Tag: GIS

Prospects of Geographic Information System and Multi-Source Data Integration in Enhancing the Accuracy of Above-Ground Biomass and Carbon Stocks Estimation

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Citation

Omali, T. U., Akpata, S. B. M., & Onyevu, R. O. (2026). Prospects of Geographic Information System and Multi-Source Data Integration in Enhancing the Accuracy of Above-Ground Biomass and Carbon Stocks Estimation. International Journal of Research, 13(1), 484–492. https://doi.org/10.26643/ijr/2026/23

Omali, Thomas Ugbedeojo (PhD)1*; Akpata, Sylvester Balm Mifue2 (PhD);

Onyevu, Rosemary Onyinye (MSc.)3

1National Biotechnology Development Agency (NABDA), Abuja, Nigeria.

2Department of Geoinformatics and Surveying, University of Abuja, Nigeria

3Department of Geoinformatics and Surveying, University of Nigeria, Nsukka, Nigeria.

Corresponding Author’s Email Id: t.omali@yahoo.com

Abstract

Evaluating Above-Ground Biomass (AGB) accurately and the successive calculation of carbon stocks are fundamental process for understanding the global carbon cycle, climate change mitigation and sustainable forest management. The traditional field-based methods for AGB and carbon stocks assessment is effective; but then, they involve more cost, time, and they are not scalable to a large area. Thus, the utilization of cutting-edge technology to support the conventional approach is expedient. This study is a mini review that discuss the prospects of Geographic Information System (GIS) and the need to integrate various data in evaluating AGB and carbon stocks. First, literature search was conducted based on which relevant and quality published articles were selected and used to discuss the topic. The result signifies that the spatially explicit GIS-based techniques can be used to create georeferenced estimates of AGB, and carbon sink/stock potential. Also, data from different sources has their unique advantages and drawbacks, which can affect the accuracy of AGB and carbon stocks assessment. However, integrating these data has proven to be highly efficient. Summarily, GIS provides the essential platform for acquiring, integrating, analyzing, and visualizing diverse data sources. This enables the creation of spatially continuous and accurate map of AGB and carbon stocks across landscapes, regions and continents.

Keywords: Above-Ground Biomass, Activity Data, Carbon Sequestration, forests, GIS, REDD+

  1. Introduction

The tropical forests are generally characterized by high biomass and carbon content, which makes them to have huge influence on the global carbon cycle. They have unlimited potential for mitigating carbon dioxide (CO2) emission through suitable conservation and management. On the other hand, deforestation alone is responsible for approximately 12% of the global human-induced emissions of greenhouse gas (GHG) and peat oxidation while fires on degraded peat lands causes another 6% [1]. Also, 10–25% of global emissions resulting from anthropological activities are linked to combined impact of logging and forest re-growth on abandoned land [2,3]. The significance of deforestation in global carbon cycle is apparent. This gave rise to the Bali Action Plan agreed on enhancement of national/international action on climate change mitigation. This includes inter alia, consideration of policy approaches and payment with regards to reducing forest-related emissions in developing nations [4].

The forest biome is a massive carbon pool that can diminish emissions of net GHG through reduction of sources that enhance sinks of CO2 [5, 6]. Precise spatial and temporal evidence of the existing condition of carbon sources and sinks is required for policy formulation to mitigate greenhouse effects [7]. Monitoring biomass and carbon stocks accurately can now be achieved, thanks to increasingly available of fine resolution and large spatial geographically referenced data. Also, the data can be used to make models that establish the relationship between biomass and their drivers can be used to estimate biomass and carbon at global level. So far, there are many GIS-based spatially explicit approaches for spatiotemporal estimation of carbon sink and stock [8,9]. GIS is a typical processing and visualization tool [9,10]. Nevertheless, much of the existing studies on estimation of terrestrial carbon sequestration and land-use spatial planning have not integrate process-based models with GIS [11].

Mapping and quantifying the tropical AGB is essential in the estimation of carbon dynamics resulting from the modification in LCLU [12]. Though site-specific estimates of AGB based on various modelling is common practice, pan-tropical or global estimates are developed through the combination of ground inventories and remotely sensed forest data. For instance, Saatchi et al. [13] mapped the pan-tropical live biomass at 1-km spatial resolution in 2011. They used a wide-ranging inventory data from 4,079 plots and many remote sensing techniques (optical, microwave and LiDAR sensors). It was revealed that the total of 247 PgC woody biomass was stored in the tropical vegetation. In this, AGB contributed 78% of carbon stocks while 22% of carbon stocks was from below ground biomass. An improved map of the pan-tropical AGB at 500m resolution emerged in 2012 as a result of additional work. The integrated data to create this map were from field inventories, 70 meters resolution LiDAR, and 500 meters resolution MODIS images [12]. Similar to this, Kanja, Zhang, and Atkinson [14] evaluated the capacity to map the AGB of Zambia’s Miombo woodlands using data from Landsat-8 OLI, Sentinel-1A, and extensive national forest inventory.

  • Methodology

This review discussed Geographic Information System and multi-source data integration for enhancing the accuracy of above-ground biomass and carbon stocks estimation. Relevant materials used consisted of research articles availed from reputable electronic databases including Web of Science and Scopus. Apart from research articles, grey literatures were equally cited in this paper. The main search for information on the review topic was conducted from September 2025 to November 2025.

  • Results and Discussion
    • Role of Geographic Information System for AGB and Carbon Stocks Estimation

A Geographic Information System is a computer-based tool for storing, retrieving, modifying, analysing, and displaying georeferenced data. It is an automated mapping and analysis system, which depends on data that are related to the geographic location of physical entities, and activities. Its intention is to locate and describe places on the Earth’s surface.

GIS data can be used for spatiotemporal monitoring of Land use and Land cover (LULC). LULC and LULC change are used as Activity Data (AD) in carbon stocks assessment. LULC is responsible for approximately 10 percent of global greenhouse gas [15]. According to Yadav [16], the boundaries of LULC classes from satellite-based analysis are typically transferred to a map to create mapping units. These units can then be digitally transformed into a GIS environment to create a vector polygon map. It is noteworthy that GIS is a spatial platform for creating data layers and databases. Apart from accurate and effective management of features [17] such as forests, GIS can be used to easily create spatial models for simulating various situations.

Additionally, georeferenced estimates of carbon sink and stock potential can be produced using spatially explicit GIS-based methods. The GIS is typically used for processing model inputs (e.g., soil texture, land cover) and visualizing the outcomes. For instance, Fatoyinbo and Simard [18] used GIS to combine height data from the Shuttle Radar Topography Mission (SRTM) and spatial coverage of the mangrove generated from Landsat imagery with the intention of computing Africa biomass of mangroves. Furthermore, Malysheva et al. [19] studied the GIS-based assessment of carbon dynamics for Russian forests. In another study, Kehbila et al. [20] carried out a comparative multi-criteria evaluation of Cameroonia’s sustainable development plans and climate policies to create a GIS decision-support tool for the creation of the best possible REDD+ plan.

It is good to note that carbon sequestration provides a major economic value of the ecosystem. Thus, it has become an essential tool for application by United Nations Framework Convention on Climate Change (UNFCCC) in REDD+ programme. Generally, the financial estimation of forest environment services is significant because it assigns an amount on nature. This estimation can correspondingly serve the purpose of guiding climate change policy-makers and decision-makers [21]. The core of REDD+ initiative is the delivery of financial reward to developing nations for keeping carbon stored in their natural forests. The economic worth of carbon sink and stock of forest environment can be mapped and quantified in a GIS environment. In this case, GIS is used for developing database, executing spatial analysis and mapping economic worth. The appraisal process used to determine and monetize the amount of carbon stock and carbon sequestered was measured and validated by Pache et al. [22]. This was accomplished through combination of terrestrial scanning, and monetary valuation to display the sequestered carbon’s spatiotemporal market value

The ground-based field measurements are the most accurate methods for biomass assessment. It is used to obtain precise data on tree for creating allometric models for computing AGB [23]. But field measurements are possible only on a limited number of points at the sample plot scale. Also, their sampling density are insufficient to afford the requisite spatial variability; and it is usually hard to sample large areas [24]. The ground-based field measurements are also costly, time consuming, and labour intensive [25]. Remote Sensing technology is thus adopted for collecting data or for large-scale mapping and monitoring of various entities such as forest AGB, vegetation structure, vegetation productivity and others [26]. However, remote sensing application has its own associated issues.

By and large, three of the key data sources that are currently being employed for forest AGB mapping and assessment include ground surveys, satellite imagery, and LiDAR. Of course, each data source has its advantages and disadvantages [27]. Thus, the combination many data from various sources can help in analyses of variables that cover a large extent. Integrating multisource data including satellite imagery into a GIS is a potential method for producing spatially-explicit estimates of AGB across a large extent. The majority of current forest estimating research has shown higher accuracy and capacity over a wide area by merging multisource remote sensing data [28]. For example, Forkuor et al. [29] mapped the forest Above-ground biomass by combining Sentinel-1 (S1) and Sentinel-2 (S2) with derivative data. Ma et al. [30] used PALSAR-2 and topographic data to predict AGB in China. Tariq, Shu, Li, et al. [31] effectively analyzed prescribed forest burning and showed Using S1 data.

Many types of satellite data can be used to estimate forest biomass [32] with each type characterized by its advantages and disadvantages. For example, optical sensors were primarily used for forest remote sensing [33]. Although optical data are commonly employed in AGB estimation, their general use is constrained by data saturation issues in places with high vegetation biomass or canopy density, and regular cloud cover. The data from Radar may flow through forest canopies and clouds, unlike passive optical systems [34], however it is impacted by signal saturation [35]. Furthermore, LiDAR data can record a forest’s vertical structure in great detail, hence, it is an excellent substitute for optical and radar data. The 3D data provided by LiDAR is closely linked to forest biomass [36].

  • Conclusion and Future Scope

In this paper, we reviewed the application of Geographic Information System, and the significance of data integration in appraising above-ground biomass and carbon stocks. It has been demonstrated in this study that spatially explicit GIS techniques can be used to create georeferenced estimates of AGB, and carbon sink/stock prospects. With GIS, it is easy to process model inputs and also visualize the results. A GIS decision-support tool for creating the best possible REDD+ plan is available through the GIS-based evaluation. Also, many data sources available for mapping and estimation of AGB and carbon stocks has their unique pros and cons. Therefore, integrating them will normally produce highly accurate result.

Finally, there is likelihood that spatially precise outlines of the worth regarding forest carbon sink and stock may soon require at various scales. Thus, be mapped using GIS techniques can be used to map the forest ecosystem and its values. Of course, this will provide managers with a foundation for identifying which areas need additional focus.

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Delineation of watershed in Amravati tehsil using geomorphological investigations through Remote Sensing and GIS techniques

Khadri S.F.R*, Sachin Thakare and Pooja Surkar

  Abstract:

In this study an attempt has been made to understand various geomorphological factors controlling the various landforms which in turn helpful in the delineation of watershed in Amravati tehsil through remote sensing and GIS techniques. The study area exposes part of Pedhi River and Kholad River which is a part of Wardha Watershed. The present investigations have helped to understand the groundwater potential as well as nature of the watershed with the help of detailed geomorphological investigations. Satellite remote sensing data as well as topographic data has been widely utilised in this study to identify the watershed and groundwater potential zones with the help of latest available techniques.

The results of the present study demonstrate the presence of various hydro geomorphological zones showing their groundwater potentialities which vary from excellent to poor. The study area covering Pedhi watershed shows excellent to good ground water quality whereas the Kholad and Kapasi watersheds show moderate to poor ground water quality. Overall, the present study demonstrates the useful ness of remote sensing and GIS techniques in the delineation of potential aquifers in the region which plays a major role in the sustainable   management of water resources in the Amravati region.

Key Words: Remote Sensing, GIS, Geomorphology, Watershed, Satellite, Topography.

 ——————————————————————*******—————————————


Introduction:

A watershed can be defined as the area of land that drains to a particular point along a stream. Each stream has its own watershed. Topography is the key element affecting this area of land. The boundary of a watershed is defined by the highest elevations surrounding the stream. A drop of water falling outside of the boundary will drain to another watershed (Sewickley Creek Watershed Conservation Plan, 2003). From a planning standpoint, watershed is considered the most ideal unit for analysis and management of natural resources. For optimal use of environmental resources in a region, integrated watershed development approach is still viewed by many to be the most ideal as it helps in maintaining the ecological basis of resources utilization (Sahai 1988). Geomorphology is defined as the science of landforms with an emphasis on their origin, evolution, form, and distribution across the physical landscape. The science that deals with surface features of the earth, their forms, nature, their origin and development is termed as geomorphology. DAVIS (1912) first projected the concept of geomorphic cycle. According to bauling (1950), the role of factors that are important the geomorphology are lithology, stratigraphy, climatic variation and the regional basis for the development of landforms. The use of remote sensing technology for Geomorphological studies has definitely increased its Importance due to the establishment of its direct relationship with allied disciplines, such as geology, soils, vegetation/Land use & hydrology.

Geomorphological mapping involves the identification and characterization of various landforms and structural features. The various landforms can influence a conservation area in many ways like slope gradient, elevation and aspect, affect the quantity of solar energy, water, nutrients and other materials, while the slope affect the flow of materials. Slope is also the deciding factors of intensity of disturbance, such as fire and wind, which are strongly influenced by the pressure of vegetation (Swanson et al 1988).

Study Area:

Amravati Tehsil basically part of Amravati city and the villages around it lying between 220 45” N to 210 20” N and 770 32” E to 780 02” E. Amravati is District place and major city in Vidharbha region. Amravati names comes from Hindu goddess “Ambadevi”, in Mahabharata epic Amravati is a capital of Vidarbha Naresh, and it is a part of Varhad (Berar).

Geography:

Amravati Tehsil is bordered with Achalpur tehsil as well as Murtizapur Tehsil in North direction and Chandur Bazar Tehsil touches the boundary in east and west direction. Wardha River is naturally separate Amravati District from Wardha District. Wardha River is a major River of Vidarbha region which is join Painganga River in the boundary of Marathwada. Pohra Malkhed is protected forest area in Amravati. The average elevation is 543m from MSL.

Climate:

Climate of Amravati Tehsil is hot and dry, April to June Month having extremely heat and temperature goes to 450 C as well as winter season temp goes down 110 C which shows temperature variation, Rainy season start from end of June Month to September in an average.

Soil:

There are three main types of soil present in Amravati Tehsil which is-

  • Deep Black Soil b) Medium Black Soil           c) Shallow black Soil

Crop pattern:

Amravati Tehsil is having different types of Crop Pattern such as Cotton, Sorghum (Jawar), Green Gram (Moong), Soybean etc. but Amravati tehsil as well as whole District is famous for Orange (Citrus Spp.)

 Rail/Road:

In Amravati tehsil Badnera is Major Railway Station of Central Railway. National Highway 6 is passed through the Amravati Tehsil.Fig.1 shows the location of Amravati Tehsil.

Fig. 1 Location Map of Study Area

Materials and Method:

Data Used

  1. Toposheet Approved by Survey of India Having 1:50000 scale
  2. Satellite Imagery LISS data having 23.5m resolution
  • ERDAS Imagine Remote sensing Software
  1. ARC GIS Software

 

 

Methodology

 

 

Fig.2: Flowchart showing the methodology used for Watershed

Software used:

  • Arc GIS 10: This software has been developed by ESRI Inc. it is one of the leading software for desktop GIS and mapping. Arc GIS gives the power to visualize, explore, query, and analyze data geographically. In this project Arc GIS has been used to display raster map, digitizing different features and querying the data for finding the attributes for any feature on map. Arc GIS Spatial Analyst is a tool which helps in analysis and understanding of spatial relationships in our data. Reclassify tool has been used to reclassify different data and raster calculator has been used for overlay analysis and calculation of final results.
  • Generation of contour map: Contours are polyline that connect points of equal value of elevation. The elevation points were prepared from toposheets on a scale of 1:50000 collected from Survey of India (SOI). The collected toposheets were scanned and registered with tic points and rectified. Further, the rectified maps were projected. All individual projected maps were finally merged as a single layer. The contours were digitized with an interval of 10m. The contour attribute table contains an elevation attribute for each contour polylines. The contour map was prepared using Arc Map of Arc GIS 10. Contour map is a useful surface representation because they enable to simultaneously visualize flat and steep areas, ridges, valleys in the study area.

Fig. 3: Contour Map of Amravati Tehsil

  1. Generation of digital elevation model (DEM): A DEM is a raster representation of a continuous surface, usually referring to the surface of the earth. The DEM is used to refer specifically to a regular grid of spot heights. It is the simplest and most common form of digital representation of topography. The Digital Elevation model for the study area was generated from the Tin.

Fig. 4: Digital Elevation Model of Amravati Tehsil

  1. Generation of slope map: The Slope function in Arc GIS 10 calculates the maximum rate of change between each cell and its neighbors. Every cell in the output raster has a slope value. The lower the slope value indicates the terrain is flatter and the higher the slope value, the steeper the terrain. The output slope raster was calculated in both percent of slope and degree of slope. Slope map was prepared from the DEM.

Fig. 5: Slope Map of Amravati Tehsil

 

  1. Generation of watershed: Watershed of the study area was demarcated using the software Arc GIS. Drainage pattern was taken as the input data.

Fig. 6: Drainage Map of Amravati Tehsil

 

Fig. 7: Water body of Amravati Tehsil

Fig. 8: Watershed of Amravati Tehsil

  1. Ground Potential zones map: Ground Water Potential Zones map of Amravati Tehsil Shown in fig. 9 having four different types of zone, they are Excellent, Good, Moderate and Poor. The Ground Water Potential Zone of Study area generated with the help of drainages, geomorphology and land use land cover with integration of Remote Sensing and GIS technique as well as Geology of that area plays an important role. Geomorphology of the study area having alluvial plain, Denudation Hills and Platues. During weighed overlay analysis, the ranking has been given for each individual parameter of each thematic map and weights of 25%, 35%, 30% and 10% were assigned according to their influence for Soil, Hydro-geomorphology, Land use/Land cover, and Slope themes respectively and obtained the ground water potential zones in terms of Excellent, Good, Moderate and Poor zones in the form of a GIS map.

Fig. 9: Ground Water Potential Zone of Amravati Tehsil

 

  1. Geomorphology Map: Geomorphology as a science developed much later than geology although several aspects of geomorphology are embedded in geological processes. Geomorphology deals with the genesis of relief forms of the surface of the earth’s crust. Geomorphological mapping and necessary supporting data are crucial to developing countries that are usually under severe environmental and demographic strains. Approaches and methods to map the variability of natural resources are important tools to properly guide spatial planning. In this paper a comprehensive and flexible new geomorphological combination legend that expands the possibilities of current geomorphological mapping concepts. The piece-by-piece legend forms a “geomorphological alphabet” that offers a high diversity of geomorphological information and a possibility for numerous combinations of information. This results in a scientific map that is rich in data and which is more informative than most previous maps but is based on a simple legend. The system is developed to also be used as a basis for applications in GIS.
  2. 10: Geomorphological Map of Amravati Tehsil

Results and Discussion

Five major watersheds were identified in the area. The area occupied by the largest watershed is 167 Sq.km and it falls under the Sub-watershed category which covers around 66.01 % of the area under study, the second watershed has an area of 37 sq km and this also falls in the sub-watershed category and covers around 14.62 % of the study area, the third watershed has an area of 35 Sq.km and falls in the category of Micro-watershed occupying about 13.83 % of the study area. There are two small watersheds having an area of 5 Sq. Km. and 9 Sq. Km respectively falling in the category of Mini-watershed and covers around 4% of the study area

  • DEM is the 3-D presentation of the surface derived by the interpolation of contour map. It represents x, y and z-axes in pixel size of the order 23.5 meters. The altitude or z axis ranges from 291 meters to 466 meters above sea level
  • Digital slope was derived from DEM on pixel size of order 23.5 meters
  • Ground water potential zones were identified on the basis of slope of the area. Five classes i.e. very good, good, moderate, poor, very poor, were identified. Most of the area comes under very good and good ground water potential zones. The area which has 1-3% slope has very good ground water potential due to nearly flat terrain, area having 3-5% slope has good ground water potential due to slightly undulating topography and some run-off, area with 5-10% slope has moderate ground water potential because these areas have relatively steep slope leading to high run-off, areas with 10-15% and 15-35% slope has poor ground water potential due to steep slope and higher run-off

Geomorphology:

Geomorphology as a science developed much later than geology although several aspects of geomorphology are embedded in geological processes. Geomorphology deals with the genesis of relief forms of the surface of the earth’s crust. Certain Natural Processes are responsible for the forms of the surface of the earth. The earth’s surface forms are primarily due to hypo gene or endogenous processes, which include diastrophism, leading to geologic structure, tectonic activity and volcanism leading to volcanic landforms.

Alluvial Plain:

An alluvial plain is a relatively flat landform and created by the deposition of highlands eroded due to weathering and water flow in study area. The sediment from the hills is transported to the lower plain over a long period of time. It identified on the imageries dark reddish moderate to fine texture due to agriculture activities. Alluvial deposits of the area constitute gravel, sand, silt or clay sized unconsolidated material. The area under alluvial plain cover in geomorphological map is 246 sq km.

Denudational Hills

Denudetional hills are the massive hills with resistant rock bodies that are formed due to differential erosional and weathering processes. These hills are composed of Vindhyan sediments which are fractured, jointed having no soil cover moderate to steep slope. On the satellite image, these landforms were identified by light or dark brownish with mix green color due to thick forest cover. The area under Denudetional hills cover in geomorphological map is 32 sq km.

Structural Hills

Structural hills are representing the geologic structures such as- bedding, joint, lineaments etc. in the study area. They are located in the eastern parts of the study area having greenish and reddish tone with rough texture on the satellite image. The area under structural hills cover in geomorphological map is 3 sq km.

Flood Plain

A flood plain is an area of land that is prone to flooding. People realize it is prone to flooding because it has flooded in the past due to a river or stream overflowing its banks. A flood plain usually is a flat area with areas of higher elevation on both sides. The area under flood plain cover in geomorphological map is 1 sq km.

Habitation Mask:

A habitation Mask is an area of land that is occupied by human being. It is human settlement area. It is defined as an area of human habitation developed due to non-agricultural use and that which has a cover of buildings, transport, communication utilities in association with water, vegetation and vacant lands. The area under Habitation Mask cover in geomorphological map is 118 sq km.

Plateau:

A plateau is an elevated land. It is a flat topped table standing above the surrounding area. A plateau may have one or more sides with steep slopes. The area under plateau cover in geomorphological map is 458 sq km.

Water Body:

It is an area of impounded water, areal in extent and often with a regulated flow of water. It includes man-made reservoirs/lakes/tank/canals, besides natural lakes, rivers/streams and creeks. The area under water body cover in geomorphological map is 32 sq km.

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