Estimation and mapping of above ground biomass and carbon of Bwindi impenetrable National Park using ALOS PALSAR data

Otukei, John Richard; Emanuel, Male

doi:10.4314/sajg.v4i1.1

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…Four bands (B1, B2, B3, and Infrared) Sousa et al (2015), Sousa et al (2017) Where; MSAVI: Modified Soil Adjusted Vegetation Index, NDVI: Vegetation Index, SR: Simple Ration, NDMI: Normalized Difference Moisture Index, RENDV: Red Edge Normalized Difference Index, VI: Vegetation Index, DVI: Difference Vegetation Index, ExGI: Excess Green Index, GLI: Green Leaf Index, EVI: Enhanced Vegetation Index, SAVI: Soil Adjusted Vegetation Index, NDGI: Normalized Difference Green Index, ARVI: Atmospheric Resistance Vegetation Index and SRRE: Red Edge Sample Ratio. Otukei et al (2015), Gizachew et al (2016), Naesset et al (2016), and assessed the use of RS for biomass estimation in the region of east Africa. The contribution of remotely sensed (RS) data to increasing the accuracy of AGB estimation in the Afromontane forests of south-central Ethiopia was evaluated by .…”

Section: Biomass Estimationmentioning

confidence: 99%

Relevance of Remote Sensing and its Applications in Forestry. A Critical Review

Getie,

Alemayehu,

Mekoya

2024

East Afr. j. for. agrofor.

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For several nations who are developing, forests play a crucial role in rural life. Due to the incredible challenges associated with staff, logistics, and chronological consistency of field-based surveys for forest management, a variety of sources of data obtained by airborne, space-borne, and terrestrial remote sensing sensors are now essential sources of knowledge for studies on the spatiotemporal patterns of forests. Most recently, understanding of forests and their conservation has been derived primarily from satellite imagery. The process of organizing and carrying out procedures for the management and use of forests can be done with the assistance of remote sensing in order to achieve economic, social, cultural, and environmental goals. Satellite remote sensing has been providing ever-more-advanced knowledge about woodland structure, management, monitoring, and oversight whenever the first civilian earth-observing program was launched. This article reviewed the application of remote sensing on forestry. Data were gathered from published research papers, books, internet resources, and expert observation. Remote sensing’s synoptic view, availability in a rage of spatial-temporal scales, high degree of homogeneity, inexpensiveness as well as the increasing trend in availability make it special in forest science. As observed from the review, remote sensing technology is critical in forest management. It helps to provide up to date information on forest cover change, forest fire, forest disturbance, forest wildlife management, forest biomass and others. Remote sensing is vital in providing scientific information in forest resources monitoring and management

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Section: Biomass Estimationmentioning

confidence: 99%

Relevance of Remote Sensing and its Applications in Forestry. A Critical Review

Getie,

Alemayehu,

Mekoya

2024

East Afr. j. for. agrofor.

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“…This makes the mangrove ecosystem play an important role in the ecosystem [3]. One of the important variables in climate change or environmental assessment is biomass [4]. Indonesia, which is located in the tropics, needs more effort in measuring the amount of carbon stored in aboveground biomass because it is always filled with uncertainty due to its sizeable area [5].…”

Section: Introductionmentioning

confidence: 99%

Above Ground Carbon Estimation Using Sentinel-1B RADAR Satellite Imagery

Munthe

Sulistiyono

2023

J. Phys.: Conf. Ser.

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Mangroves are an important forest ecosystem in Indonesia because mangroves are known to have the ability to store high carbon. The use of remote sensing technology can be used to estimate the distribution of carbon content. The advantage of using radar satellite imagery such as Sentinel-1b is its ability to penetrate clouds. Satellite images produced from radar satellites are cloud-free satellite images. The purpose of this study was to obtain information on the spatial distribution of above-ground carbon (AGC) using the Sentinel-1b radar satellite imagery in the Karang Gading dan Langkat Timur Laut Wildlife Reserve. Regression is used to estimate the distribution of AGC based on the backscattering values of VH, VV, VV/VH, VV-VH, and VV+VH. The results showed that the carbon distribution in the best model was 45.02 tons ha-1 to 222.16 tons ha-1. The quadratic regression model with VV-VH backscatter is the best model for estimating AGC with a value of R2 = 0.374, and the value of Root Mean Square Error (RMSE) value of 19.083 ha/ton.

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“…Some studies (e.g., [16,19,40]) evaluated the use of RS data for biomass estimation in small study areas in the region of east Africa. However, to the best of our knowledge, except some efforts related to the use of Landsat images for land cover classification and mapping, data from the mentioned satellite missions subject to analysis in the current study have never been used to assess AGB of the dry Afromontane forests in Ethiopia.…”

Section: Introductionmentioning

confidence: 99%

Use of Remotely Sensed Data to Enhance Estimation of Aboveground Biomass for the Dry Afromontane Forest in South-Central Ethiopia

et al. 2020

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Periodic assessment of forest aboveground biomass (AGB) is essential to regulate the impacts of the changing climate. However, AGB estimation using field-based sample survey (FBSS) has limited precision due to cost and accessibility constraints. Fortunately, remote sensing technologies assist to improve AGB estimation precisions. Thus, this study assessed the role of remotely sensed (RS) data in improving the precision of AGB estimation in an Afromontane forest in south-central Ethiopia. The research objectives were to identify RS variables that are useful for estimating AGB and evaluate the extent of improvement in the precision of the remote sensing-assisted AGB estimates beyond the precision of a pure FBSS. Reference AGB data for model calibration and estimation were collected from 111 systematically distributed circular sample plots (SPs) of 1000 m2 area. Independent variables were derived from Landsat-8, Sentinel-2 and PlanetScope images acquired in January 2019. The area-weighted mean and standard deviation of the spectral reflectance, spectral index and texture (only for PlanetScope) variables were extracted for each SP. A maximum of two independent variables from each image type was fitted to a generalized linear model for AGB estimation using model-assisted estimators. The results of this study revealed that the Landsat-8 model with the predictor variable of shortwave infrared band reflectance and the PlanetScope model with the predictor variable of green band reflectance had estimation efficiency of 1.40 and 1.37, respectively. Similarly, the Sentinel-2 model, which had predictor variables of shortwave infrared reflectance and standard deviation of green leaf index, improved AGB estimation with the relative efficiency of 1.68. Utilizing freely available Sentinel-2 data seems to enhance the AGB estimation efficiency and reduce cost and extensive fieldwork in inaccessible areas.

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Estimation and mapping of above ground biomass and carbon of Bwindi impenetrable National Park using ALOS PALSAR data

Abstract: Abstract:Biomass

Cited by 6 publications

References 21 publications

Relevance of Remote Sensing and its Applications in Forestry. A Critical Review

Relevance of Remote Sensing and its Applications in Forestry. A Critical Review

Above Ground Carbon Estimation Using Sentinel-1B RADAR Satellite Imagery

Use of Remotely Sensed Data to Enhance Estimation of Aboveground Biomass for the Dry Afromontane Forest in South-Central Ethiopia

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