Monitoring urban land cover and vegetation change by multi-temporal remote sensing information

In order to analyze changes in human settlement in Xuzhou city during the past 20 years, changes in land cover and vegetation were investigated based on multi-temporal remote sensing Landsat TM images. We developed a hierarchical classifier system that uses different feature inputs for specific classes and conducted a classification post-processing approach to improve its accuracy. From our statistical analysis of changes in urban land cover from 1987 to 2007, we conclude that built-up land areas have obviously increased, while farmland has seen in a continuous loss due to urban growth and human activities. A NDVI difference approach was used to extract information on changes in vegetation. A false change information elimination approach was developed based on prior knowledge and statistical analysis. The areas of vegetation cover have been in continuous decline over the past 20 years, although some measures have been adopted to protect and maintain urban vegetation. Given the stability of underground coal exploitation since 1990s, urban growth has become the major driving force in vegetation loss, which is different from the vegetation change driven by coal exploitation mainly before 1990.

Land Use Land Cover Analysis for Godavari Basin in Maharashtra Using Geographical Information System and Remote Sensing

The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-cover and climatic conditions.This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and 2019.For this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were taken.Secondly,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were used.Thirdly,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were employed.For Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under research.The composition of red,green,and near infra-red bands was used for supervised classification.Classified images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to 2019.It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

Improving the Interpretability and Reliability of Regional Land Cover Classification by U-Net Using Remote Sensing Data

The accurate and reliable interpretation of regional land cover data is very important for natural resource monitoring and environmental assessment.At present,refined land cover data are mainly obtained by manual visual interpretation,which has the problems of heavy workload and inconsistent interpretation scales.Deep learning has greatly improved the automatic processing and analysis of remote sensing data.However,the accurate interpretation of feature information from massive datasets remains a difficult problem in wide regional land cover classification.To improve the efficiency of deep learning-based remote sensing image interpretation,we selected multisource remote sensing data,assessed the interpretability of the U-Net model based on surface spatial scenes with different levels of complexity,and proposed a new method of stereoscopic accuracy verification(SAV)to evaluate the reliability of the classification result.The results show that classification accuracy is more highly correlated with terrain and landscape than with other factors related to image data,such as platform and spatial resolution.As the complexity of surface spatial scenes increases,the accuracy of the classification results mainly shows a fluctuating declining trend.We also find the distribution characteristics from the SAV evaluation results of different land cover types in each surface spatial scene.Based on the results observed in this study,we consider the distinction of interpretability and reliability in diverse ground object types and design targeted classification strategies for different surface scenes,which can greatly improve the classification efficiency.The key achievement of this study is to provide the theoretical basis for remote sensing information analysis and an accuracy evaluation method for regional land cover classification,and the proposed method can help improve the likelihood that intelligent interpretation can replace manual acquisition.

An Integrated Remote Sensing and GIS in Monitoring Landuse and Land Cover Change in Egbeda Local Government Area, Oyo State, Nigeria

Effective planning relies on accurate and up-to-date information on existing land use and land cover. The timely detection of trends in land use and land cover change and a quantification of such trends are of specific interest to planners and decision makers. The aim of this research is to use remote sensing and GIS to monitor landuse and land cover change in Egbeda Local Government Area, Oyo State with a view to determining how useful such information can be to planners and decision makers for effective urban management. The research was conducted using remote sensing and Geographical information System at determining the trend and extent of land use and land cover change and its driving force in Egbeda Local Government Area, Oyo State. The methods used include: digitization, digital image processing and spatial analysis using an inverse distance weighted (IDW) technique, Maximum likelihood supervised classification and post classification change detection techniques were applied to Landsat imageries acquired in 1984, 2006 and 2018. Imageries were classified into built-up area, vegetation, bare surface, cultivation and water body. The results of the analysis obtained showed drastic change in built-up area which rose to 32.8% from 25.4% between 1984 and 2018 periods. To reduce the effect of land use expansion in the study areas, policy measures were recommended which include proper inventory of land use and land cover, regular monitoring of urban areas spread of development and regional development programs. These will enable the government, stakeholders, policy makers and planners to make informed decisions provided by these technologies to attain and sustain future urban development.

Characterizing urban expansion of Korla City and its spatial-temporal patterns using remote sensing and GIS methods

Cities provide spatial contexts for populations and economic activities. Determining the spatial-temporal patterns of urban expansion is of particular significance for regional sustainable development. To achieve a better understanding of the spatial-temporal patterns of urban expansion of Korla City, we explore the urban expansion characteristics of Korla City over the period 1995-2015 by employing Landsat TM/ETM＋ images of 1995, 2000, 2005, 2010, and 2015. Urban land use types were classified using the supervised classification method in ENVI 4.5. Urban expansion indices, such as expansion area, expansion proportion, expansion speed, expansion intensity, compactness, and fractal dimension, were calculated. The spatial-temporal patterns and evolution process of the urban expansion （e.g., urban gravity center and its direction of movement） were then quantitatively analyzed. The results indicated that, over the past 25 years, the area and proportion of urban land increased substantially with an average annual growth rate of 15.18%. Farmland and unused land were lost greatly due to the urban expansion. This result might be attributable to the rapid population growth and the dramatic economic development in this area. The city extended to the southeast, and the urban gravity center shifted to the southeast as well by about 2118 m. The degree of urban compactness tended to decrease and the fractal dimension index tended to increase, indicating that the spatial pattern of Korla City was becoming loose, complex, and unstable. This study could provide a scientific reference for the studies on urban expansion of oasis cities in arid land.

Remote Sensing Data and SLEUTH Urban Growth Model:As Decision Support Tools for Urban Planning

Sri Lanka is experiencing speedy urbanization by converting the agriculture land and other natural land cover into built-up land. The urban population of Sri Lanka is expected to reach to 60% by 2030 from 14% in 2010. The rapid growth in urban population and urban areas in Sri Lanka may cause serious socioeconomic disparities, if they are not handled properly. Thus, planners in Sri Lanka are in need of information about past and future urban growth patterns to plan a better and sustainable urban future for Sri Lanka. In this paper, we analyzed the characteristics of past land use and land cover trends in Matara City of Sri Lanka from 1980 to 2010 to assess the historic urban dynamics. The land use change detection analysis based on remote sensing datasets reveal that the conversion of homestead/garden and paddy into urban land is evident in Matara City. The historic urban trends are projected into the near future by using SLEUTH urban growth model to identify the hot spots of future urbanization and as well as the urban growth patterns in Matara City up to the basic administrative level, i.e., Grama Niladari Divisions(GND). The urban growth simulations for the year 2030 reveal that 29 GNDs out of 66 GNDs in Matara City will be totally converted into urban land. Whereas, 28 GNDs will have urban land cover from 75% to 99% by 2030. The urban growth simulations are further analyzed with respect to the proposed Matara city development plan by the Urban Development Authority(UDA) of Sri Lanka. The results show that the UDA's city development plan of Matara will soon be outpaced by rapid urbanization. Based on the calibration and validation results, the SLEUTH model proved to be a useful planning tool to understand the near future urbanization of Sri Lankan cities.

An Appraisal of Land Use/Land Cover Change Scenario of Tummalapalle, Cuddapah Region, India—A Remote Sensing and GIS Perspective

The study was aimed at appraising the changing land use/land cover scenario of Tummalapalle region in Cuddapah district of Andhra Pradesh using Remote sensing data and GIS technology. The region is considered as it has rich uranium reserves and is experiencing a remarkable expansion in recent times. The land use/land cover change analysis was carried out using IRS P6 LISS-III and LANDSAT-8 OLI multitemporal data pertaining to the years 2006 and 2016. The image classification resulted in five major land use/land cover classes namely built-up, agricultural, forest, wasteland and water bodies. The study noticed that the areas under built-up and agricultural classes are found increased from 0.94 km2 (0.84%) to 2.75 km2 (2.44%) and 61.68 km2 (54.84%) to 63.91 km2 (56.82%), respectively during 2006-2016. Area under forest, wasteland and water bodies are found decreased considerably from 3.09 km2 (2.75%) to 0.86 km2 (0.76%), 43.71 km2 (38.56%) to 42.60 km2 (37.88%) and 3.05 km2 (2.71%) to 2.35 km2 (2.09%), respectively. The study recommends development of industrial based economy by optimally utilizing the existing land resource (scrub and wasteland classes) and simultaneously extending the agricultural practices to other possible areas to make them more productive.

The Use of GIS, Remote Sensing and Shannon’s Entropy Statistical Techniques to Analyze and Monitor the Spatial and Temporal Patterns of Urbanization and Sprawl in Zarqa City, Jordan

The aim of this study is to understand and quantify the urban growth and trend in Zarqa city during the period 1990 to 2014 and to produce land use and cover map for the studied area through the use of the GIS and remote sensing techniques with Shannon’s Entropy statistical method. For this purpose, three Landsat images were used for land use classification by using supervised maximum likelihood classification techniques to extract and assess the changes of urban lands. The results indicated that the urban areas in Zarqa city increased by 22.15% in the period from 1990 to 2005 and 14.86% from 2005 to 2014, with a rate of expansion of 0.96 and by 1.31 km2/ year for the two time periods respectively. The entropy value increased from 1.20 in the first period to 1.38 in the second, while the entropy value for the NE, NW, SE and SW zones showed high values, which confirmed that urban expansion and sprawling had existed in the past twenty four years in the study area. Urban expansion and sprawl cause different impacts on the natural, economic, and aesthetic aspects of the city which lead and guide government officials and planners to understand and monitor current growth and visualize future growth.

Study the Urban Expansion of Taif City Using Remote Sensing and GIS Techniques for Decision Support System

The goal of this study is to spatially portray Taif’s urban expansion and determine for last 30 years, from 1990 to 2020. It is only including the residential neighborhoods approved by the Taif Municipality, which is responsible and organized for urban planning in the city. The geographical location of the city of Taif is a vital crossroad between eastern and western parts of the Kingdom of Saudi Arabia, which made it a tourist destination, as well as commercial and agricultural preference for many years, as it was considered the summer capital of the KSA. Moreover, it serves as the entrance to Makkah city from the eastern side. The proposed study has necessitated because the lack of recent scientific studies that dealt with the spatial analysis of urban expansion and its trends in the city of Taif and follow the stages of expansion during periods of time by relying on remote sensing and geographic information systems (GIS) techniques. The many development projects in the city of Taif, such as Taif International Airport, the new Taif project, and other projects, which will cause an increase in demand for residential, commercial, industrial and service units have also prompted the proposed study. This was investigated using a multitemporal Landsat data for the years of 1990, 2002 and 2020, as well as census data from 1990 to 2020, along with Remote Sensing (RS) and Geographic Information System (GIS) techniques. The results revealed that over the last 30 years, urban land cover has increased by 20,448 (ha) whereas other land covers, such as green area, have decreased significantly by 14,554 (ha). The results also indicate that the increase in urban areas amounted to 114.8% during the period from 1990 to 2020. The locations of new developments such as Taif airport, Taif university, Ministry of Housing projects, etc. were located to the North and Northeast. This is due to the area’s topography, which played a major role in determining the direction of urban expansion. According to the study, multiple urban centers, rising low-density dispersed communities, and leapfrogging growth were all hallmarks of urban expansion in Taif. The study demonstrated that Taif is at risk of ecosystem loss as a result of continued urban expansion. To ensure environmental sustainability, the current effort asks for actions that will restrict urban sprawl and prepare the city for future growth.

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usually result in the change in the land use/land cover change (LULC). Pokhara Metropolitan is influenced mainly by the combination of various driving forces: geographical location, high rate of population growth, economic opportunity, globalization, tourism activities, and political activities. In addition to this, geographically steep slope, rugged terrain, and fragile geomorphic conditions and the frequency of earthquakes, floods, and landslides make the Pokhara Metropolitan region a disaster-prone area. The increment of the population along with infrastructure development of a given territory leads towards the urbanization. It has been rapidly changing due to urbanization, industrialization and internal migration since the 1970s. The landscapes and ground patterns are frequently changing on time and prone to disaster. Here a study has been carried to study on LULC for the last 18 years (2000-2018). The supervised classification on Landsat Imagery was performed and verified the classification through computing the error matrix. Besides, the water bodies and vegetation area were extracted through the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDWI) respectively. This research shows that during the last 18 years the agricultural areas diminishing by 15.66% while urban area is increasing by 13.2%. This research is beneficial for preparing the plan and policy in the sustainable development of Pokhara Metropolitan.

The Use of Remote Sensing and Landscape Metrics to Analyze Land Use Evolution in Rural Areas of the Municipality of Passo Fundo/RS(Brazil)

The use of remote sensing in the design of land use mapping allows analyses of landscape evolution during a certain period of time which helps studies in a global scope.The objective of this study is to identify and analyze changes in characteristics of rural land use in the municipality of Passo Fundo,located in the state of Rio Grande do Sul,Brazil,during the years 2001 and 2020,through images taken from the Landsat TM-7 and TM-8 satellites.Methodologically,satellite images were classified by supervised methods,generating thematic maps,and taking into account the following groups:tillage(growing area),forest,exposed soil and water resources.Results demonstrated that the process of connecting agricultural crop patches went from 5.495 in 2001 to a figure of 10.812 in 2020,thus having an increase of 96%.

ART Based Reliable Method for Prediction of Agricultural Land Changes Using Remote Sensing

This paper focuses on prediction of change in agricultural lands by using ART2 algorithm. The existing method used ENVI and ARCGIS software to predict the changes in land, which showed less accuracy due to human errors. To overcome this user friendly GUI based ART2 algorithm has been developed in java to obtain more accuracy in prediction of changes in land. The input is satellite temporal images of the years 1990 and 2014. By using the ART2 algorithm, the input images of the years 1990 and 2014 are classified, where the features are identified to form cluster. The clustered image is given as input and pixel to pixel comparison method in ART2 is implemented in java, for detecting the changes in agricultural lands. The comparison results of ENVI and ARCGIS and GUI based ART2 with in situ data show that the prediction of changes in agricultural land is more accurate in the case of GUI based ART2 implementation.

Combined Remote Sensing and GIS Techniques for Studying the Large Roman Urban System Expansion during the Last Twenty Years

Several factors may contribute to on-going challenges for spatial planning and policy in megacities such as Rome, including rapid population shifts, poorly organized areas, and lack of data through which monitoring urban growth and land use change. This research was conducted to examine past and current effects of the urbanization process, occurred over the large Roman urban system, on the basis of multi-source and multi-temporal optical remote sensing (RS) data, collected between 1990 and 2013. These changes were then validated via Geographic Information System (GIS) techniques, in a particular procedure applied to urban land/agricultural transformations. The proposed approach, based on geo-statistical methods, was used to calculate the index of innovative space (AP Index), useful for the monitoring of the urban sprawl phenomenon. Strong evidence of urban expansion over the north-eastern quarter of the city, accompanied by environmental degradation and loss of biodiversity, is provided. Urban infill developments are expected to emerge in the south-eastern areas too, and these might increase urban pressure as well. In conclusion, RS and GIS technologies together with ancillary data can be used to assist decision makers in preparing future plans to find out appropriate solutions to urbanization encroachment.

Towards a Remote Sensing and GIS-Based Technique to Study Population and Urban Growth: A Case Study of Multan

In recent decades, the migration rates of the large cities of Punjab have been risen up to a considerable level due to the lack of employment opportunities as well as lack of facilities in the rural areas of the province. It has caused an unprecedented and unplanned urbanization across the urban areas of the province. This study has been undertaken to perform fractal analysis about the sprawl in rapidly growing city. GIS and remote sensing data have been used in this study as an emerging technology which is cost effective as well as accurate at the same time. Landsat images have been taken for the study and the sprawl has been calculated with the analysis of the data of each decade for past more than 40 years. It has been observed that the built up area is 47.8 to 141.12 Sq. Km whereas the pattern of urban settlement has been classified as clustered and linear, following the roads network. The temporal population growth also seconded these results. The population growth rate and population density increase, are based on the pixel based extraction of the data from satellite imagery for the period of 2000 to 2014, which is taken as a decision support tool. In 2000, the population of the district increased from 2,071,694 (1981 census) to 2,939,907 and then in 2013, it became 4,384,191 at a rate to 2.93% upturn per annum. Moreover, the study also reveals the extent of the growth of other land uses as well which may be taken as a reference that in an agricultural country like Pakistan, the natural resources are being wasted (by urbanization of the fertile land). There must be some master planning to avoid such things in the other cities as well.

Analysis of Urban Greening of Tai'an City in China Based on Remote Sensing Image with High Resolution

By taking urban greening of Tai'an City of Shandong Province for example,selecting remote sensing image Quickbird with high resolution,and combining visual interpretation with automatic classification of the computer,based on urban green space systematic planning map,green space information of the built-up area has been selected for the research centering on green lands in urban parks,productive green lands,green lands attached to residential areas and units,green lands attached to the road,other green lands,water surfaces and so on.Through the statistics and analysis,the distribution condition of each type of urban green land has been obtained,and some suggestions have been proposed in view of existing problems of urban greening.It should enhance the construction of green lands in urban parks,residential areas and units,improve road greening level,implement vertical greening,increase the area of productive green lands and fully make use of idle lands.

Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China

China has experienced rapid urbanizations with dramatic land cover changes since 1978. Forest loss is one of land cover changes, and it induces various eco-environmental degradation issues. As one of China’s hotspot regions, the Guangdong-Hong KongMacao Greater Bay Area(GBA) has undergone a dramatic urban expansion. To better understand forest dynamics and protect forest ecosystem, revealing the processes, patterns and underlying drivers of forest loss is essential. This study focused on the spatiotemporal evolution and potential driving factors of forest loss in the GBA at regional and city level. The Landsat time-series images from 1987 to2017 were used to derive forest, and landscape metrics and geographic information system(GIS) were applied to implement further spatial analysis. The results showed that: 1) 14.86% of the total urban growth area of the GBA was obtained from the forest loss in1987–2017;meanwhile, the forest loss area of the GBA reached 4040.6 km2, of which 25.60%(1034.42 km2) was converted to urban land;2) the percentages of forest loss to urban land in Dongguan(19.14%), Guangzhou(18.35%) and Shenzhen(15.81%) were higher than those in other cities;3) the forest became increasingly fragmented from 1987–2007, and then the fragmentation decreased from2007 to 2017);4) the landscape responses to forest changes varied with the scale;and 5) some forest loss to urban regions moved from low-elevation and gentle-slope terrains to higher-elevation and steep-slope terrains over time, especially in Shenzhen and Hong Kong.Urbanization and industrialization greatly drove forest loss and fragmentation, and, notably, hillside urban land expansion may have contributed to hillside forest loss. The findings will help policy makers in maintaining the stability of forest ecosystems, and provide some new insights into forest management and conservation.

A Review of Researches on Deep Learning in Remote Sensing Application

In recent years, deep learning has been widely used in the field of image understanding and made breakthroughs research progress in image understanding. Because remote sensing application and image understanding are inseparable, researchers have carried out a lot of research on the application of deep learning in remote sensing field, and extended the deep learning method to various application fields of remote sensing. This paper summarizes the basic principles of deep learning and its research progress and typical applications in remote sensing, introduces the current main deep learning model and its development history, focuses on the analysis and elaboration of the research status of deep learning in remote sensing image classification, object detection and change detection, and on this basis, summarizes the typical applications and their application effects. Finally, according to the current application of deep learning in remote sensing, the main problems and future development directions are summarized.

Analysis of Urban Land Use and Its Effects on Air Environment in Chengdu, Western China from 1992 to 2008

Large-scale development of urban land use has led to change of a variety of natural processes and ecological processes, resulting in complex eco-environmental consequences. The objective of this study was to analyze the urban land use and its impact on air environment effect in Chengdu, western China from 1992 to 2008 following the RS (Remote Sensing) and GIS technique. The environmental effects data of urban land use was extracted and analyzed by overlaying layers of urban land use and the density of nitrogen dioxide and total suspended particulate matter in sampling points data concerning to the air quality of the environment in Chengdu based on GIS spatial analysis method. The results show that the main feature of urban land use change was substantial reduction of cultivated land and construction land and forest land increased significantly within the study area from1992 to 2008. The temporal-spatial change was notable in study period time. Land use has a significant impact on urban air environment, the chroma change of nitrogen dioxide derived from forest land was obvious, the area occupied by different nitrogen dioxide chroma was the largest. The urban land use impact on the highest class chroma of total suspended particulate matter was notable and its area was the greatest. The results show also the spatial distribution of nitrogen dioxide chroma and total suspended particulate matter chroma in study area is reduced following from Qingbaijiang District-Xindu District-downtown to both sides. The spatial distribution of industry, mining and traffic land is basically the same chroma spatial distribution. Therefore, the results of this study provide a scientific basis for improvement air environment quality, the urban sustainable development and a scientific response for decisions from the municipal governments.

Remote Sensing and GIS Application on Forest Resource Mapping and Monitoring in Bulolo District, Morobe Province

Forest resources monitoring are particularly challenging for tropical forest due to their diverse composition and structure and a wide range of stakeholder’s expectations and requirement. New monitoring approaches and control policies directions are required to meet these different challenges. For the past decades, much of the focus of formal forest monitoring and management policy in Papua New Guinea (PNG) has been on large scale conventional harvesting to meet national requirements for economic development, with little attention given to community or small area forest management and monitoring. The current management is considered to be unsustainable and, as forest resources from primary forests are exhausted. This has resulted in extensive cutover forest areas being left to degrade over time. Forest reserve has suffered seriously and if the present trend of deforestation continues;it is just a matter of time when the whole reserve would have been converted to a bare ground. This study therefore examined the integration of remote sensing (RS) and geographic information system (GIS) application on forest resource mapping and monitoring in Bulolo district, Morobe province. Landsat satellite imageries for 1992, 2002 and 2014 were used to classify and identify forest changes through change detection techniques. A GIS database of land use categories and their location within 24 years (1992-2014) were generated and analysed with the aid of GIS analytical functions. This function includes area calculation, overlay, and image differencing, supervised classifications, cross tabulations and map representation. The result shows that population growth (anthropogenic) factors among communities around the natural forest imposes a lot of pressure on the natural forest resources. This should also include consideration of the future usage capacity of the forest resources as well as development of the capacity of local forest owner communities to participate in small scale forest management and utilization.