Supervised Classification Techniques Research Articles

Detection of Land Use Changes in Batam Island Coastal Using Remote Sensing

Batam is one of the fastest growing cities in Indonesia that has resulted in increasing demand for industrial, commercial and residential areas. However, due to limited land supply there is a process of transferring existing land functions. By detecting changes in land use, we can find out how much land use changes are influenced by social, economic and industrial in Batam Island. This research was conducted on the detection of land use change using Remote Sensing multi-temporal or differentiated image of the year with a supervised Classification Technique to obtain Land use information and changes in Batam Island, area percentage of land use change from 2010 and 2016 in Batam Island, determining the relationship of land use change to conditions in the industry sector presented in to the maps. The calculation result of classification accuracy test obtained total overall accuracy of 88% where the classification results can be said true and almost in accordance with the field. The relationship between land use classification change with industrial condition on the Non-Agricultural area I can be said to related with the number of companies and the labors number on the data of labor changes from Central Bureau of Statistic/Badan Pusat Statistik (BPS) 2010-2014 with the assumption of changing function and the many needs of various land uses sector except agriculture, the Non-Agriculture Area II can be said to be related with the number of companies and the number of labors in the data of labor changes BPS 2010-2014 with the assumption of changing function and the many land use needs of various sectors except agriculture, increased farming can be said to be related with the number of agriculture companies that also increases in data of labor changes BPS 2010-2014, the area of the Areas without Vegetation Coverage Class increased can be said to have influence with the number increased of companies and the number of manpower on the data labor changes BPS 2010-2014 with the assumption that there are many land use needs of various sectors except agriculture.

Feature extraction and ageing state recognition using partial discharges in cables under HVDC

PD detection is an effective way to evaluate the degradation state of cable insulation. The extraction and selection of relevant features from PD raw data have been mostly investigated to recognize the types of insulation defects in HV equipment. In this study, two different feature extraction methods combined with supervised classification techniques are implemented for ageing state recognition of a polyethylene-insulated cable under HVDC conditions. For this purpose, an original experimental setup is implemented. Experiments are performed on a long length 100m coaxial cable subjected to high electric fields. PD events are detected by direct coupling and collected with a digitizing oscilloscope. Feature extraction based on PD pulse shape parameters represented in time domain as well as wavelet decomposition coefficients are used separately as input variables of Support Vector Machines classifiers (SVMs). A feature selection method is implemented to design optimized SVM classifiers that attribute an ageing state to the cable insulation. The classification performance achieved with both feature extraction methods are presented and compared. The results show satisfactory recognition rates of two ageing states of cable insulation, up to 100% with a small subset of variables, particularly when features are extracted from wavelet decomposition of PD experimental data.

A Supervised Classification Techniques to Optimize Error Evaluation and Space Complexity

Bayesian classification is based on Baye’s Theorem, which is applied on a conditional probability basis of posterior and prior probabilities in parallel with future evidence. Prior Probabilities are the original probabilities of an outcome which will be updated with new information to create posterior probability. The revised probability of an event occurring after taking into consideration new information.A Bayesian classifier is used to predict the values of features for members of that class. It is used to over come the diagnostic and predictive problems. This classification provides a useful perspective for understanding and evaluating machine learning algorithms.It is a probabilistic learning algorithm which calculates the explicit probabilities for hypothesis, among the most common learning problem.The proposed work has focused on designing of two classification algorithms naïve space and naïve Mine classification to optimize space complexity and error evaluation forlarger data sets.

Prediction of Heart Disease using SVM

Support Vector Machine (SVM) is an important classification method in data mining. It is a supervised classification technique. It finds a hyperplane for classification of the target classes. The heart disease consists set of disorders affecting the heart. It includes blood vessel problems such as irregular heart beat issues, weak heart muscles, congenital heart defects, cardio vascular disease and coronary artery disease. Coronary heart disorder is a familiar type of heart disease. It reduces the blood flow to the heart leading to a heart attack. In this paper the UCI machine learning repository data set consisting of patients suffering from heart disease is analyzed using support vector machines. The classification accuracy of the patients suffering from heart disease is predicted. Implementation is done using R language.

REMOTE SENSING FOR CROPS IDENTIFICATION

Abstract. Since 2012, the INEGI began using satellite images to identify crops and to be able to locate areas where they develop. To date, this activity has been developed as a research project in which different multispectral images and different tools for the processing of satellite images have been used. Supervised classification techniques have made possible to identify the surface of some important crops of annual cycle and some perennials, such as sorghum, corn, lemon, grape, potato, orange, among others. The use of this methodology allows exploring new ways of obtaining agricultural statistical information, as well as generating statistical data that can be used to validate or confirm crop coverage at specific places. To put this project into practice, after the initial investigations, we trained a small group of people, with whom we carried out some tests to know and evaluate the benefits or disadvantages of applying identification of crops through remote sensing. Between 2012 and 2018, INEGI has received support from institutions that are more advanced in the use of remote sensors, such as NASS from the USA, StatCanada and JRC of the European Union. Tests designed by INEGI have run on sites with different topographic and climatic conditions to better understand the spectral response of crops. This document presents some of the main results obtained.

Monitoring of Land Use/Land Cover and Socioeconomic Changes in South China over the Last Three Decades Using Landsat and Nighttime Light Data

Land use and land cover changes (LULCC) are prime variables that reflect changes in ecological systems. The Guangdong, Hong Kong, and Macau (GHKM) region located in South China has undergone rapid economic development and urbanization over the past three decades (1986–2017). Therefore, this study investigates the changes in LULC of GHKM based on multi-year Landsat and nighttime light (NTL) data. First, a supervised classification technique, i.e., support vector machine (SVM), is used to classify the Landsat images into seven thematic classes: forest, grassland, water, fishponds, built-up, bareland, and farmland. Second, the demographic activities are studied by calculating the light index, using nighttime light data. Third, several socioeconomic factors, derived from statistical yearbooks, are used to determine the impact on the LULCC in the study area. The post-classification change detection shows that the increase in the urban area, from 0.76% (1488.35 km2) in 1986 to 10.31% (20,643.28 km2) in 2017, caused GHKM to become the largest economic segment in South China. This unprecedented urbanization and industrialization resulted in a substantial reduction in both farmland (from 53.54% (105,123.93 km2) to 33.07% (64,932.19 km2)) and fishponds (from 1.25% (2463.35 km2) to 0.85% (1674.61 km2)) during 1986–2017. The most dominant conversion, however, was of farmland to built-up area. The subsequent urban growth is also reflected in the increasing light index trends revealed by NTL data. Of further interest is that the overall forest cover increased from 33.24% (65,257.55 km2) to 45.02% (88,384.19 km2) during the study period, with a significant proportion of farmland transformed into forest as a result of different afforestation programs. An analysis of the socioeconomic indicators shows that the increase in gross domestic product, total investment in real estate, and total sales of consumer goods, combined with the overall industrialization, have led to (1) urbanization on a large scale, (2) an increased light index, and (3) the reduction of farmland. The speed of development suggests that opportunistic development has taken place, which requires a pressing need to improve land policies and regulations for more sustainable urban development and protection of farmland.

Supervised classification of geometrical objects by integrating currents and functional data analysis

This paper focuses on the application of supervised classification techniques to a set of geometrical objects (bodies) characterized by currents, in particular, discriminant analysis and some nonparametric methods. A current is a relevant mathematical object to model geometrical data, like hypersurfaces, through integration of vector fields over them. As a consequence of the choice of a vector-valued reproducing kernel Hilbert space (RKHS) as a test space to integrate over hypersurfaces, it is possible to consider that hypersurfaces are embedded in this Hilbert space. This embedding enables us to consider classification algorithms of geometrical objects. We present a method to apply supervised classification techniques in the obtained vector-valued RKHS. This method is based on the eigenfunction decomposition of the kernel. The novelty of this paper is therefore the reformulation of a size and shape supervised classification problem in functional data analysis terms using the theory of currents and vector-valued RKHSs. This approach is applied to a 3D database obtained from an anthropometric survey of the Spanish child population with a potential application to online sales of children’s wear.

Hybrid machine learning framework for hydrological assessment

This study introduces a novel hydrological assessment tool (HAT) based on hybrid machine learning (HML) framework. The HML framework combines an unsupervised clustering technique and a supervised classification technique, to determine reasonable performance ratings (unsatisfactory, satisfactory, good, and very good) and build a practical assessment tool. Hydrologically significant error indices are used to cluster the performance rating groups and train the HAT. The HAT was applied to the National Water Model (NWM), which is operated in real time for the continental United States (CONUS). For establishing, training, and validating the HAT, data from October 2013 to February 2017 were used, and a performance assessment was conducted on the NWM in the San Francisco Bay Area. As a result, the HAT determined the performance ratings that were reliable in terms of the statistics and hydrograph. It was confirmed that the HAT could perform an accurate hydrograph assessment as the concordance rate of the performance ratings was 98%. The NWM was evaluated against 57 USGS streamflow gauges using the HAT and was found to perform with 46% on average, good and very good ratings. The HML framework, an integral part of the HAT, is expected to be useful not only in hydrological analysis but also across all geophysical fields that deal with physical processes.

Management Recommendation Generation for Areas Under Forest Restoration Process through Images Obtained by UAV and LiDAR

Evaluating and monitoring forest areas during a restoration process is indispensable to estimate the success or failure of management intervention and to correct the restoration trajectory through adaptive management. However, the field measurement of several indicators in large areas can be expensive and laborious, and establishing reference values for indicators is difficult. The use of supervised classification techniques of high resolution images, combined with an expert system to generate management recommendations, can be considered promising tools for monitoring and evaluating restoration areas. The objective of the present study was to elaborate an expert system of management recommendation generation for areas under restoration, which were monitored by two different remote sensors: UAV (Unmanned Aerial Vehicle) and LiDAR (Light Detection and Ranging). The study was carried out in areas under restoration with about 54 ha and five years of implementation, owned by Fibria Celulose S.A. (recently acquired by Suzano S.A.), in the southern region of Bahia State, Brazil. We used images from Canon S110 NIR (green, red, near infrared) on UAV and LiDAR data compositions (intensity image, digital surface model, digital terrain model, normalized digital surface model). The monitored restoration indicator entailed land cover separated into three classes: Canopy cover, bare soil and grass cover. The images were classified using the Random Forest (RF) and Maximum Likelihood (ML) algorithms and the area occupied by each land cover classes was calculated. An expert system was developed in ArcGIS to define management recommendations according to the land cover classes, and then we compared the recommendations generated by both algorithms and images. There was a slight difference between the recommendations generated by the different combinations of images and classifiers. The most frequent management recommendation was “weed control + plant seedlings” (34%) for all evaluated methods. The image monitoring methods suggested by this study proved to be efficient, mainly by reducing the time and cost necessary for field monitoring and increasing the accuracy of the generated management recommendations.

Framework for agricultural performance assessment based on MODIS multitemporal data

We present a hierarchical classification framework for automated detection and mapping of spatial patterns of agricultural performance using satellite-based Earth observation data exemplified for the Aral Sea Basin (ASB) in Central Asia. The core element of the framework is the derivation of a composite agricultural performance index which is composed of different subindicators taking into account cropping intensity, crop diversity, crop rotations, fallow land frequency, land utilization, water use efficiency, and water availability. We derive these subindicators from net primary productivity and evapotranspiration data obtained from the MODIS sensor on board the Terra satellite during the observation period from 2000 to 2016, as well as from cropland maps created through multiannual classification of normalized difference vegetation index (NDVI). We classified pixel-based NDVI time series covering more than 8 × 106 ha of irrigated cropland based on a hierarchical approach concatenating unsupervised and supervised classification techniques to automatically generate and refine training labels, which are then used to train a decision fusion classifier, achieving an average overall accuracy of 78%. The results give unprecedented insights into spatial patterns of agricultural performance in the ASB. The proposed method is transferable and applicable for global-scale mapping, and the results of this remote sensing-aided assessment can provide important information for regional agricultural planning purposes.

Temporal detection and prediction of agricultural land consumption by urbanization using remote sensing

The most fertile land in Egypt is under continuous loss by urbanization. In some countries, this could be considered normal, but in Egypt, it is a severe violation of the country’s limited agricultural land. The main objectives of this research are to quantify the agricultural land consumption by urbanization and predict the future pattern of this process in some parts of Alexandria and El-Behiera governorates using the Landsat satellite images of 1987, 2001, 2015, and 2019. Image classification was performed using the Maximum likelihood supervised classification technique. Five land use/cover (LULC) types have been identified, which are agriculture, urban, gardens, the international road, and water. Classification results were further enhanced by employing post-classification and filtering processes which increased classification accuracy and minimized urban land use overestimation. Four LULC maps have been developed for 1987, 2001, 2015 and 2019. A post-classification comparison method was applied to detect the LULC changes among the independently classified images and quantify the loss of agricultural land by urbanization. Prediction of urban encroachment to agricultural land was performed by Land Change Modeler basing on both the past change trend of urbanization and the influencing distance-to-urban factor. The future LULC prediction maps for 2019, 2050, and 2100 have been generated. Prediction validity was applied using actual vs predicted LULC 2019 maps. The results indicated that the agricultural and urban categories are the prevailing LULC types in the investigated area. Over the last three decades significant loss in the agricultural land was observed together with remarkable growth in the urban area. The agricultural land was decreased by 11.03%, while the urban area was increased by 11.84%. The rate of agricultural land consumption by urbanization after 2001 was more remarkable than the rate before 2001. The results also indicated that the agricultural and urban land use categories have the highest possible probability of change. It is predicted that agricultural land will decrease by 11.5% and 24.5% in 2050 and 2100, respectively. Conversely, urban areas will increase by 12% and 25% in 2050 and 2100, respectively. This process will become the main feature of LULC change in the future, which means that the agricultural land could completely vanish within the next two hundred years at the current degree of agricultural land consumption by urbanization. The outcomes of this research could help policy makers to adopt appropriate strategies towards better management of LULC and increase the sustainability of the highly productive agricultural land through preventing violation on such valuable land.

-Omics biomarker identification pipeline for translational medicine

BackgroundTranslational medicine (TM) is an emerging domain that aims to facilitate medical or biological advances efficiently from the scientist to the clinician. Central to the TM vision is to narrow the gap between basic science and applied science in terms of time, cost and early diagnosis of the disease state. Biomarker identification is one of the main challenges within TM. The identification of disease biomarkers from -omics data will not only help the stratification of diverse patient cohorts but will also provide early diagnostic information which could improve patient management and potentially prevent adverse outcomes. However, biomarker identification needs to be robust and reproducible. Hence a robust unbiased computational framework that can help clinicians identify those biomarkers is necessary.MethodsWe developed a pipeline (workflow) that includes two different supervised classification techniques based on regularization methods to identify biomarkers from -omics or other high dimension clinical datasets. The pipeline includes several important steps such as quality control and stability of selected biomarkers. The process takes input files (outcome and independent variables or -omics data) and pre-processes (normalization, missing values) them. After a random division of samples into training and test sets, Least Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods are applied to identify the most important features representing potential biomarker candidates. The penalization parameters are optimised using 10-fold cross validation and the process undergoes 100 iterations and a combinatorial analysis to select the best performing multivariate model. An empirical unbiased assessment of their quality as biomarkers for clinical use is performed through a Receiver Operating Characteristic curve and its Area Under the Curve analysis on both permuted and real data for 1000 different randomized training and test sets. We validated this pipeline against previously published biomarkers.ResultsWe applied this pipeline to three different datasets with previously published biomarkers: lipidomics data by Acharjee et al. (Metabolomics 13:25, 2017) and transcriptomics data by Rajamani and Bhasin (Genome Med 8:38, 2016) and Mills et al. (Blood 114:1063–1072, 2009). Our results demonstrate that our method was able to identify both previously published biomarkers as well as new variables that add value to the published results.ConclusionsWe developed a robust pipeline to identify clinically relevant biomarkers that can be applied to different -omics datasets. Such identification reveals potentially novel drug targets and can be used as a part of a machine-learning based patient stratification framework in the translational medicine settings.

Patterns of forest cover change and their association with forest management regimes of forest reserves in the high forest zone of Ghana

ABSTRACTThis paper sought to determine how different forest management regimes influence the pattern of forest degradation at different periods in Ghana. The paper tested the hypotheses that timber production forests degrade faster than protection forests and forest degradation slows down with time. For monitoring the changes in forest cover, Landsat TM, ETM+ and Landsat 8 with a spatial resolution of 30 m were used in the mapping of land use/land cover for the periods 1990, 2000, 2010 and 2014 using supervised classification technique. The results showed that forest degradation occurred at different rates within the period and was more rapid from 1990 to 2000 than from 2000 to 2010. Protected forests and Globally Significant Biodiversity Areas (GSBAs) declined in forest quality at the same rate as timber production forests and did not benefit from any extra protective measures. New strategies should be found to manage protection forests and GSBAs to ensure their optimum protection.

PERUBAHAN LUAS LAHAN MANGROVE DAN PENGIKISAN PESISIR JEPARA MENGGUNAKAN ANALISIS KOMPONEN UTAMA SPEKTRAL PENGINDERAAN JAUH

Konversi mangrove menjadi wilayah terbangun di sepanjang pesisir Kecamatan Kedung, Jepara menyebabkan kerentanan wilayah pesisir tersebut terhadap abrasi Laut Jawa. Abrasi telah mengikis wilayah daratan dan menggeser garis pantai. Perubahan luas lahan mangrove dan pergeseran garis pantai dideteksi dengan mengunakan kombinasi kanal merah, NIR, dan SWIR serta rasio kanal merah/SWIR dan SWIR/NIR dari citra Landsat 5 TM, Landsat 7 ETM+, dan Landsat 8 OLI multitemporal 1995-2017. Analisis komponen utama dari semua kanal dan rasio kanal tersebut yang dikombinasikan dengan teknik klasifikasi terbimbing dipilih untuk mengklasifikasikan tutupan lahan, termasuk membedakan vegetasi mangrove dengan vegetasi non mangrove serta membedakan wilayah daratan dengan wilayah lautan. Hasil analisis menunjukkan bahwa perubahan tutupan lahan mangrove menurun sebesar 668,95 ha atau sebesar 55% selama 1995-2017. Penurunan ini diikuti dengan meningkatnya luasan badan air atau laut akibat abrasi dan kawasan tambak. Laju penurunan tutupan lahan mangrove sebesar 30,7 ha/tahun, sedangkan laju peningkatan kawasan tambak sebesar 31,98 ha/tahun dan laju perubahan tutupan lahan pemukiman berkurang sebesar 15,6 ha/tahun. Akibat abrasi, garis pantai di sepanjang pesisir Kecamatan Kedung bergeser dengan laju 12,6 meter per tahun ke arah darat.

Spatial Monitoring of Urban Expansion Using Satellite Remote Sensing Images: A Case Study of Amman City, Jordan

Amman, the capital city of Jordan, faces urbanization challenges and lacks reliable data for urban planning. This study is aimed at assessing, monitoring, and mapping urban land cover using multitemporal Landsat satellite images. Four different land use/cover maps were produced; periods of over ten years between 1987 and 2017 (i.e., in 1987, 1997, 2007, and 2017) were used to evaluate and analyze urban expansion visually and quantitatively. Supervised classification technique followed by the post classification comparison change detection approach was used to analyze images. Over the past three decades, the urban area has increased rapidly in Amman. It increased by 90.78 km2, from 149.08 km2 in 1987 to 237.86 km2 in 2017, with an average annual rate of increase of 2.03%. Urban area increases were significantly higher in the first 10 years of the study period (i.e., from 1987 to 1997), during which the average annual rate of increase reached 3.33%, while it was 2.04% for the last two decades of the study period (i.e., from 1997 to 2017). Urban growth in Amman generally occurred along transport routes away from the core of Amman, and as a result, this growth led to the expansion of urban areas into other types of land use/cover classes, particularly vegetation areas. The spatial analysis of urban expansion and trends of urban growth in Amman could provide the required input data for the urban modeling of the city.

Mapping mangrove forest cover using Landsat-8 imagery, Sentinel-2, Very High Resolution Images and Google Earth Engine algorithm for entire Cambodia

Currently there is limited information to estimate accurate and reliable mangrove forest area in Cambodia. Previous estimates did not explicitly illustrate the spatial distribution of mangrove for the entire country however, according to Global Forest Resource Assessment 2010, Cambodia’s mangrove area cover in 1990 and 2010 was calculated at 79,638 ha and 55,704 ha, respectively using extrapolation of 1992-1996 (original data from Remote Sensing of forest cover 1992/93 and 1996/97) for 1990 and extrapolation of 1996-2002 (original data from Remote Sensing of forest cover 1996/97 and 2002) for 2010. This study mapped the spatial distribution of Cambodia’s mangrove forest derived from 30 m × 30 m spatial resolution and polygon spatial extent from Landsat 8 (L8) image. Publicly available Landsat data was acquired from Google Earth Engine (GEE) Explorer including the Landsat Surface Reflectance-Landsat 8 (L8) OLI/TIRS; Top-Of-Atmosphere (TOA) L8 32-Day Reflectance Composite; Landsat Archive Pre-Collection from US Geological Survey (USGS, 2015); VHR Google-derived images (Collin et al. 2014) and field data collection from 63 plots representing the entire Cambodia of Surface Water Ambient Monitoring Program (SWAMP) to create a Google Fusion Table aiming at mapping the spatial resolution and extent. Random Forest (RF) Classifier, a supervised classification technique, was applied to three L8 images Archive Pre-Collection Level-1 (L8 OLI/TIRS) collected in December 2014, February 2015 and April 2015. Statistical analysis indicates the total area of mangrove forest cover reached 73,240ha with overall classification accuracy of 98.2%, 97.9% and 99.5% for three periods and the validation overall accuracy were 91.5%, 89.1% and 97.6%, respectively. Our findings suggest that L8 imagery and Google Earth Engine algorithm can be used to estimate area changes of mangrove forests in Cambodia with higher accuracy. The results of this study may be useful to assist decision making in planning for mangrove ecosystem restoration activities, evaluation of ecological services and in better estimation of carbon stock in mangrove forest. (Tier 1- default emission or Tier 3- plot data are more related to emission factor).

LAND USE CHANGES MONITORING OVER 30 YEARS AND PREDICTION OF FUTURE CHANGES USING MULTI-TEMPORAL LANDSAT IMAGERY AND THE LAND CHANGE MODELER TOOLS IN RAFSANJAN CITY (IRAN)

The purpose of this study is predicting and modeling of future changes in the Rafsanjan area, using remote sensing and GIS. The multispectral satellite data obtained from Landsat 5 (TM), 7 (ETM+) and 8 (OLI) for the years 1986, 1992, 1998, 2004, 2010 and 2016, were used respectively. The supervised classification technique was applied to multitemporal Landsat images. Rafsanjan city was classified into four major LU classes including urban areas, pistachio gardens, bare-land, and salt. Change detection analysis was performed to compare the quantities of land use class variation between time intervals. The results revealed both increase and decrease of the different LU classes from 1986 to 2016. Generally, the conclusions indicate that during the study period, Urban areas and pistachio gardens have increased by 6.89% (18.47 km2) and 12.76% (34.18 km2) while bare-land and salt have decreased by 13.43% (35.97 km2) and 9.96% (26.68 km2), respectively. In order to predict the future land use changes map, we used the Land change modeler tools of IDRISI software. Consequently, the predicted land use map of 2022 was prepared based on the trend of 30 years of land use changes and effective variables.

Evaluation of the effect of land use change on runoff using supervised classified satellite data

<p>The main objective of this study was to determine the effect of land use change on runoff in Chenar Rahdar watershed. Land use map of the studied basin was determined using Landsat satellite imagery for 2004 and 2015 using ENVI software. After applying the necessary corrections to the images and field surveys to take the educational points, supervised classification technique and maximum probability algorithm were applied to mapping land use change in the study area. According to results, 6 classes of land use were investigated (bare land, rain fed land, forest land, water agriculture land, rangelands and urban lands). In this study, 21 model parameters were calibrated with monthly runoff using 2004-2012 data and validated using 2012-2015 data. The efficiency coefficient for calibration and validation were between 0.88 and 0.94, respectively. The land use changes trend within the time interval showed that the highest percentage of incremental changes is related to urban lands with 108.45%, whereas, the highest decline was observed for agricultural land with 12.46%. In order to investigate the effect of land use change on surface runoff, different land use maps were applied to SWAT model, supposing constant condition for other parameters of the model. The results show that surface runoff increased by 11%, in 2015 compared to 2004. Comprehensive water management can reduce surface runoff in the watershed. The results showed that if all uncertainties were minimized, the calibrated SWAT model can give acceptable runoff simulation results regarding the land use change. These results can be useful for water and environmental resource managers.</p>

Forest degradation monitoring and assessment of biomass in Harenna Buluk District, Bale Zone, Ethiopia: a geospatial perspective

Forest is one of the major natural resources which play a vital role in maintaining the ecological balance of nature. Detection of forest degradation with remote sensing remains a challenging field of study. Over exploitation of forest resources has resulted in the depletion of the extent and quality forests resources. The present study was undertaken to monitor deforestation, forest degradation, and biomass estimation using remote sensing and GIS technology in Harenna Buluk District in Ethiopia. Supervised classification technique was applied to landsat images of 1995, 2005 and 2016. Satellite images were classified into different land-use/land-cover classes using maximum likelihood algorithm with the aid of field observations and Google Earth. Forest degradation was assessed using object-based image analysis from Sentinel-2A satellite image. Results of this study have revealed that during 1995–2005 and 2005–2016, forest and shrubland areas were decreased by 119.2 km2 (6.19%) and 12.1 km2 (0.62%), respectively. The extent of farmland, bareland, grassland, settlement and water body were increased by 99.94 km2 (5.19%), 43.11 km2 (2.24%), 75.78 km2 (3.94%), 20.54 km2 (1.06%) and 6.93 km2 (0.36%), respectively. As a result, the biomass dramatically decreased. The normalized difference vegetation index (NDVI) value decreased significantly during 1995−2016 indicating that the vegetation cover in the study area was highly disturbed. The estimated degradation during the year 2016 was 145.91 km2 (7.58%), while deforestation accounted for 171.39 km2 (8.91%) of the total study area. Thus, advanced satellite images are useful to monitor forest cover and degradation processes of natural habitats in the context of human related impacts.

Application of Ca-Markov Model for the Analysis of Urban Growth in Kenyir Basin

The Modelling and Simulation of land cover change are fundamental to the evaluation of successive environmental effects. Remote Sensing and Geographical Information System (GIS) give major procedures which can be valuable in the analysis at the town locale as well as the city levels. The study project the future land use change of Kenyir Basin in Hulu Terengganu. RS data from Thematic Mapper sensor of 2000, 2009 and Operational Land Imager of 2016 were used for the analysis. The study used supervised classification technique for classification, and Cellular Automata Markovian (CA-Markov) Model analysis was used for future projection of 2030. The result classified water body, vegetation and urban area. Modelling analysis reveals a continuous pattern of urban growth in the Kenyir Basin from 5.04 km2, 79.51 km2, 117.03 km2 and 220 km2 in the years 2001, 2009, 2016 and the projected the year 2030 with 0.6934 K-standard value of stimulation. This study gives a chance to characterise and apply better methodologies for environmental management of land use to make improved coordination between urban growths, and conservation of natural environment.