Monitoring Land Cover Changes in Halabja City, Iraq.

This paper presents land use / land cover changes of the Halabja city in the north part of Iraq over 1986 to 1990 by utilizing multi-temporal remote sensing imagery. Halabja city has been facing severe land use/land cover changes following a series of wars beginning with Iraq-Iran war (1980-1988) to the just concluded invasion of Iraq (March 19, 2003 – 2011). In this study, multi-temporal Landsat images (TM) between the years of 1986 and 1990 were used. All images are rectified and registered to Universal Transverse Mercator (UTM), zone 38N and WGS_84 datum. Hybrid classification as a combine of k-Means and Maximum Likelihood Classification (MLC) algorithms were applied to classify the images in five different land cover categories: water body, cultivated area, shrub land, urban area and bare land. Quantitative analysis was conducted by using post-classification change detection technique. The results show an overall accuracy for 1986 and 1990 images are 92.2% and 96.8% respectively. During 1986 to 1990 land use / land cover changes a lot with a huge decrease about 40.8% in cultivated area whereas, urban area, Shrub Land and bare land classes increased by 57.9 %, 67.1 % and14 % respectively

NDVI Differencing and Post-classification to Detect Vegetation Changes in Halabja City, Iraq.

This study presents vegetation change detecting in Halabja city, Iraq by using Landsat-5Thematic Mapper images. This city was shelled with chemical weapons on 16 March, 1988. The Normalized Difference Vegetation Index (NDVI) image differencing and post–classification techniques were applied. The NDVI was derived first then classified to produce vegetation maps followed by quantifying the changes.The results indicated a drastic decrease in the dense, sparse and moderate vegetation by55%, 7% and 9% respectively. In contrast, the non-vegetation class increased by 5%. This means that, the field and planted areas were at risk of losing vegetation

Thermal imaging for pests detecting-a review

Thermal remote sensing technology (thermography) is a non-destructive technique used to determine thermal properties of any objects of interest. The principle of thermal remote sensing is the invisible radiation patterns of objects converted into visible images and these images are called thermal images. These images can be acquired using portable, handheld or thermal sensors that are coupled with optical systems mounted on an airplane or satellite. This technology has grown into an important technology that is applied directly or indirectly in many applications such as civil engineering and industrial maintenance, etc. The potential use of thermal remote sensing in agriculture includes nursery and greenhouse monitoring, irrigation scheduling, plant disease detection, estimating fruit yield, evaluating the maturity of fruits and bruise detection in fruits and vegetables. However, in recent years, the usage of thermal imaging is gaining popularity in pest detection due to the reductions in the cost of the equipment and simple operating procedure. The purpose of this paper is two parts, the first part discusses about thermal remote sensing system while the second part epitomize various studies conducted on the potential application of thermal imaging system in pest detection

Image Classification in Remote Sensing

One of the most important functions of remote sensing data is the production of Land Use and Land Cover maps and thus can be managed through a process called image classification. This paper looks into the following components related to the image classification process and procedures and image classification techniques and explains two common techniques K-means Classifier and Support Vector Machine (SVM). Keywords: Remote Sensing, Image Classification, K-means Classifier, Support Vector Machin

Change Detection Process and Techniques

Land use / land cover changes studies have become very interesting over the past decades through using remote sensing because of the availability of a suite of sensors operating at various imaging scales and scope of using various techniques as well as considered the good ways for effective monitoring and accurate land use /land cover changes. This paper looks into the following aspects related to the remote sensing technology, change detection process and techniques for land cover changes, and factor affecting change detection techniques and considerations. Keywords: Remote Sensing, Land Use / Land Cover, Change Detectio

War Impacts Studies Using Remote Sensing.

Since 1960, remote sensing satellite imagery reconnaissance has played a significant role in military operations by providing information concerning enemy missiles, troop deployments and military positioning using photographic images from lighter-than-air balloons to aircraft platforms and finally satellite remote sensing imagery with little attention given to broader war impacts. However, besides the war-related uses of such technology, many academic researchers have taken pains to use such advanced technology for examining war impacts. This paper highlights the applications of this technology for detecting war impacts

SCS+C Topographic Correction to Enhance SVM Classification Accuracy

The topographic impact may change the radiance values captured by the spacecraft sensors, resulting in distinct reflectance value for similar land cover classes and mischaracterization. The problem can be more clearly seen in rugged terrain landscapes than in flat terrains, such as the mountainous areas. In order to minimize topographic impacts, we suggested the implementation of Modified Sun-Canopy-Sensor Correction (SCS+C) technique to generate land cover maps in Gua Musang district which is located in a rugged mountainous terrain area in Kelantan state, Malaysia using an atmospherically corrected Landsat 8 imagery captured on 22 April 2014 by Support Vector Machine (SVM) algorithm. The results showed that the SCS+C method reduces the topographic effect particularly in such a steep and forested terrain with classification accuracy improvement about 4 % which was statistically significantly with the McNemar test value Z and P measured 6.42 and 0.0001 on the corrected image classification 90.1 % accuracy compared to the uncorrected image 86.2 % for the test area. Thus, the topographic correction is suggested to be the main step of the data pre-processing stage in mountainous terrain before SVM image classification

Support vector machine classification to detect land cover changes in Halabja city, Iraq

Halabja city in Iraq has faced drastic landscape change since the Iraq-Iran war, especially when this city and the surrounding areas were attacked with chemical bombs in 1988. This paper illustrates the results of land use/cover change in Halabja obtained by using multi-temporal remotely sensed data from 1986 to 1990. The support vector machine supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect and monitor land use/cover change. Derived land use/cover maps were further validated by using high resolution images derived from Google earth. The results from this research indicate that the overall accuracy of land cover maps generated from Landsat Thematic Mapper (TM) data were more than 89%. The urban areas and vegetation classes decreased approximately 58.7% to 40.7% between 1986 and 1990, while bare land increased 25.4%. Also, some changes in urban areas were detected that have already been identified as bombed areas particularly around the main roads of Halabja city

Use of hybrid classification algorithm for land use and land cover analysis in data scarce environment

The technique of remote sensing satellite imaging has played a significant role in facilitating the study of land use/land cover changes (LULC). This is because the information that can be extracted from images constitutes a fundamental key in many diverse applications such as Environment, Planning and Monitoring programs and others. LULC changes are mainly the result of human intervention and natural phenomena such as population growth, urbanization, wars and other factors. During the 1980-1988 Iraq-Iran war, many cities and villages in the north of Iraq were shelled several times with chemical weapons that caused many changes in land covers. Among the cities seriously affected by these chemical weapons is Halabja City (the study area for this research), which was shelled on 16 March 1988, leaving approximately 5,000 people dead and 7,000 injured with long-term damage to their health. In this study, vegetation indices, tasseled cap transformation, hybrid classification as a combination of k-means and support vector machine algorithms,and post-classification comparison were respectively implemented to detect and assess LULC in Halabja. Two Landsat 5 (Thematic Mapper - TM) images obtained in 1986, 1990 with one Landsat 7 (Enhanced Thematic Mapper Plus - ETM+) image acquired in 2000 were used. All images were geometrically corrected and projected to UTM, Datum WGS_84 and Zone 38N using automatic image to image registration with polynomial transformation equations and a nearest neighbor re-sampling algorithm. The root mean square (RMS) error was less than 0.5 pixels. Subsequently,all images were atmospherically corrected by applying dark object subtraction and sub-setted to (1400) samples, (999) lines. The hybrid classifier with the aid of visual interpretation tools, knowledge-based assignment and other supplementary data like Google earth images and vegetation indices were run on subsets to classify images into five thematic classes based on the NLCD 92 classification system scheme (Water Bodies; Shrub Land; Cultivated/Planted Area; Low-Intensity Urban Area; and Bare Land). To assess classification accuracy, the classified images were randomly sampled to produce confusion matrix which provided LULCC maps with an average overall accuracy of 95% and 0.94 Kappa statistic that tendered them deal for further qualitative and quantitative analysis of land cover changes through a postclassification. Based on the overall accuracy and kappa statistics, hybrid classifier was found to be more preferred classification approach than k-means and SVM. A multi-date post-classification comparison algorithm was used to determine LULC changes in two intervals, 1986-1990, and 1990-2000. Change analysis during 1986 to 1990 revealed that all classes decreased and showed few changes except the bare land which showed an increase of about 30%. The Low intensity urban changed area was determined and overlaid with chemical weapons bombing location GPS points; roads with the aid of the NDBI index to locate low intensity urban area changes. It was noticed that bombed places are the same places where the urban area changed. During the 1990 to 2000 period, there were significant increases in low intensity residential and cultivated / plant areas. The low intensity residential area increased by 83%. Most of the increments of this class come from the conversion of 36% water bodies, 24% of shrub land, 14% of bare land, and 6% of low intensity residential areas. On the contrary, there was a significant decrease in water bodies by 55% overall and other class designations. In conclusion, hybrid classification as a combination of k-means and support vector machine algorithms and post-classification comparison change detection technique can be used to monitor land cover changes in Halabja city, Iraq