceptibility, the geographic information systems framework used 15 ideal causative factors for flooding: altitude, slope, distance to streams, flow accumulation, drainage density, rainfall, soil type, lithology, curvature, topographic wetness index (TWI), topographic roughness index stream power index, stream transport index, land use/land cover, and normalized difference vegetation index. The factors contributing to flooding were optimally weighted with respect to the proposed model. The final...
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Copyright © 2014 Mohamad J. Noori et al. This is an open access article distributed under the Creative Commons Attribution Li-cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work... more
Copyright © 2014 Mohamad J. Noori et al. This is an open access article distributed under the Creative Commons Attribution Li-cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accordance of the Creative Commons Attribution License all Copyrights © 2014 are reserved for SCIRP and the owner of the intel-lectual property Mohamad J. Noori et al. All Copyright © 2014 are guarded by law and by SCIRP as a guardian. Rainfall is a significant portion of hydrologic data. Rainfall records, however, are often incomplete due to several factors. In this study, the inverse distance weighting (IDW) method integrated with GIS is used to estimate the rainfall distribution in Duhok Governorate. A total of 25 rain fall stations and rainfall data between 2000 and 2010 were used, where 6 rainfall stations were used for cross-validation. In addition, the relationship between interpolation accuracy and two critical paramete...
Land degradation is a complex process and significant environmental problem affected by both natural and anthropogenic driving factors. Globally, the prevention of land degradation has become one o...
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Monitoring and mapping of urban trees are essential to provide urban forestry authorities with timely and consistent information. Modern techniques increasingly facilitate these tasks, but require the development of semi-automatic tree... more
Monitoring and mapping of urban trees are essential to provide urban forestry authorities with timely and consistent information. Modern techniques increasingly facilitate these tasks, but require the development of semi-automatic tree detection and classification methods. In this article, we propose an approach to delineate and map the crown of 15 tree species in the city of Duhok, Kurdistan Region of Iraq using WorldView-2 (WV-2) imagery. A tree crown object is identified first and is subsequently delineated as an image object (IO) using vegetation indices and texture measurements. Next, three classification methods: Maximum Likelihood, Neural Network, and Support Vector Machine were used to classify IOs using selected IO features. The best results are obtained with Support Vector Machine classification that gives the best map of urban tree species in Duhok. The overall accuracy was between 60.93% to 88.92% and κ-coefficient was between 0.57 to 0.75. We conclude that fifteen tree ...
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Estimating the contribution of forests to carbon sequestration is commonly done by applying forest growth models. Such models inherently use field observations, such as leaf area index (LAI), whereas relevant information is also available... more
Estimating the contribution of forests to carbon sequestration is commonly done by applying forest growth models. Such models inherently use field observations, such as leaf area index (LAI), whereas relevant information is also available from remotely sensed images. The purpose of this study is to improve the LAI estimated from the physiological principles predicting growth (3-PG) model by combining its output with LAI derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. A Bayesian network (BN) approach is proposed to take care of the different structure of the inaccuracies in the two data sources. It addresses the bias in the 3-PG model and the noise of the ASTER images. Moreover, the EM algorithm is introduced into BN to estimate missing the LAI ASTER data, since they are not available for long time series due to the atmospheric conditions. This paper shows that the outputs obtained with the BN were more accurate than the 3-PG est...
Monitoring and analyzing forests and trees are required task to manage and establish a good plan for the forest sustainability. To achieve such a task, information and data collection of the trees are requested. The fastest way and... more
Monitoring and analyzing forests and trees are required task to manage and establish a good plan for the forest sustainability. To achieve such a task, information and data collection of the trees are requested. The fastest way and relatively low cost technique is by using satellite remote sensing. In this study, we proposed an approach to identify and map 15 tree species in the Mangish sub-district, Kurdistan Region-Iraq. Image-objects (IOs) were used as the tree species mapping unit. This is achieved using the shadow index, normalized difference vegetation index and texture measurements. Four classification methods (Maximum Likelihood, Mahalanobis Distance, Neural Network, and Spectral Angel Mapper) were used to classify IOs using selected IO features derived from WorldView-2 imagery. Results showed that overall accuracy was increased 5-8% using the Neural Network method compared with other methods with a Kappa coefficient of 69%. This technique gives reasonable results of various tree species classifications by means of applying the Neural Network method with IOs techniques on WorldView-2 imagery.
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The rapid urban development in the Duhok city since the 1990s has dramatically enhanced the potential impact of human activities. To identify and monitor this urban development effectively, remote sensing provides a viable source of data... more
The rapid urban development in the Duhok city since the 1990s has dramatically enhanced the potential impact of human activities. To identify and monitor this urban development effectively, remote sensing provides a viable source of data from which updated land cover information can be extracted efficiently and cheaply. In this study, three satellite datasets, Landsat Thematic Mapper (Landsat TM), and two Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+), acquired during 1989, 2001 and 2012, respectively, were used to detect and evaluate Duhok’s urban expansion. Two change detection techniques were tested to detect areas of change. The techniques considered were image differencing, and post-classification comparison. The land use/land cover (LULC) maps of the years 1989, 2001 and 2012 were produced and the changes were determined with significant accuracies. The simplicity of our methods and the minimal investment of time and money make incorporation of remotely sensed data int...