Land Use and Cover Mapping with Airborne Hyperspectral Imager in Setiu, Malaysia

In terrestrial biomes, ecologists and conservation biologists commonly need to understand vegetation characteristics such as structure, primary productivity, and spatial distribution and extent. Fortunately, there are a number of airborne and satellite sensors capable of providing data from which you can derive this information. We will begin this chapter with a discussion on mapping land cover and land use. This is followed by text on monitoring changes in land cover and concludes with a section on vegetation characteristics and how we can measure these using remotely sensed data. We provide a detailed example to illustrate the process of creating a land cover map from remotely sensed data to make management decisions for a protected area. This section provides an overview of land cover classification using remotely sensed data. We will describe different options for conducting land cover classification, including types of imagery, methods and algorithms, and classification schemes. Land cover mapping is not as difficult as it may appear, but you will need to make several decisions, choices, and compromises regarding image selection and analysis methods. Although it is beyond the scope of this chapter to provide details for all situations, after reading it you will be able to better assess your own needs and requirements. You will also learn the steps to carry out a land cover classification project while gaining an appreciation for the image classification process. That said, if you lack experience with land cover mapping, it always wise to seek appropriate training and, if possible, collaborate with someone who has land cover mapping experience (Section 2.3). Although the terms “land cover” and “land use” are sometimes used interchangeably they are different in important ways. Simply put, land cover is what covers the surface of the Earth and land use describes how people use the land (or water). Examples of land cover classes are: water, snow, grassland, deciduous forest, or bare soil.

Information on land cover and its changes is essential for rational planning of agricultural land use, sustainable management of agricultural land, increase of crop production, and environmental protection. Yet, in most developing countries such information is non-existent or not reliable. This chapter describes how the Food and Agriculture Organization of the United Nations (FAO) assists developing countries with land cover mapping and establishment of land cover databases. FAO assistance to developing countries in the field of land cover mapping is based on a three-pronged approach: ● Development of standardized methodologies for land cover and land use classification and mapping appropriate for developing countries, ● Providing assistance with land cover and land use mapping and establishment of associated digital databases, ● Strengthening the institutional capacities for land cover and land use mapping and monitoring in developing countries.

The main objective of this research work is to produce the Land Use and Cover Map of So Paulo State in scale 1: 2.500.000, and also to provide a diverse set of thematic maps of So Paulo State in digital format and in the context of a Geographic Information System. The Land Use and Cover Map have the purpose to characterize the diverse land use and cover forms observed in that territory. This mapping regional scale methodological approach is based on the use of high temporal resolution satellite images and advanced digital image processing and GIS technologies as an analysis and integration tool of Geography, Cartography and Agronomy science knowledge, creating a possibility of survey of geographical space linking it with the interference and actions of society in nature. The study area is located in southeastern Brazil and has an area of 248.209.426 km 2 distributed in 645 municipalities. In this work the segmentation technique and linear mixture model were applied in the sensor MODIS images, to facilitate the delimitation of the main land use and land cover classes. The land use and land cover classification was developed based on KELLER (1969), ANDERSON (1971), et al. (1979), JENSEN (1983) and TROPPMAIR (1983) classical mappings. Vegetation indices (NDVI and EVI) products contributed in gauging the thematic classes of vegetation, the CANASAT project data (INPE) in the agricultural classes and CBERS satellite images and Google Earth as well in the others. Several thematic maps in digital format were made for the study area characterization, Geology, Geomorphology, Watersheds, Climate, Primitive Vegetation Cover, some comings from the Atlas of So Paulo State project: a methodological reflection (MARTINELLI, 2009). The topography and slope maps were obtained from Shuttle Radar Topographic Mission (SRTM) data. The land use and land cover mapping results highlights the agricultural supremacy of cane sugar crop, which has been pressing and substituting other crops, the reforestation and environmental conservation areas as well. This wild occupation had already been appointed since 2003 by RUDORFF et al. (2004) pushed by ethanol production demand as a bio-fuel. Therefore, we hope that the results presented in this work contributes to future studies regarding the threat to fragments of Cerrado and Atlantic Forest remnants that are still present in So Paulo State, besides the possibilities to put on line in a WebGIS format the elaborated thematic maps for its widespread use and dissemination by the national and international community.

Mapping farming systems in an agricultural landscape is important for quantifying ecosystem services provided by landscapes such as management of pests and pathogens, conservation of beneficial arthropods, improving crop pollination and crop yields. Landscape structure is defined as the spatial arrangement (configuration) of land cover types (composition) that influence ecological processes and biodiversity distribution at varying scales. Human induced landscape modification of once-pristine natural environment has resulted to habitat loss and disturbance of species communities and their interaction, which has compromised on ecosystem functioning and service provision. Big data from earth observation and machine learning algorithms provide unprecedented opportunity for mapping these agricultural landscapes. However, continuous cloud cover and intercropping in smallholdings poses challenges in landscape characterisation especially in the tropics. We conducted our study in Murang'a County, which is one of the five counties in central Kenya that grow coffee and tea. Coffee grows in four sub zones of the upper Midland (UM) agro-ecological zone that cuts across an elevation gradient of 1300 – 2000 masl. UM1 is the transition zone for growing tea and coffee. UM2 is the major coffee growing zone whereas UM3 is the marginal coffee zone. At UM4, coffee is grown under irrigation. We explored multi-source satellite images to evaluate the best dataset with least cost for mapping coffee farms and identified fragmentation levels of cover types in each agroecological subzone. Using reference data from Google Earth, commercially available PlanetScope (3m spatial resolution) and freely available Sentinel 2 (10m spatial resolution) and Landsat 8 (30m spatial resolution) images combined with vegetation indices (VI) were tested using a random forest classifier. The dataset with the highest accuracy for land use land cover classification was further analyzed using fragstats software to measure landscape fragmentation. 13-band Sentinel 2 had the highest accuracy for mapping coffee (kappa - 0.98) compared to 4-band PlanetScope (kappa – 0.85) and 11-band Landsat 8 (kappa – 0.88). Despite having the highest spatial resolution, PlanetScope had the lowest accuracy which only improved when combined with VI. The final land use land cover map from Sentinel 2 mapped the following land use land cover classes: annuals, bananas, bareland, coffee, agroforest, grassland, perennials/shrubs, settlements, tea and waterbody. Coffee covered over 50% of the total landscape in UM1 and UM2 while annual crops occupied 43% of the total landscape in UM3. Other perennials and annual crops occupied 29% and 22% of UM4 respectively. The results also showed that Coffee was highly fragmented in UM3 and UM4 with the largest patch occupying 3% and 1.4% of the total landscape compared to UM1 which occupied 62% of the total landscape. Additionally, Agroforest and bananas were more fragmented in UM2 and UM3 while annual crops, shrubs, grassland and settlements in UM4. We show in this study that Sentinel 2 is the most robust satellite data to map land use and cover types with high level of accuracies even in challenging landscapes such as smallholder agroecosystems. This is due to high number of spectral bands that delineates vegetation type more accurately compared to other satellites. Furthermore, it has the added advantage of being freely available; this dataset can be taken up easily in resource-constrained projects. UM3 and UM4 are the marginal coffee growing zones. Within these landscapes, coffee is highly fragmented and interspersed with annual crops which are the dominant cover type. Naturally, coffee grows as an understorey crop. At UM3 and UM4, agroforest trees are also highly fragmented, hence, within these landscapes coffee is more vulnerable to pest and disease infestation, low crop yields and adverse effects of climate change compared to coffee in UM1 and UM2.

Over the last 18 years, Indonesia has experienced significant deforestation due to the expansion of oil palm and rubber plantations. Accurate land cover maps are essential for policymakers to track and manage land change to support sustainable forest management and investment decisions. An automatic digital processing (ADP) method is currently used to develop land cover change maps for Indonesia, based on optical imaging (Landsat). Such maps produce only forest and non-forest classes, and often oil palm and rubber plantations are misclassified as native forests. To improve accuracy of these land cover maps, this study developed oil palm and rubber plantation discrimination indices using the integration of Landsat-8 and synthetic aperture radar Sentinel-1 images. Sentinel-1 VH and VV difference (>7.5 dB) and VH backscatter intensity were used to discriminate oil palm plantations. A combination of Landsat-8 NDVI, NDMI with Sentinel-1 VV and VH were used to discriminate rubber plantations. The improved map produced four land cover classes: native forest, oil palm plantation, rubber plantation, and non-forest. High-resolution SPOT 6/7 imagery and ground truth data were used for validation of the new classified maps. The map had an overall accuracy of 92%; producer’s accuracy for all classes was higher than 90%, except for rubber (65%), and user’s accuracy was over 80% for all classes. These results demonstrate that indices developed from a combination of optical and radar images can improve our ability to discriminate between native forest and oil palm and rubber plantations in the tropics. The new mapping method will help to support Indonesia’s national forest monitoring system and inform monitoring of plantation expansion.

In recent years, the availability of georeferenced data has increased substantially, as have the number of producers and users of this information. As a consequence, there is a growing need for harmonization of data, not least in its classification descriptions. Unfortunately, inadequate metadata hampers understanding of how data sets are produced and what data classes represent. This study describes how five different categorical geodata sets for Denmark, ranging from habitat registrations through maps of agricultural land use to national topographic data, are integrated and how the integrated data set is reclassified to land-use and land-cover classes. All five data sets differ with respect to data acquisition, and description and classification methodologies, and none of the data distinguish between land use and land cover. The purpose of the reclassification was to produce maps of land use and land cover, with classes being compatible with the land cover classification system (LCCS) from the Food and Agriculture Organization of the United Nations. We identified land-cover and land-use classes from the LCCS that matched Danish conditions and cross-tabulated those classes with classes from the integrated Danish data set. Based on the semantic meaning of the class names from the integrated data set, we used heuristic associational knowledge to estimate their membership in the land-use and land-cover classes. The results are three land-use maps and five land-cover maps, indicating qualitative estimates of the presence of land-cover classes measured on an ordinal scale.

Decision Tree Classification of Land Use and Land Cover of the Tapajos National Forest Region, Brazilian Amazon The Amazon rainforest covers an area of approximately 5 million km2 and is responsible for harbouring much of the planet biodiversity. Despite its importance this region suffers constantly with the deforestation process and is a source of study and the center of attention of the scientific community worldwide. The Tapajos National Forest is an important reference unit for conservation of tropical forest resources and is often the target of several studies. However, there are few studies that integrate different information from data collected remotely in the mapping land use and land cover in the Tapajos National Forest. In this context, the main objective of this study was evaluate the use of the technique of decision tree for map the land use and land cover in the Tapajos National Forest region, including classes of forest degradation and regeneration. For this, we used data mining technique, known as decision tree, and as input data for creating the decision tree we used different information obtained from an optical image of the sensor TM of the satellite Landsat 5 and this image is from the year 2009. Therefore, the data that were used in the decision tree were the six bands of Landsat 5 TM sensor of the year 2009, the three-fraction images (soil, shade and vegetation) obtained by the Linear Spectral Mixture Model, the three vegetation indices, Normalized Difference Vegetation Index, Normalized Water Index and Soil-Adjusted Vegetation Index. Through this work we concluded that the use of decision tree enabled the integration of the information obtained from the image Landsat 5 TM. Moreover, the classification of land cover and land use the Tapajos National Forest showed satisfactory results with Kappa index of 0.79. Approximately 81.2% of the pixels were classified correctly and approximately 18.8% of the pixels were classified incorrectly by the decision tree. The largest classification errors occurred between classes of pasture, regeneration, forest and degraded forest. The classes that showed the best results in classification were the classes water, cloud and cloud shadow.

Land cover type is a crucial parameter that is required for various land surface models that simulate water and carbon cycles, ecosystem dynamics, and climate change. Many land use/land cover maps used in recent years have been derived from field investigations and remote-sensing observations. However, no land cover map that is derived from a single source (such as satellite observation) properly meets the needs of land surface simulation in China. This article presents a decision-fuse method to produce a higher-accuracy land cover map by combining multi-source local data based on the Dempster–Shafer (D–S) evidence theory. A practical evidence generation scheme was used to integrate multi-source land cover classification information. The basic probability values of the input data were obtained from literature reviews and expert knowledge. A Multi-source Integrated Chinese Land Cover (MICLCover) map was generated by combining multi-source land cover/land use classification maps including a 1:1,000,000 vegetation map, a 1:100,000 land use map for the year 2000, a 1:1,000,000 swamp-wetland map, a glacier map, and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001). The merit of this new map is that it uses a common classification system (the International Geosphere-Biosphere Programme (IGBP) land cover classification system), and it has a unified 1 km resolution. The accuracy of the new map was validated by a hybrid procedure. The validation results show great improvement in accuracy for the MICLCover map. The local-scale visual comparison validations for three regions show that the MICLCover map provides more spatial details on land cover at the local scale compared with other popular land cover products. The improvement in accuracy is true for all classes but particularly for cropland, urban, glacier, wetland, and water body classes. Validation by comparison with the China Forestry Scientific Data Center (CFSDC)–Forest Inventory Data (FID) data shows that overall forest accuracies in five provinces increased to between 42.19% and 88.65% for our MICLCover map, while those of the MODIS2001 map increased between 27.77% and 77.89%. The validation all over China shows that the overall accuracy of the MICLCover map is 71%, which is higher than the accuracies of other land cover maps. This map therefore can be used as an important input for land surface models of China. It has the potential to improve the modeling accuracy of land surface processes as well as to support other aspects of scientific land surface investigations in China.

Land cover mapping in Latvia is performed as part of the Corine Land Cover (CLC) initiative every six years. The advantage of CLC is the creation of a standardized nomenclature and mapping protocol comparable across all European countries, thereby making it a valuable information source at the European level. However, low spatial resolution and accuracy, infrequent updates and expensive manual production has limited its use at the national level. As of now, there is no remote sensing based high resolution land cover and land use services designed specifically for Latvia which would account for the country’s natural and land use specifics and end-user interests. The European Space Agency launched the Sentinel-2 satellite in 2015 aiming to provide continuity of free high resolution multispectral satellite data thereby presenting an opportunity to develop and adapted land cover and land use algorithm which accounts for national enduser needs. In this study, land cover mapping scheme according to national end-user needs was developed and tested in two pilot territories (Cesis and Burtnieki). Hyperspectral airborne data covering spectral range 400-2500 nm was acquired in summer 2015 using Airborne Surveillance and Environmental Monitoring System (ARSENAL). The gathered data was tested for land cover classification of seven general classes (urban/artificial, bare, forest, shrubland, agricultural/grassland, wetlands, water) and sub-classes specific for Latvia as well as simulation of Sentinel-2 satellite data. Hyperspectral data sets consist of 122 spectral bands in visible to near infrared spectral range (356-950 nm) and 100 bands in short wave infrared (950-2500 nm). Classification of land cover was tested separately for each sensor data and fused cross-sensor data. The best overall classification accuracy 84.2% and satisfactory classification accuracy (more than 80%) for 9 of 13 classes was obtained using Support Vector Machine (SVM) classifier with 109 band hyperspectral data. Grassland and agriculture land demonstrated lowest classification accuracy in pixel based approach, but result significantly improved by looking at agriculture polygons registered in Rural Support Service data as objects. The test of simulated Sentinel-2 bands for land cover mapping using SVM classifier showed 82.8% overall accuracy and satisfactory separation of 7 classes. SVM provided highest overall accuracy 84.2% in comparison to 75.9% for k-Nearest Neighbor and 79.2% Linear Discriminant Analysis classifiers.

In Sumatra, Indonesia, the establishment of oil palm and rubber plantations is widespread. However, it occurs at the expense of forest area. Since global demand for palm oil and rubber is increasing, forest conversion is expected to continue. Furthermore, studies have shown that forest destruction and the establishment of agricultural land uses influence the soil–atmosphere exchange of the climate-relevant trace gases carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and nitric oxide (NO). Nevertheless, trace gas measurements from oil palm and rubber plantations are scarce. Additionally, researchers have so far not considered oil palm canopy soils as a possible source or sink of trace gases. The present thesis consists of three studies, which assess the impact of forest conversion into smallholder oil palm and rubber plantations on soil CO2 and CH4 fluxes, as well as on soil N2O and NO fluxes, and which investigate the importance of oil palm canopy soil for N2O and CH4 fluxes. We conducted the studies on highly weathered tropical soils in Jambi Province, Sumatra, Indonesia and selected two soil landscapes which mainly differ in texture (clay and loam Acrisol). To examine the impact of land-use change on soil trace gas fluxes we investigated four different land uses per landscape: lowland forest and jungle rubber (rubber trees interspersed in secondary forest), as reference land uses, as well as smallholder rubber (7–17 years old) and oil palm plantations (9–16 years old), as converted land uses. Each land use was replicated four times in both landscapes.
\n\tThe first study investigated changes in soil CO2 and CH4 fluxes with forest conversion to smallholder oil palm and rubber plantations. We determined soil CO2 and CH4 fluxes monthly from December 2012 to December 2013, using static vented chambers. Our findings show that soil CO2 fluxes in oil palm plantations were reduced and that fluxes from the other three land uses were comparable among each other in both landscapes. We attributed this decrease to strongly decomposed soil organic matter, reduced soil carbon (C) stocks as well as to phosphorus fertilization and liming, which led to a lower C allocation to roots. Due to reduced nitrogen (N) availability in the converted land uses CH4 uptake was lower in oil palm and rubber when compared to the reference land uses in both landscapes. Thus, soil fertility appeared to be an important controller of soil CO2 and CH4 fluxes in this tropical landscape.
\n\tThe second study focused on the impact of forest conversion into smallholder oil palm and rubber plantations on soil N2O and NO fluxes. Additionally, we compared soil N2O fluxes from smallholder oil palm plantations with fluxes from a large-scale oil palm plantation. We determined soil N2O fluxes monthly from December 2012 to December 2013 in the two landscapes and weekly to bi-weekly from July 2014 to July 2015 in the large-scale oil palm plantation, using static vented chambers. Using open dynamic chambers, we measured soil NO fluxes four times in all land uses of both landscapes between March and September 2013. Our results show that land use change did not affect soil N2O and NO fluxes because of low initial N availability in the reference land uses, so that N2O and NO fluxes were also low, and any changes due to conversion might have been too small to identify. However, the large-scale oil palm plantation, although not significantly different, showed, because of their higher fertilizer input, on average 3.5 times higher soil N2O fluxes than the smallholder oil palm plantations.
\n\tThe aim of the third study was to quantify N2O and CH4 fluxes from oil palm canopy soils. We measured soil N2O and CH4 from three different stem heights in eight smallholder oil palm plantations across the two landscapes from February 2013 to May 2014, on a bi-weekly to monthly basis, using in-situ incubation. Oil palm canopy soil emitted N2O and CH4 from all stem heights. However, fluxes were low compared to ground soil fluxes. This was due to a low amount of canopy soil on a hectare basis and due to high nitrate contents, which might have suppressed CH4 production.
\n\tIn the synthesis of this dissertation, data on soil trace gas fluxes were embedded into a broader context to gain information on changes of the net biome exchange (NBE) and on partial N budgets with land-use change. Soil CO2 and CH4 fluxes were combined with an ancillary study on net primary production and harvest as well as with estimations on the contribution of heterotrophic soil respiration to total soil respiration. Soil N2O and NO fluxes were combined with ancillary studies on N inputs and outputs via fertilization, bulk precipitation, leaching and harvest. The results revealed that the NBE of oil palm plantations was higher compared to forest. Nevertheless, implications for climate change are negative since forest conversion itself results in a huge C loss, which cannot be compensated over time by oil palm plantations. The lowest partial N budget was detected in oil palm, indicating that N inputs via precipitation and fertilization were smaller than the huge N loss via harvest. Overall, these results illustrate that land-use change has negative effects on the C and N budgets of tropical ecosystems.

Dynamics of ecosystem services (ESs) in response to land use land cover (LU/LC) changes in the lower Gangetic plain of India

Neste trabalho, objetivou-se identificar, quantificar e analisar as mudanças ocorridas no campus da Universidade Federal de Lavras, município de Lavras, MG. Imagens do ano de 2009 do satélite QUICKBIRD e fotografias aéreas verticais dos anos de 1985, 1979, 1971 e 1964 foram usadas para produzir uma série de cartas de uso e cobertura da terra. O trabalho teve início com a ortorretificação da imagem de satélite QUICKBIRD e das fotografias aéreas. A identificação e a definição das classes de uso e cobertura da terra foram obtidas a partir de levantamentos de campo em 2009. A partir dessas informações, foram então construídos os mapas de uso e cobertura da terra. Por fim, foram feitas a quantificação e a análise das mudanças ocorridas no período estudado. Os resultados mostraram que: em 1964 a "área urbanizada" do campus que somava 6,24 ha, aumentou para 65,79 ha em 2009 e que o aumento mais expressivo dessa classe ocorreu entre os anos de 1964 (6,24 ha) e 1971 (24,4 ha). O menor valor de área de "vegetação natural" encontrado no campus foi de 38,38 ha em 1971, sendo que, a partir de 1979, essa situação foi alterada e em 2009 o campus apresentou 113,18 ha dessa categoria. Em relação à classe "água", observou-se que antes de 1971 não existia nenhuma represa no campus. A maior parte do campus, antes utilizada para "atividades agrícolas", teve uma redução significativa dessa categoria, de 384,19 ha em1964, para 271,16 ha em 2009.

The study aimed to assess the changes that have occurred in land use and land cover within the Maasai Mara landscape using remote sensed data from 1997 to 2017; examine the elephant distribution in relation to land use and land cover changes within the Mara landscape and to determine changes in elephant home ranges in relation to Land use and cover changes in the Mara landscape. In examining the land use and land cover changes on the elephant ranges and distribution, an integrated methodological approach was employed in which the changes that have taken place within the study area over a period of 20 years was determined by analysis involving a 10-year changes in land use and land cover using three epochs from 1997, 2007 and 2017 to generate six land use classes. The Maasai Mara Landscape (MML) supports one of the richest wildlife populations remaining on earth but over the last century, has experienced transformation notably through conversion of former rangelands into croplands. Elephants have both temporal and spatial requirements, which if not provided, render them vulnerable to the land-use practices. The study assessed land use and vegetation cover changes that have occurred and their effects on the elephant movements and distribution within the MML using an integrated methodological approach. The analysis revealed changes in land use and land cover classes over a period of 20 years for the three epochs, from 1997, 2007 and 2017. Elephant’s distribution has been restricted to areas of high vegetation densities within specific habitats hence accelerating the rate of habitat destruction and degradation due to their high densities. These changes have drastically reduced forage for elephants necessitating them to travel longer distances out of their home range in search for food. Human beings have caused land use and cover changes which have detrimental impacts on the ecosystem and ecosystem services. The Maasai Mara landscape supports one of the richest wildlife populations remaining on earth but over the last century, it has experienced land transformation notably through conversion of former rangelands used mainly for tourism and production of grains such as wheat. Land outside the national parks and the reserve is important to the future of elephant existence in Kenya. Little is known about how human occupation on these landscapes negatively affects elephants (Loxodonta africana) habitats, movement and ranges. This has been confirmed by the current continuous demarcation/fencing of land in most areas in Narok County. Elephants like other landscape species, have both temporal and spatial requirements, which if not provided, will render them vulnerable to the land use practices of people. The study aimed to assess the changes that have occurred in land use and land cover within the Maasai Mara landscape using remote sensed data from 1997 to 2017; examine the elephant distribution in relation to land use and land cover changes within the Mara landscape and to determine changes in elephant home ranges in relation to Land use and cover changes in the Mara landscape. The paper describes the different changes that have taken place within the MML and how these changes have affected elephant populations, their trend and distribution within the MML. In examining the land use and land cover changes on the elephant ranges and distribution, an integrated methodological approach was employed in which the changes that have taken place within the study area over a period of 20 years was determined by analysis involving a 10-year changes in land use and land cover using three epochs from 1997, 2007 and 2017 to generate six land use classes. The study found out that there were significant changes of various classes across the years. Forest, water and open shrubs coverages decreased from 1997 to 2017. Classification noted a serious problem within the study area of continuous increase of bare ground coverage across the study years. Elephant populations have been increasing within the area .at an annual rate of 2.69%. The animals are distributed all over the landscape. Distribution of elephants has been restricted to high densities within a specific habitat hence accelerating rate of habitat destruction and degradation due to their high densities within a specific habitat. These changes have reduced drastically foliage for elephants thus necessitating them to travel longer distances in search and as a result increases elephant home ranges.

GIS and Remote Sensing Based Detecting the Expansion of Tree Plantation and Its Impacts on Fagita Lekoma District, Amhara Regional State, Ethiopia

Land Use and Land Cover (LULC) studies form the backbone of understanding environmental dynamics, urbanization, agriculture, forestry, and ecosystem management. The distinction between Land Use (LU) and Land Cover (LC) is Land Cover refers to the physical characteristics of the Earth's surface, such as vegetation, water bodies, bare soil, or built-up areas. Land Use describes how humans utilize the land, such as for agriculture, urban development, forestry, or recreation. The terms land use and land covers are often used interchangeably, but both concepts are used in different senses. Land cover means vegetation, urban infrastructure, water, open space, soil cover etc. and the land is primarily used for wildlife habitat or land use purposes such as agriculture, industry etc. Land use and land cover are changing day by day due to a growing population and growing needs. To study LULC based on remote sensing (RS) and geographic information system (GIS) techniques, land use and land cover techniques can help us to monitor Spatial-temporal changes in land cover. The main objective of present research paper is to study the land use changes after the construction of Samruddhi expressway in Kopargaon tehsil of Ahmednager district. For this study we use the Time series of annual global maps of land use and land cover (LULC) of ESRI. The downloaded satellite image covered Variable mapped of land use/land cover with Universal Transverse Mercator (UTM) Data Projection and there extension is whole globe .The source imagery: Sentinel-2 with Cell Size is 10m resolution. The satellite images are Thematic in data type .The above mention satellite images downloaded from Esri, Microsoft, and Impact Observatory which were published on March 2022. Land use and Land cover data use for the year 2017 and 2021 to find Land Use and Land Cover Change of Kopargaon Tehsil