Interpretation Of Satellite Images Research Articles

INFLUENCE OF THE HYDROLOGICAL REGIME ON THE STATE OF FLOODPLAIN GEOSYSTEMS OF THE CHARYN RIVER

The paper deals with the influence of the hydrological regime of the Charyn River, where the Bestyubinsk reservoir was built in 2012, on the state of floodplain geosystems. The research aims to identify and analyze the impact of regulation of the Charyn River flow reservoir on the state of geosystems. Research tasks include (1) calculating and analyzing the hydrological regime of the Charyn River; (2) identifying the sections of the river floodplain most susceptible to the threat of flooding before and after the construction of the reservoir; (3) compiling comprehensive physical and geographical characteristics of the floodplain geosystems of the Taskarasu Valley subject to frequent flooding. In the analysis of the hydrological regime of the river, we used difference and total integral curves to identify the cycles of fluctuations in the annual runoff. The river runoff was estimated by the change in the parameter of the mean annual runoff for 2004–2018. As a result of the interpretation of satellite images, using the Automated Water Extraction Index integrated with ArcGIS, the most frequently flooded area of the river floodplain for this period was identified. The time and area of flooding were determined. Based on the data obtained, the flooded areas were compared for 2004–2018. The advantages of visual observation using satellite images are the ability to clearly and intelligibly visualize the flooded area and systematically, as quickly as possible, reach the required site coordinates using decoded digital data, which is necessary when studying the functioning of geosystems. To describe the state of the floodplain geosystems most susceptible to flooding, we used the data for many years of field landscape studies. Thus, comprehensive physical and geographical characteristics of the Taskarasu Valley were given.

Landslide hazard assessment of the Norak region of Tadjikistan

Introduction. The application of geographic information system (GIS) technologies is a promising technology for mapping and evaluating landslide hazards. At present, satellite-based terrestrial sensing is widely used. Satellite images make it possible not only to identify individual landslides, particularly in hard-to-reach areas, but also to identify those locations where landslide processes have manifested, are manifesting and may manifest themselves in the future.Aim. Mapping of the present landslide propagation in the territory of the Norak region of Tajikistan using satellite imaging and GIS.Materials and methods. In the course of the study, maps of landslide propagation were compiled both automatically and manually. An analysis of the results of previous studies and interpretations of satellite images was conducted. Digital models of the relief, as well as topographic, geomorphological, engineering and geological maps, were investigated.Results. A detailed map of landslide phenomena was compiled in the GIS format and a database of landslide phenomena was created for the first time for the territory of the Norak region of the Republic of Tajikistan.Conclusions. The conducted zoning showed that, out of the total landslide number, 24%, 36%, 10%, 18% and 12% correspond to very small, small, medium, large, and very large landslides, respectively. The landslide processes are developed across the area of 2601×104 m2, which accounts for the 4% of the total area of the district. The results demonstrate that the approach used in this study may be applicable to the landslide hazard assessment in hard-to-reach and poorly studied regions.

HrreNet: semantic segmentation network for moderate and high-resolution satellite images

ABSTRACT Semantic segmentation is a commonly used intelligent interpretation method. However, semantic segmentation via deep learning usually faces several problems: resolution loss due to downsampling, difficulty in obtaining context information, and imprecise object boundaries. These problems lead to a poor performance when using satellite images instead of conventional images and limit the value of intelligent interpretation of satellite images in practical application. In this study, we construct a Qilian Ecological Resource Extraction Dataset (QERED) with multiple scene satellite images of Qilian after preprocessing via correction, fusion, mosaic, and annotation. We then propose a segmentation network, namely, High-resolution Resource Extracting Network (HrreNet), by using high-resolution feature representation, multi-scale context fusion, boundary refinement with relearning, and structural similarity loss. Experiments on QERED show that HrreNet greatly improves performance on small-size objects and slightly improves performance on larger-size objects. HrreNet achieves the best result of 73.36% mIoU on a moderate-resolution satellite dataset CCFD and 80.87% mIoU on a high-resolution satellite dataset Vaihingen.

Aplikasi Remote Sensing dan Geographic Information System Untuk Monitoring Perubahan Penggunaan Lahan Dalam Menunjang LP2B Di Subak Intaran Barat, Kota Denpasar

Remote Sensing and Geographic Information System Aplication For Monitoring Land Use Change In Supporting LP2B In Subak Intaran Barat, Denpasar City. Subak Intaran Barat is one of 41 subak in Denpasar City which has been designated as Sustainable Subak and agro-ecotourism and is recommended as a Sustainable Food Agricultural Land area. This study aims to create a thematic map of changes in paddy land use in 2012 and 2018, a map of paddy land ownership, and compile an information system of Land Resources (SDL), Artificial Resources (SDB), and Agricultural Resources (SDP). This research method, namely: analysis of satellite images, surveys and mapping. The stages of this research are: literature study, interpretation of satellite images, field surveys and interviews, reinterpretation of satellite images, mapping and compilation of attribute data for SDL, SDB, SDP, and land ownership. Changes in land use for 6 years (2012-2018) in the West Intaran subak covering an area of 8.9 acres. The number of Subak farmers is 76 people. The status of the owner is 6 people with a land area of 6.85 acres (8.00%) and 70 smallholders with a land area of 79.55 acres (92%) who come from Sanur Kauh Village. Land resources found in Subak Intaran Barat are: irrigation canals (5,120 m), jogging track (1,960 m), rainfall 1500-2000 mm/year, slope 0-2%, soil type Typic Tropaquepts Isohiptermic, rice crop rotation patterns, horticulture-rice.

MAPPING NATURAL NON-FOREST VEGETATION REMOVAL IN THE BRAZILIAN AMAZON – A PILOT PROJECT

Abstract. The increasing pressure from agriculture and pasture activities over non-forest vegetation areas revealed the need of INPE to extend forest regular monitoring to non-forested areas. Non-forest vegetation consists of savannahs, shrub lands, grasslands or seasonal floodplains that occupy 279 thousand km2 (6,63 %) of Brazilian Amazon biome extension. Addressed ecosystem services of non-forest vegetation vary from climate, soil, carbon storage, biodiversity, water and fire regulation to cultural benefits and living of the population. The challenge in monitoring non-forest removal lies in climatic seasonality, high variability of phytophysiognomies and cloud coverage. In this work we developed a method to map non-forest vegetation from 2000 to 2021 in a pilot area enclosing five municipalities in Roraima and Amapá states. Considering the total area of 47 thousand km2, 58 % was originally forest and 37 % was non-forest vegetation. In 2020, soybean planted areas occupied 60 thousands hectares in these municipalities. We adapted PRODES protocol to non-forest features using visual interpretation of Landsat and SENTINEL-2 satellite images. Regional expertise supported the interpretation keys to distinguish non-forest removal from seasonal changes or other land use changes. A baseline map for 2000 is provided together with biannual and annual increments for 2002–2018 and 2019–2021, respectively. Results show removal of non-forested areas even overpassing deforestation in some municipalities. Accumulated non-forest removal was 3.133,06 km2 or 17,44 % of the non-forest area enclosed. This work consolidated the method to be applied in the Brazilian Amazon biome allowing a uniform historical mapping series of deforestation and non-forest removal.

Environmental geophysical study for ideal locations of landfill within Iraqi Southern Desert

Solid wastes and toxic chemicals are the main contributors, which influence the environment and quality of life. Demographic population, economic and building structures considered as major factors that are controlling such pollution. Therefore, it is very important to find proper areas to dispose of these pollutants. The current study aims to find best locations for wastes disposal areas that comply with the national and international standards. The study area, the Iraqi Southern Desert had been subjected to comprehensive study for geology, gravity and magnetic investigations. The existence of impermeable beds, tectonic, seismology and structural features show that the area understudy is located in the stable shelf that has no folding and faulting complexity; however, the saline groundwater lies at about 150 m depth. The gravity field data were analyzed using three methods: Trend Surface Analysis, Average Smooth Matrix and the First Vertical Derivative. Negative and positive gravity anomalies were noticed and interpreted. Remote sensing is also carried out in order to locate linear features, drainage patterns and watersheds within the Southern Desert depending on the interpretation of satellite images of the study area. Finally, a comparison of natural and environmental factors with remote sensing and both gravity and magnetic analyses gives thirteen locations of different coordinates. Only sites 1, 2, 3,10 and 13 yield good environment satisfaction agreement that permits us to choose them as a good locations for future landfill waste purposes.

Geospatial Artificial Intelligence for Early Detection of Forest and Land Fires

Over the years, early detection of forest and land fires has been conducted using hotspot data provided by the National Institute of Aeronautics and Space (LAPAN), based on its interpretation of satellite images. The hotspot data have tremendously helped firefighting efforts and further enforcement. However, the system has several shortcomings, especially due to its inability to distinguish forest and land fires from other hot surfaces or fires caused by common human activities. Furthermore, this method also requires labor-intensive verification, and heavily relies on human factors for advanced analysis and validation. Recently, the DG of Law Enforcement of the Ministry of Environment and Forestry (DGLE MoEF) has been piloting a new approach through advancement in artificial intelligence, called Geospatial Artificial Intelligence (GeoAI). By utilizing recorded satellite image data from 2017 - 2019, the machine has been trained to recognize the pattern and tone of the image in burnt areas so that it can validate the presence of the burnt area based on the history of Sentinel-2 imagery for the past week at each cluster. DGLE MoEF found that the burnt area data processed by GeoAI has better accuracy than the hotspot count for forest and land fire identification. Moreover, GeoAI may ease forest and land fire analysis and verification by automatically overlaying forest area and company concessions at the burnt area. GeoAI’s innovation in forest and land fire monitoring can produce more accurate and complete early detection data of forest and land fires than currently available hotspot data. The results of hotspot clustering that detect fires may assist firefighters in rapidly extinguishing the fire, and support law enforcement officers in determining the appropriate target location. Therefore, GeoAI technology may increase the effectiveness and efficiency of resources allocated by law enforcement officers in providing better and more responsive public services.
 Keywords: GeoAI, geospatial technology, artificial intelligence, forest fire, hotspot

The folds and faults kinematic association in Zagros

The Zagros orogenic belt, one of the most prominent and famous collisional belts in the central part of the Alpine-Himalayan orogenic chain, is located between the southern margin of the Central Iranian microcontinent and the northern margin of the Arabian plate. The structural architecture and folds and faults relationships of a significant segment of the south-central part of the Zagros’ hinterland are investigated in this study through stereoscopy of aerial photographs, interpretations of satellite images, consideration of the major ground topographic variations, and field research. This research found that there must have been at least two major deformation events: (1) a ductile phase, which is older than the Eocene, and (2) a semi-brittle deformation stage, which is younger than the early Miocene and is represented by thrusting, folding, and strike-slip faulting. The presence of numerous fault-related folds and fold-accommodation faults in this area demonstrates the close kinematic relationship between folding and faulting. Based on the topographic changes, a major hidden tear fault and a basement hidden back thrust, which play important roles in the architecture of the area, have been suggested.

A VOXEL-BASED METHOD FOR THE THREE-DIMENSIONAL MODELLING OF HEATHLAND FROM LIDAR POINT CLOUDS: FIRST RESULTS

Abstract. Bushfires are an intrinsic part of the New South Wales’ (NSW) environment in Australia, especially in the Blue Mountains region (11400km2), that is dominated by fire prone vegetation that includes heathland. Many of the Australian native plants in this region are fire-prone and combustible, and many species even require fire to regenerate. The classification of the lateral and vertical distribution of living vegetation is necessary to manage the complexity of bushfires. Currently, interpretation of aerial and satellite images is the prevalent method for the classification of vegetation in NSW. The result does not represent important vegetation structural attributes, such as vegetation height, subcanopy height, and destiny. This paper presents an automated method for the three-dimensional modelling of heathland and important heathland parameters, such as heath shrub height and continuity, and sparse tree and mallee height and density in support of bushfire behaviour modelling. For this study airborne lidar point clouds with a density of 120 points per square meter are used. For the processing and modelling the study is divided into a point cloud processing phase and a voxel-based modelling phase. The point cloud processing phase consists of the normalisation of the height and extraction of the above ground vegetation, while the voxel phase consists of seeded region growing for segmentation, and K-means clustering for the classification of the vegetation into three different canopy layers: a) heath shrubs, b) sparse trees and mallee, c) tall trees.

A Method for Digital Terrain Reconstruction Using Longitudinal Control Lines and Sparse Measured Cross Sections

Using longitudinal control lines and sparse measured cross sections with large spaces, a new method for quickly reconstructing digital terrains in natural riverways is presented. The longitudinal control lines in a natural riverway, mainly including the river boundaries, the thalweg, the dividing lines of floodplains and main channel, and the water edges, can be obtained by interpreting satellite images, remote sensing images or site surveys. Then, the longitudinal control lines are introduced into quadrilateral grid generation as auxiliary lines that can control longitudinal riverway trends and reflect transverse terrain changes. Then, by the equal cross-sectional area principle at the same water level, all measured cross sections are reasonably fitted. On the above basis, by virtue of the fitted cross-sectional data and the weighted distance method, the terrain interpolations along the longitudinal grid lines are conducted to obtain the elevation data of all grid nodes. Finally, according to the readable text formats of MIKE21 and SMS, the gridded digital terrain and connection information are output by computer programming to achieve good construction of the data exchange channels and fully exploit the special advantages of various software programs for digital terrain visualization and further utilization.

Farmland change and its implications in the Three River Region of Tibet during recent 20 years.

Farmland is a key resource for safeguarding the regional food security and social stability, particularly in Tibet where the farmland is very limited due to its high altitude. With quick economic development during recent decades, farmland changes are great in China, and thus have been extensively studied. These studies generally focused on eastern regions, and seldom for Tibet due to the lack of good quality and available data. To this end, taking the Three River Region (TRR) as the case area, this study obtained 1 m spatial resolution farmland data for 2000 and 2018 by visual interpretation of the Google Earth high resolution satellite images, and then analyzed the farmland change, its driving factors and impact on grain production between 2000 and 2018. The results showed that farmland in the TRR decreased by 8.85% from 219.29 k ha in 2000 to 199.89 k ha in 2018, averagely reduced by 0.51% per year, mainly driven by the economic development, agricultural progress, urbanization, and population growth. The farmland losses largely occurred in urban areas and their surrounding counties due to urban land occupation, and caused the grain production reduced by 9.38%. To control the quick farmland losses and to ensure the regional food security of Tibet, it should strengthen the supervision on non-agricultural occupation of farmland and increase agricultural investment to improve the land productivity in the TRR.

A Methodology for Automatic Identification of Units with Ecological Significance in Dehesa Ecosystems

The dehesa is an anthropic complex ecosystem typical of some areas of Spain and Portugal, with a key role in soil and biodiversity conservation and in the search for a balance between production, conservation and ecosystem services. For this reason, it is essential to have tools that allow its characterization, as well as to monitor and support decision-making to improve its sustainability. A multipurpose and scalable tool has been developed and validated, which combines several low-cost technologies, computer vision methods and RGB aerial orthophotographs using open data sources and which allows for automated agroforestry inventories, identifying and quantifying units with important ecological significance such as: trees, groups of trees, ecosystem corridors, regenerated areas and sheets of water. The development has been carried out from images of the national aerial photogrammetry plan of Spain belonging to 32 dehesa farms, representative of the existing variability in terms of density of trees, shrub species and the presence of other ecological elements. First, the process of obtaining and identifying areas of interest was automated using WMS services and shapefile metadata. Then, image analysis techniques were used to detect the different ecological units. Finally, a classification was developed according to the OBIA approach, which stores the results in standardized files for Geographic Information Systems. The results show that a stable solution has been achieved for the automatic and accurate identification of ecological units in dehesa territories. The scalability and generalization to all the dehesa territories, as well as the possibility of segmenting the area occupied by trees and other ecological units opens up a great opportunity to improve the construction of models for interpreting satellite images.

TESTING THE METHOD FOR DETERMINING THE POTENTIAL OF SEISMOGENERATING ZONES BY GEOMAGNETIC DATA IN THE TERRITORY OF EASTERN KAZAKHSTAN

In certain territories of the East Kazakhstan region (Ust-Kamenogorsk, Semipalatinsk, the Ulba object, Zaisan), a method for calculating the seismic potential (maximum possible earthquake magnitude) was tested using geomagnetic data: the values of the geomagnetic Z-component calculated in the geocentric solar-magnetospheric coordinate system (GSM). Estimates of seismic potential based on geomagnetic data were compared with data on the potential of seismogenic zones in the territory of the East Kazakhstan region, obtained from seismological, geological and geophysical data and the results of interpretation of satellite images. Satisfactory agreement was obtained between the estimates by two different methods, which confirms the possibility of using an alternative method for estimating the seismic potential in regions with a deficit of information about the seismic regime (possibly, being at present in the regime of temporary seismic calm).

Productivity, Potentiality and Soil-site Suitability Evaluation for Rice Crop in the Moridhal Watershed of Dhemaji District of Assam

A study was undertaken to carry out the productivity and suitability evaluation of the soils of the Moridhal watershed in Dhemaji district of Assam. Satellite image interpretation led to the recognition of four different physiographic units in the studied area which included: upper piedmont plain, lower piedmont plain, alluvial plain, and flood plain. One hundred seventy surface soil samples representing different physiographic units were collected and analyzed in the laboratory for various soil parameters. The productivity and suitability of the soils were computed and assessed by following standard procedures. The productivity index of studied soils varied from 12.13 to 62.14 could be ranked as: alluvial plain>flood plain>lower piedmont plain>upper piedmont plain. However, through adoption of proper improvement measures the soils could be improved to land index values of 41.04 to 90.25.The land index value for Sali rice, Ahu rice and Bororice could categorize into permanently unsuitable (N2) to moderately suitable (S2). The studied soils in terms of their suitability for Sali rice, Ahu rice and Boro rice could be arranged in the sequence: flood plain> alluvial plain> lower piedmont plain> upper piedmontplain. The productivity and soil suitability maps as generated under GIS environment will serve as ready reckoner to farmers, planners and administrators in identifying the potential are assuitable for the major crops grown in that area.

A State-of-the-Art Vegetation Map for Jordan: A New Tool for Conservation in a Biodiverse Country

In many countries, including Jordan, the updating of vegetation maps is required to aid in formulating development and management plans for agriculture, forest, and rangeland sectors. Remote sensing data contributes widely to vegetation mapping at different scales by providing multispectral information that can separate and identify different vegetation groups at reasonable accuracy and low cost. Here, we implemented state-of-the-art approaches to develop a vegetation map for Jordan, as an example of how such maps can be produced in regions of high vegetation complexity. Specifically, we used a reciprocal illumination technique that combines extensive ground data (640 vegetation inventory plots) and Sentinel-2 satellite images to produce a categorical vegetation map (scale 1:50,000). Supervised classification was used to translate the spectral characteristics into vegetation types, which were first delimited by the clustering analyses of species composition data from the plots. From the satellite image interpretation, two maps were created: an unsupervised land cover/land use map and a supervised map of present-day vegetation types, both consisting of 18 categories. These new maps should inform ecosystem management and conservation planning decisions in Jordan over the coming years.

Geomorphology of the Kalamas river delta (Epirus, Greece)

ABSTRACT This article presents the geomorphological mapping of the Kalamas river delta in Thesprotia (Epirus, north-western Greece). The Kalamas (also known as Thyamis) is one of the three main deltas of this region. Detailed mapping was performed through analysis of field geomorphological surveys and interpretation of old maps, satellite images, aerial photos, and DEM. The evolution of the delta as well as its current morphology derives from complex interactions between alluvial, marine dynamics and human activities. Several palaeo-channels have been identified, and the recent morphology of the delta has been altered by the construction of a dam and the canalization of the river during the second half of the twentieth century. The coastline is complex, and mainly consists of lagoons, sandy barriers and sand spits. Since part of the delta has been prograding for about fifty years, the current dynamics indicate erosion as well as progressive submersion of these low coasts.

EFFECTS OF WATERSHED LAND OCCUPATION AND LAND USE ON THE TROPHIC STATE OF TWO SHALLOW LAKES

The increase in external phosphorus inputs has as main consequence the cultural eutrophication, currently regarded as one of the primary threats to water bodies. Anthropogenic activities involving the watershed use and occupation of land can accelerate this process. Thus, the aim of this study was to assess the effects of land use and land cover on the trophic status of two shallow lakes (lakes Paó and Santa Lúcia, Paraíba state, Brazil). Samples of water and sediments were collected for 6 months at three sampling stations in the lakes. The land-use and land-cover mapping was performed through visual interpretation of satellite images with high spatial resolution. Lake Paó remained eutrophic, with a low euphotic zone (ZEU≤1), while Lake Santa Lucia, oligotrophic and with clear waters (ZEU≥1), throughout the study period. The advanced eutrophication of Lake Paó was related to high sewage inputs from nonpoint sources, since the urban/ built-up area corresponded to 37.63% of its riparian zone (the lakeshore), whereas in Lake Santa Lucia to only 2.43%. Our results evidenced the importance of maintaining phosphorus at low concentrations and a minimum of riparian vegetation for reducing the vulnerability to eutrophication and thus preserving water quality.

Classification of Coastal Belts on the Coral Coast of Viti Levu, Republic of Fiji, South Pacific, using the BCCS (Biophysical Cross-shore Classification System)

Finkl, C.W. and Makowski, C., 2022. Detailed interpretation of satellite images of coastal belts on the Coral Coast of Viti Levu, Republic of Fiji, South Pacific, using the BCCS (Biophysical Cross-shore Classification System). Journal of Coastal Research, 38(3), 491–511. Coconut Creek (Florida), ISSN 0749-0208. Lying on the southwest Fijian coast of Viti Levu, the Coral Coast is noted for its fringing coral reefs that extend for an overall alongshore length of about 80 km (from Momi to Suva), with the study area being a 32-km-long subset in the Sigatoka region. The fringing reefs, extending offshore for an average maximum distance of about 650 m, are broken into contiguous segments by deep-water cross-shore channels composing discrete coastal belts. When the fringing reef segments are combined with other onshore (terrestrial) eco-geomorphological units, they make up nine distinct coastal belts in the study area comprised by 35 domains. The cross-shore classification of the Coral Coast examples was based on application of the BCCS (Biophysical Cross-shore Classification System), which required interpretation of satellite images to comprehend cross-shore catenary (linked) sequences that had alongshore extent. Imagery from 2010 to 2017 was accessed to obtain the best possible views of each reef segment and adjacent mountain hinterland. Due to spatial limitations, capabilities of the Google Earth Pro platform were accessed to acquire sufficient interpretive details for compilation of cross-shore transectal archetypes and subarchetypes from the reef front to coastal mountain slopes. Typical cross-shore classificatory sequences consisted of Coral Reef (Cr), Flat (F), Beach (Be), and Mountain (M) archetypes that formed tetrasequent Cr-F-Be-M catenas. Although a limited number of small Delta (De) archetypes overrode back-reef flat margins, their frequency of occurrence was not a domainal characteristic. The occurrence of Developed (Dv) archetypes were more or less ubiquitous onshore, whereas alongshore bedrock outcrops of the Rock (R) archetype were rare. Because the scale of natural and anthropogenic features along the coast demanded a detailed (hectometric) coastal classification, the BCCS was applied on an experimental basis to ascertain its applicability as an exemplar for similar studies in insular settings elsewhere.

IDENTIFIKASI PERUBAHAN TUTUPAN LAHAN DI KEBUN RAYA MEGAWATI SOEKARNOPUTRI MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS

This study aims to identify land cover and analyze land cover changes in the Megawati Soekarnoputri Botanical Gardens area in 2010-2019. This research was conducted in December 2021-January 2022 at the Megawati Soekarnoputri Botanical Gardens, Southeast Minahasa and at the Forestry Science Study Program, Faculty of Agriculture, Sam Ratulangi University, Manado. This study uses satellite image/ Digital Globe Imagery with visual interpretation methods or on-screen digitization. The results showed that there were 6 land cover classes: forest, developed, open land, agriculture, shrub, and water bodies. The land cover area resulting from the interpretation of satellite image in 2010 is 189,51 Ha of forest, 0,34 Ha of developed, 6,78 Ha of open land, 9,71 Ha of agriculture, 8,96 Ha of shrub and water bodies of 6,11 Ha. The land cover area from the interpretation of satellite photos in 2019 is 185,79 Ha of forest, 0,57 Ha of developed, 6,02 Ha of open land, 12,40 Ha of agriculture, 8,71 Ha of shrub and water body 4,62 Ha. The results of the analysis of land cover changes showed that forested land cover from 2010 to 2019 in Megawati Soekarnoputri Botanical Gardens decreased by (0.22%), namely 0,22 Ha, so that forested land cover in 2019 had an area of 189,10 Ha and non-forest land cover 32,31 Ha.

IDENTIFIKASI PERUBAHAN TUTUPAN LAHAN DI KEBUN RAYA MEGAWATI SOEKARNOPUTRI MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS

This study aims to identify land cover and analyze land cover changes in the Megawati Soekarnoputri Botanical Gardens area in 2010-2019. This research was conducted in December 2021-January 2022 at the Megawati Soekarnoputri Botanical Gardens, Southeast Minahasa and at the Forestry Science Study Program, Faculty of Agriculture, Sam Ratulangi University, Manado. This study uses satellite image/ Digital Globe Imagery with visual interpretation methods or on-screen digitization. The results showed that there were 6 land cover classes: forest, developed, open land, agriculture, shrub, and water bodies. The land cover area resulting from the interpretation of satellite image in 2010 is 189,51 Ha of forest, 0,34 Ha of developed, 6,78 Ha of open land, 9,71 Ha of agriculture, 8,96 Ha of shrub and water bodies of 6,11 Ha. The land cover area from the interpretation of satellite photos in 2019 is 185,79 Ha of forest, 0,57 Ha of developed, 6,02 Ha of open land, 12,40 Ha of agriculture, 8,71 Ha of shrub and water body 4,62 Ha. The results of the analysis of land cover changes showed that forested land cover from 2010 to 2019 in Megawati Soekarnoputri Botanical Gardens decreased by (0.22%), namely 0,22 Ha, so that forested land cover in 2019 had an area of 189,10 Ha and non-forest land cover 32,31 Ha.