Interpretation Of Satellite Imagery Research Articles

Analysis of net-zero emission index for several areas in Indonesia using individual carbon footprint and land use covered

The issue of global warming continues to be a concern for the international world. One of the causes of global warming is the greenhouse gas (GHG) effect caused by an increase in the amount of emissions in the atmosphere. Indonesia has set targets for reducing GHG emissions in the Nationally Determined Contribution (NDC) by 31.89% on its own capability and 43.20% with international assistance in 2030. Therefore, to support the program, it is necessary to conduct research to inventory the emissions produced and emissions absorbed by each region in Indonesia. This study calculated the net-zero emission index in an area by comparing the value of carbon emission with the value of carbon sequestration based on land cover in an area. Regional emission data was obtained from Aksara Bappenas, while regional sequestration data was obtained from RAD-GRK and satellite imagery interpretation based on land cover. Meanwhile, to calculate emissions at the city/regency level, individual carbon footprint data was used and converted into city/regency emission data. The calculation of the carbon index was carried out nationally in all provinces of Indonesia. Several provinces were further tested as examples to determine sequestration based on land cover from imagery interpretation. The results show that the Indonesian net-zero emission index is lower than 1, but some provinces with dense populations have a net-zero emission index >1, namely: DKI Jakarta, DI Yogyakarta, North Sumatra, and Riau.

Aplikasi Penginderaan Jauh dan SIG Pemetaan Pengunaan Lahan dan Sistem Informasi Lahan Pertanian (Studi Kasus di Kabupaten Nagekeo, Provinsi Nusa Tenggara Timur)

Remote sensing technology combined with Geographic Information Systems (GIS) can provide and produce data and information quickly, precisely, and accurately, so that the latest land use can be known. This study aims to determine the level of accuracy of satellite imagery interpretation, make existing land use maps, and provide agricultural land information systems in Nagekeo Regency. The research method used is an explorative method. The stages of the research consisted of identifying satellite images, field surveys, reinterpreting land use maps, and creating agricultural land information systems. The results showed that the level of accuracy of interpretation of satellite imagery for land use in Nagekeo Regency was 97%. The widest use of paddy fields is in Aesesa Subdistrict with 5080 ha, the widest use of dry land is in Nangaroro Subdistrict with 2062 ha, the widest use of mixed garden land is in Boawae Subdistrict with 7062 ha and the widest use of garden land is in Mauponggo Subdistrict 2800 ha. Information on paddy fields contains information on land area, land status, farmer's name, farmer's address, farmer's age, cropping pattern, and yield sharing system. The total land ownership of the owner's land was 2417.77 ha with a total of 2798 plot units, 1102.7 ha of sharecroppers' land with a total of 1240 plot units, and pawning farmer's land area of 233.99 ha with a total of 331 plot units.

Understanding the landscape evolution of El Mogote barrier spit, Baja California Sur, Mexico, using OSL dating, satellite images and a multivariate analysis

Barrier spits are natural archives that provide information about coastal evolution and prevailing environmental conditions of the mainland. In this study, we analyze one of the largest barrier spits in Baja California Sur, Mexico, known as El Mogote bar, using a multi-method approach to unravel the coastal landscape evolution. We used (1) OSL dating to provide a robust geochronology and depositional rates of the emerged beach-dune ridges, (2) satellite imagery interpretation using the Digital Shoreline Analysis System (DSAS) software to assess shoreline changes on Landsat images from 1986 to 2014, and (3) the Principal Component Analysis (PCA) with climatic and geomorphologic parameters to elucidate the environmental conditions of the beach-dune ridges for the period comprised in the DSAS analysis. OSL results indicate that the emerged bars started to form ∼2 ka and were developed in three main building phases: (1) the Medieval Warm Period (MWP) and part of the Little Ice Age (LIA) with accretional rates of 1.13 m yr −1, (2) the ending of the LIA with 7.03 m yr −1 and (3) the post-LIA with 3.98 m yr −1. In this paper, we hypothesize two probable causes that led to the destruction of the former beach-dune ridges: (1) tsunami waves triggered by seismic events and (2) local sea level rise associated with local tectonic subsidence. Satellite images and DSAS analysis show that, in the barrier spit, accretion and erosion have been in dynamic equilibrium for at least three decades. PCA results indicate that dry and cold weather conditions, El Niño modes, and tropical cyclone occurrence with high accretional rates on the barrier spit are the main controls on the beach-dune ridges. We conclude that the multi-method approach used in this research is suitable to tease out the landscape evolution of a barrier spit from a climatic and geomorphological perspective, as we were able to identify that the accretional episodes of El Mogote bar are mainly related to rainfall during the coldest and driest environmental conditions that enhance sediment supply from continental areas to the coast.

Identifying the relationship between changes in vegetation indices and the anthropogenic activities in the tropical Semarang City of Indonesia using space imagery

The existence of vegetation in the watershed area as a function of maintaining the continuity of the process of soil infiltration, erosion resistance, and landslide resistance is essential. Studies that measure the existence of vegetation through vegetation index parameters by utilizing optical-based satellite imagery maps, as well as radiometric waves or Radio Detection and Ranging (RADAR), have been widely carried out. However, attempts to test it to formulate it into vegetation resilience indices on the scale of functional areas such as watersheds are still a challenge in itself. In this study, the vegetation resilience index was measured by looking for spatial change patterns from Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI) in 2015-2021. This spatial pattern change is associated with anthropogenic factors represented by the dynamics of changes in vegetation to housing and population distribution. The use of residential land is obtained from the interpretation of optical-based satellite imagery maps from Google Earth sources mitigated by the administrative boundaries of villages. The population change was obtained from statistical data on the village’s population in the same year. The change in vegetation index, residential, and inhabitants were examined by the Spatial Autocorrelation (SA) using the Global Moran’s Index (GMI). Finally, the changes in the spatial patterns in vegetation indices, residentials index, and inhabitant index were conducted with correlation analysis to determine the relationship between these parameters. The results of GMI indicate a tendency towards dispersion in NDVI and SAVI, which showed a linear index decline in the dry season but weak linear indices incline in the wet season. The trend of dispersion patterns also occurs in residential and inhabitants, showing a linear index decline with a high confidence level (R2=0.9171 in residentials and R2=0.5872 in inhabitants). The relationship between pattern changes in NDVI and SAVI and Residential and Inhabitant found by the following formula; NDVI = 0.214173 -3.12952 * Residential + 6.457788 * Inhabitant; SAVI = 0.101544 + 0.120294 * Residential + 1.963223 * Inhabitant.

Active faulting of the Nanhe Fault and relation to the Anninghe Fault zone in the late Quaternary, eastern Tibetan Plateau

Faults along the boundaries of active tectonic blocks are the main structures that are responsible for major earthquakes in mainland China. Investigating the geometric distribution, rupture behavior, and paleoseismic history of these faults is the prerequisite for constraining geodynamic models and regional seismic hazard analyses. The Nanhe Fault, located at the eastern boundary of the Sichuan–Yunnan Block near Mianning County, has been paid less attention so far due to insufficient historical records of major earthquakes. In this paper, we focused on the Nanhe Fault and conducted satellite imagery interpretation, field investigations, and trench excavations. Our findings indicate that the Nanhe Fault initiates north of Mianning County; the north segment of the fault is connected with the Anninghe Fault; and it extends for about 70 km south-westward and terminates southwest of Ermaga Village. The fault has been faulting in the late Late Pleistocene with a left-lateral strike-slip rate of 2.40–2.56 mm/yr, while in the late Holocene, the left-lateral strike-slip and vertical slip rates are 2.50–2.60 mm/yr and about 0.60 mm/yr, respectively. Three paleoseismic events (5373–4525 BC, AD 1193–1576, and AD 1496–1843) were identified by excavating trenches at the Nanhe Fault. A comparative analysis of paleoseismic events between the Nanhe and the Anninghe fault indicates that both faults may have induced cascade rupture or triggered earthquakes—such related events may have occurred in 1496–1627. Additionally, by comparing the kinematic relationship of the faults at the eastern boundary of the Sichuan–Yunnan Block, we propose that the Nanhe Fault takes part in strain partitioning along the boundary. This interpretation reasonably explains the loss of the sliding rate between the Anninghe and Zemuhe faults, which also supports the GPS inversion results, and the discontinuous deformation model for the eastern margin of the Tibetan Plateau.

Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya

This study aimed at (i) developing, evaluating and comparing the performance of support vector machines (SVM), boosted regression trees (BRT), random forest (RF) and logistic regression (LR) models in mapping gully erosion susceptibility, and (ii) determining the important gully erosion conditioning factors (GECFs) in a Kenyan semi-arid landscape. A total of 431 geo-referenced gully erosion points were gathered through a field survey and visual interpretation of high-resolution satellite imagery on Google Earth, while 24 raster-based GECFs were retrieved from the existing geodatabases for spatial modeling and prediction. The resultant models exhibited excellent performance, although the machine learners outperformed the benchmark LR technique. Specifically, the RF and BRT models returned the highest area under the receiver operating characteristic curve (AUC = 0.89 each) and overall accuracy (OA = 80.2%; 79.7%, respectively), followed by the SVM and LR models (AUC = 0.86; 0.85 & OA = 79.1%; 79.6%, respectively). In addition, the importance of the GECFs varied among the models. The best-performing RF model ranked the distance to a stream, drainage density and valley depth as the three most important GECFs in the region. The output gully erosion susceptibility maps can support the efficient allocation of resources for sustainable land management in the area.

MANIFESTASI PANAS BUMI SERTA REKOMENDASI PEMANFAATANNYA DI DESA KOTO SANI DAN SEKITARNYA, KECAMATAN X KOTO SINGKARAK, KABUPATEN SOLOK, PROVINSI SUMATRA BARAT

The volcanic arc or volcanic arc that extends along the western side of Sumatra Island. Koto Sani Village, Kec. X Koto Singkarak, Solok Regency, West Sumatra Province is one of the areas on Sumatra Island that has geothermal potential. In terms of the developing tectonic order, namely in the form of a geological structure in the form of the Sumani segment of the Sumatra Fault which moves in a strike slip to the right or dextral. This geological structure is one of the main components forming the geothermal system apart from the heat source, reservoir rock, stratigraphic characteristics and the cap layer. This study aims to determine the geological setting, any geothermal manifestations found in the study area and estimate the temperature below the surface for recommendations for its use. The research method used was the interpretation of satellite imagery on the 1:25,000 scale of the Indonesian Rupa Bumi map (RBI), surface geological mapping, measurement of the physical characteristics of geothermal fluids, geochemical analysis of geothermal fluids (anion-cation) and then plotting Giggenbach's triangle diagram (1991). and geothermal geothermal calculations, as well as analysis of 18O and 2H isotopes. The research area generally forms a landscape of elongated hills with quite steep valleys as a result of tectonic activity in the form of geological structures as migration routes of hot fluids from the reservoir to the surface. This is reinforced by the results of the interpretation of river flow patterns, namely meandering or sub-dendritic and elongated or called parallel as an indication of disturbance from faults or faults. Geothermal manifestations found are hot springs, sintered silica and hydrothermal alteration rocks. The estimated temperature below the surface is 407.64°C with a high temperature category and is recommended for direct use, such as the tourism and fisheries sector, as well as indirect use, such as geothermal energy power plants.

Integrated workflow for interpretation of satellite imageries using machine learning to assess and monitor algal blooms in Utah Lake, USA

An integrated workflow is developed to estimate the spatial distribution of harmful algal blooms, especially cyanobacteria concentrations in inland water bodies. The methodology comprises satellite data extraction and preprocessing for atmospheric and water surface corrections, identifying feature importance, in-situ sample collection, training and testing of machine learning algorithms, and prediction. Six input bands are selected using feature importance algorithms from 12 original bands of Sentinel-2 satellite imagery. In-situ sample data that are synchronous with Sentinel-2 image capture time were obtained from a public database. These models are evaluated and compared using spider plots of different error calculations. The workflow developed in this study and the predicted spatial concentration of harmful algal blooms across the lake can be used to improve warning and advisory systems for the public and avoid exposure. The incorporation of other parameters such as water temperature, nutrient concentrations, and surface wind speed could improve the machine-learning models.

Quantitative estimates of oil-seepage rates from satellite imagery with implications for oil generation and migration rates

A new approach to quantifying oil fluxes from natural seafloor seepage sites based on the dynamic characteristics of floating oil slicks is presented. It enables rapid analysis of interpreted satellite imagery and permits large datasets to be analyzed. The method uses the drift path of the oil on water surface, termed the “centerline” of the slick, along with average current and wind speeds to estimate the age of seepage slicks. The results of the CL (centerline) method compare favorably with the hindcast modeling approach which requires time-resolved analysis of wind and current for each slick. The CL method yields an average flux estimate within the range of the flux documented by on-bottom capture for a seafloor oil pollution source. A survey of published flux estimates using a variety of techniques reveals a wide range with on-bottom estimates (0.08–12 ml/s) generally lower than estimates from slicks (1.6–125 ml/s). Determination of fluxes from three seepage sites in the Gulf of Mexico (GC600, Bush Hill, and TYK) reveals a wide range in seepage rates from 2.4 to 52.9 ml/s and flux distributions. Comparison of the observed fluxes from seeps with potential fluxes from a source region can be used to constrain source rock parameters (e.g., thickness, richness, area) and to understand the cumulative oil supply represented by seeps over geologic time scales.

Assessment of land use/land cover change detection of Mettupalayam Taluk, Coimbatore district, Tamil Nadu, India, using RS and GIS

Understanding changes in land use land cover is essential managing and monitoring natural resources and development, particularly where urbanization is expanding. In present study, describes land use/land cover (LU/LC) mapping and change detection analysis of Mettupalayam taluk of Coimbatore district in Tamil Nadu. Estimation in ArcGIS for LU/LC classification from LANSAT images, is the best method for classifier for the different features were used to classify the satellite images are used to classify the land use/land cover change s for the given period. The results indicated that land cover changes have occurred in slight decrease in forests, vegetation and mountain and a corresponding tremendous increase in settlement. The comparison of LU/LC in 2000 and 2019 derived from topo-sheet and satellite imagery interpretation indicates that the settlement area shows variations. Settlement is an area of human habitation, which has a cover of buildings and a network of transport and other civic amenities, due to increasing population and the land is converted into habitation. The (Urbanization) settlement/ built-up areas have increased by 7.29% in 2000 while in 2019 it has increased to 16.43 %. It is found urban area increased due to population growth cum rapid economic progress.

Application of Spatial Model for Potential Flood Hazard Susceptibility at Trumon Area, South Aceh Regency of Indonesia

Indonesia is currently embarking on a transition from a ‘risk retention’ to a ‘risk transfer’ strategy for managing the impact of disaster events. The risk transfer strategy, i.e., insurance policy and protection, requires high-level preliminary risk assessment, which requires detailed attention and analysis in producing hazard mapping. Improvement of methods requires, preferably, the non-deterministic index method in a GIS environment, to produce reasonably good quality hazard susceptibility mapping. Recently, a new spatial method has been developed to improve the parameterization of the spatial analysis method for watershed-scale flood hazard susceptibility mapping. Those parameters, which include the Topographic Wetness Index (TWI), Rainfall Intensity (R), Distance to rivers (D), Altitude (A), Land use (L), and Soil type (S), configure the proposed method called “TWIRDALS”. The present study aims at testing the spatial model TWIRDALS watershed at the Trumon Area, South Aceh Regency, Sumatra Island, Indonesia. Historical flood events associated with the watershed have escalated over the last 25 years in the Trumon watershed. Several steps of geospatial analysis in this study use multi-temporal satellite imagery from 1995 to 2021 to identify area changes in land use rendering over the watershed. The satellite imagery interpretation reveals a remarkable land use change, particularly of the previously 2130 ha of peatland forest observed in 1996, to become a 10,000 ha palm oil plantation in 2021. This current situation has made the Trumon area the recipient of more frequent floods, i.e., from a five-year return period to an annual event.

Dynamics of Low-Lying Sandy Coast of the Gydan Peninsula, Kara Sea, Russia, Based on Multi-Temporal Remote Sensing Data

The retreat rates of Arctic coasts have increased in recent decades at many sites, and an essential part of coasts considered accumulative before have turned erosional due to global climate changes and construction in the coastal zone. In this paper, we study a 7 km long coastal section of the western Gydan Peninsula in a new construction area. Based on the interpretation of multi-temporal satellite imagery, we assessed coastal dynamics in distinct periods from 1972 to 2020. We analyzed the geological structure of the coast as well as changes in hydrometeorological parameters with time, and considering the human impact, we proposed the main drivers of spatial and temporal variations of coastal dynamics. The studied low-lying sandy accumulative marine terrace was more or less stable in the period before construction (1972–2014). However, with the area’s development, the coast dynamics changed drastically: in 2014–2017, three-quarters of the studied area experienced retreat, and the average retreat rate amounted to 5.8 m/yr, up to 28.5 m/yr near the construction sites. We relate this coastal erosion intensification to human impact combined with the growth of hydrometeorological forcing. Although coastal erosion slowed down after 2017, the retreat trend remained. In the coming years, with Arctic climate warming, erosion of the studied coast will continue.

Effect of Sea Surface Temperature and Chlorophyll-A on the Total Catches of Lift Net Fishery in the Padang Pariaman Regency, Indonesia

The West Sumatera Waters has the potential to be developed for capture fisheries. This potential is utilized through the application of local knowledge for better fishing operations. Fishermen in West Sumatera in determining fishing ground still use the traditional method, namely using previous experience even though on vessels of > 40 GT and it has been assisted by the fish finder. This study aims to analyze the distribution of sea surface temperature (SST) and chlorophyll-a through Aqua Modis satellite imagery interpretation and analyze its effect on boat lift catch from December 2019 to March 2020. The remote sensing technique via satellite is an efficient method to determine the distribution of sea surface temperature and the distribution of chlorophyll-a. Data from satellites is very helpful in determining temperature and chlorophyll-a. The results obtained are in December 2019 to March 2020 SST distribution ranged from 27 to 30.9°C with dominant SST fluctuating every month and chlorophyll-a concentration ranging from 0.13 to 4 mg m³ with dominant chlorophyll-a concentration fluctuating every month. Sea surface temperature (SST) and chlorophyll-a simultaneously did not affect the catch.

Aplikasi Geographic Information System dan Remote Sensing untuk Pengembangan Potensi Wisata Agro di Kawasan Objek Wisata Air Terjun Tegenungan, Desa Kemenuh

Wiradhika Mahayasa Putra. NIM. 1706541045. GIS Application and Remote Sensing for Agrotourism Potential Development In Tegenungan Waterfall Tourism Area, Kemenuh Village, Sukawati District. The implementation of agricultural and tourism activities is an agro-tourism concept can be synergized with other natural tourism objects such as waterfalls, Tegenungan Waterfall tourist attraction areas, Kemenuh Village, agro-tourism potential development can be carried out. The purpose of this research is to inform the area database based on GIS and remote sensing, identification and information on agro-tourism potential, mapping of tourist attractions and planning of locations for agro-tourism objects. Research methods: literature study, interpretation of satellite imagery, recording of information on potential tourist objects, calculation of the status of potential tourist objects, recording of regional databases, identification of agro-tourism objects, calculation and division of potential classes, mapping of tourist attractions. The results of this study indicate the value of the potential class of Tegenungan Waterfall tourism objects is in the good category with a value of 28. The map of the area displays a database of area, road length and land use, the value of the agro-tourism destination plan are classified as good with a value of 25, tourist information maps present an overview of tourist attractions at the location, the agro-tourism plan map displays the agro- tourism object plan: rice field cycling, plowing, canang making, educational tours, banana and coconut biodiversity and subak view.

Landscape change trends and their impacts on coastal tourism resources in the future: a case study from pak Phanang, Thailand.

This study aims to investigate the impacts of landscape change on natural tourism resources in coastal areas of Pak Phanang and to estimate future landscape changes. Data on tourism resources, land covers and coastline modification were collected on a random basis and combined with in-depth interviews of key informants. Data analysis seeking to demonstrate future coastline modification and projections interpreted satellite imagery using the hybrid approach to generate periodic coastal maps and the Dinamica EGO program to analyze future change rates of the coastline and landscape change. The results found that during 2021-2036, the areas of rice/vegetable farms, mudflats, beaches,and beach forests are likely to increase. The impacts on tourism resources can be negative or positive. The negative impacts arising from such changes include the decrease in the number of beaches in the southeast area and the increase in mudflats in Pak Phanang. The positive impacts include the beaches and beach forests in the northeast area increasing. These results can equip governments, communities and tourists with knowledge on the changes in landscape and tourism areas to prepare and adjust for such changes in the future.

Visual interpretation of satellite imagery for hotspot dust sources identification

Identifying dust sources using remotely sensed satellite imagery is one of the most effective ways of studying dust emission and dynamics. Even though complex algorithms and sophisticated models that take advantages of different data sources have been developed, human knowledge and experiences are key factors in the accurate dust sources identification. Visual interpretation of satellite imagery (VISI) is essentially influenced by interpreters' subjective judgment. Meanwhile, the capabilities of VISI are not comprehensively explained in the literature. Therefore, this study performed an experimental VISI to identify hotspot dust sources (HDS) in the Tigris and Euphrates basin (TEB) involves a variety of active sources of global dust emission. In this regard, the characteristics of various satellite sensors including spatial, temporal, and spectral resolutions, swath width, free availability, and length of archive time were explored. Accordingly, the MODIS-Terra/Aqua was qualified as the best sensor for HDS identification. By visual interpretation of 27,239 MODIS images, a total of 10,422 HDSs was identified in TEB in the 2000–2021 period. The research findings revealed that the interpreter's proficiency in using specific VISI keys, knowledgeability about the geoenvironment, and familiarity with the study area can significantly reduce subjectivity and increase the accuracy of HDS identification.

Geospatial Analysis of Abandoned Lands Based on Agroecosystems: The Distribution and Land Suitability for Agricultural Land Development in Indonesia

The Indonesian land area is 191.1 million ha, part of which is abandoned land in various agroecosystems that have the potential for expanding the agricultural area. The purpose of this research was to geospatially analyze abandoned land based on its agroecosystem at the national and district levels, as well as to evaluate the land suitability of the land for expanding agricultural development. The methods included: (1) geospatial analysis of the national land cover map at a scale of 1:250,000 combined with soil and climate information to identify abandoned land and examine its agroecosystem, (2) selecting representative districts in each agroecosystem for visual interpretation using high-resolution imagery, i.e., SPOT 6/7, (3) assessing the land suitability of abandoned land for agricultural development at the national and district levels, and (4) predicting national abandoned land and its land suitability. The essential finding is the identification of abandoned land at around 42.6 million ha in Indonesia distributed over six agroecosystems, with the widest being in dry lowland and wet climates. Then, 54 districts were selected to characterize abandoned land by using SPOT 6/7 high-resolution imagery and were interpreted visually. It was found that the abandoned land covered approximately 16.9 million ha. The distribution of abandoned land from the interpretation of satellite imagery was smaller than that of geospatial analysis due to differences in the map scale and the use of ancillary data. The identification of abandoned land from high-resolution imagery should be carried out for all regions of Indonesia to accurately map the distribution of the abandoned land and characterize the properties. However, it requires a large amount of time, cost, and facilities to complete the inventory. The geospatial analysis that combined imageries and ancillary data identified 27.7 million ha of abandoned land suitable for expanding the agricultural area. The largest suitable abandoned land for the purpose was found in the lowlands with a wet climate, especially in Papua, Kalimantan, and Sumatra islands. The identified suitable abandoned land of 54 districts differed by scale, in which it was 11.2 million ha at the scale of 1:250,000 and 8.5 million ha at the scale of 1:50,000, respectively. The potential land expansion for food crops, particularly paddy fields, was only 2.2 million ha, located in mineral swamp land, which was predominantly located in Papua, with inadequate accessibility. Expanding paddy fields for national food security in the future would be constrained by less suitable land resources, while the near future challenge is the competition of land allocation for agricultural and non-agricultural sectors, as well as for food crops and plantations.

Monitoring Culturable Wastelands Using Satellite Images in A Part of Tarai Region of Uttar Pradesh

Land is a basic natural resource on which development of human with other living beings. Inadequate management of natural resources has affected biodiversity, agriculture productivity and ecological balance. It is necessary not only to conserve the degrading natural resources but also to bring in available culturable wastelands under agriculture after reclamation to achieve required production of food. Salt affected wastelands, waterlogged & marshy lands and undulating lands are the common culturable wastelands found in the Tarai region of Uttar Pradesh. In this study the land use changes have been monitored over a period of 16 years in a cluster of villages in a part of Lakhimpur District, using Google earth imageries and GIS software. An on-screen visual interpretation of satellite imageries of 2003, 2011, 2015 and 2019 to monitor land use changes revealed a decrease in culturable wastelands by 46.19 % and increase in agricultural area from 69.34 % to 83.79 % in a period of 16 years. The progressive farmers have used pressmud, which is readily available from nearby sugar mills for reclamation of culturable wastelands.

Geomorphological Analysis of Ayad River Basin, Udaipur (India) using Remote Sensing and GIS Techniques

Geomorphological analysis is useful for managing land resources and aid in resource inventory, mapping, and management for a variety of purposes. The science of geomorphology is now better able to comprehend surface dynamics because to recent improvements in remote sensing and GIS, the availability of satellite-based DEM, and improvements in mathematical modelling skills. The study is emphasis on utilization of secondary data which have been compiled from different sources e.g. SOI, USGS, NBSS&LUP, GSI. LULC, drainage, river-lake link, soil, geology, landform, geomorphology have been analyzed based on satellite imagery, DEM, toposheet, and other secondary data. Dendritic and sub-dendritic drainage pattern is common in Ayad basin and drainage density is ranging between 0.46 and 3.45 (Km/Km2). Eight artificial lakes are linked together in a chain in the saucer-shaped valley, which is the first historic river linking project in India. Lithology ranging in age from Archaean to Upper Proterozoic are present and belong to three geological cycles - Bhilwara, Aravalli, and Delhi supergroups. Landforms of the area are mainly lower-flat, and ridge cover only 13% of the area. A very detailed geomorphological map has developed by visual image interpretation of satellite imagery, DEM data, toposheets, geological map and classified in 13 broad geomorphic units, and 63 micro-geomorphic units. Through the percentage hypsometric curve method, the hypsometric integral is 27% which indicates the old stage of the basin.

Heavy Oil Potentials in Central Sumatra Basin, Indonesia Using Remote Sensing, Gravity, and Petrophysics Data: From Literature Review to Interpretations and Analyses

Central Sumatra basin is located on Sumatra Island, Indonesia, and is considered an oil prolific basin that produces heavy oil. The basin is believed to have unexplored heavy oil potential. Therefore, this study aims to map the heavy oil potential distribution in the basin using surface and subsurface lineaments analyses interpreted from satellite imagery and gravity data, and assisted by well log/petrophysics analysis. A thorough basin analysis was conducted based on surface/subsurface structures’ control and source rock location settings to map all potential heavy oil traps. The gravity anomaly data interpretation identified the low areas and lineaments in NW – SE, and N – S directions. The interpretation of satellite imageries showed very similar lineament patterns with the same general direction. It was observed that there is continuity between subsurface and surface lineament features, which provide contact between reservoirs and surface water sources, thereby facilitating heavy oil generation. Overlapping the lineament interpretation of gravity and satellite imagery data, supported by petroleum system understanding and verification from wells data have confirmed 7 heavy oil trap potential areas within the sedimentary basin.