A METHOD FOR MAPPING PLANT AND SOIL CARBON IN PARTS OF SOUTHWEST NIGERIA (original) (raw)

Estimation of Aboveground Carbon Stock Using Sar SENTINEL-1 Imagery in Samarinda City

International Journal of Remote Sensing and Earth Sciences (IJReSES), 2021

Estimation of aboveground carbon stock on stands vegetation, especially in green open space, has become an urgent issue in the effort to calculate, monitor, manage, and evaluate carbon stocks, especially in a massive urban area such as Samarinda City, Kalimantan Timur Province, Indonesia. The use of Sentinel-1 imagery was maximised to accommodate the weaknesses in its optical imagery, and combined with its ability to produce cloud-free imagery and minimal atmospheric influence. The study aims to test the accuracy of the estimated model of above-ground carbon stocks, to ascertain the total carbon stock, and to map the spatial distribution of carbon stocks on stands vegetation in Samarinda City. The methods used included empirical modelling of carbon stocks and statistical analysis comparing backscatter values and actual carbon stocks in the field using VV and VH polarisation. Model accuracy tests were performed using the standard error of estimate in independent accuracy test samples...

Improvement of Spatial Estimation for Soil Organic Carbon Stocks in Yuksekova Plain using Sentinel 2 imagery and Gradient Descent Boosted Regression Tree

Research Square (Research Square), 2022

Carbon sequestration in earth surface is higher than the atmosphere, and the amount of carbon stored in wetlands is much greater than all other land surfaces. The purpose of this study was to estimate soil organic carbon stocks (SOCS) and investigate spatial distribution pattern of Yuksekova wetlands and surrounding lands in Hakkari province of Turkey using machine learning and remote sensing data. Total carbon stock in study area was calculated at 10-cm vertical resolution in 0 to 30 cm depth for 50 sampling locations. Vegetation, soil and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Signi cant correlations were obtained between the indices and SOCS, thus, the remote sensing indices were used as covariates in Multi-Layer Perceptron Neural Network (MLP) and Gradient Descent Boosted Regression Tree (GBDT) machine learning models. Mean Absolute Error, Root Mean Square Error and Mean Absolute Percentage Error were 3.94 (Mg C ha − 1), 6.64 (Mg C ha − 1) and 9.97%, respectively. The Simple Ratio Clay Index (SRCI), which represents the soil texture, was the most important factor in the SOCS estimation variance. In addition, the relationship between SRCI and Topsoil Grain Size Index revealed that topsoil clay content is a highly important parameter in spatial variation of SOCS. The spatial SOCS values obtained using the GBDT model and the mean SOCS values of the CORINE land lover classes were signi cantly different. The wetlands had the highest SOCS (61.46 Mg C ha − 1), followed by the lands principally occupied by natural vegetation and used as rangelands around the wetland (50.22 Mg C ha − 1). Environmental conditions have signi cant effect on SOCS which has high spatial variation in the study area. Reliable spatial SOCS information was obtained with the combination of Sentinel-2 guided multi-index remote sensing modeling strategy and the GBDT model. Therefore, the spatial estimation of SOCS can be successfully carried out with up-to-date machine learning algorithms only using remote sensing data. Reliable estimation of SOCS in wetlands and surrounding lands can help understand policy and decision makers the importance of wetlands in mitigating the negative impacts of global warming .

Multi-sensor approach integrating optical and multi-frequency synthetic aperture radar for carbon stock estimation over a tropical deciduous forest in India

Carbon Management, 2019

An optimal model was developed for accounting forest carbon stock from synergistic use of optical data from Landsat TM and synthetic aperture radar (SAR) data from COSMO-Skymed (X-band), Radarsat-2 (C-band) and ALOS PALSAR (L-band) sensors over a tropical deciduous heterogeneous forest of India. The best-fit integrated multiple linear regression model had a model accuracy of 83%, r 2 ¼ 0.96, root mean square error ¼ 10.02 Mg/ha and Willmott's index of agreement of 0.98. The model further validated using chi-squared and t-test. Results of models for calculating the aboveground biomass (AGB) were converted to C and CO 2 using conversion factors. Average AGB, C and CO 2 were 70.5, 35.26 and 130.89 Mg/ha, respectively. The synergistic use of optical and multi-frequency SAR data enhanced the AGB saturation threshold to about 150 Mg/ha for tropical deciduous mixed forests. Hence, the synergistic use of this data is suggested for large-scale AGB and C estimations for tropical forests. Optical remote sensing sensors are extensively used due to greater data availability despite their poor sensitivity toward forest parameters. In contrast, SAR signals are highly sensitive toward forest biophysical and structural parameters, providing a better alternative. This unique integrated approach provides valuable information regarding the spatial distribution and quantification of forest biomass and carbon.

Remote Sensing Based Estimation of Potential Terrestrial Carbon Stocks in West Africa

Climate change, initiated by increasing greenhouse gas emissions leads to serious ecological and economic problems especially in developing countries. The potential carbon sinks are analyzed by a combination of different remote sensing based products for three countries (Ghana, Togo, Burkina Faso). In order to derive these information, the following analysis need to be done: Firstly, the net primary productivity was estimated on a regional scale based on MODIS 250 m time series. Secondly, the actual land cover was classified with medium spatial resolution based on multi scale analysis with in situ, Landsat and MODIS data. Thirdly, the potential vegetation was modeled with abiotic factors, whereas land cover of protected areas was used as training data. Based on a combination of these datasets a balance of potential carbon sinks could be estimated over a time period of 100 years. Due to decreasing soil fertility, the potential of emission trading could be an alternative source of income in parts of this region.

Geospatial temporal assessment and monitoring of land area carbon: Evidence from Adekunle Ajasin University, Akungba Akoko, Ondo State, Nigeria

This study was conducted to monitor some area carbon in Adekunle Ajasin University Akungba (AAUA) Campus to encourage tree planting alongside her edifice and discourage high rate of deforestation. This study relies solely on the use of remote sensing data to estimate carbon sequestration between year 2014 and 2020. The objective of this study is to estimate the carbon sequestration capacity in the study area using remote sensing (RS) and geographical information system (GIS) with a view to enkindling the importance of trees or forest within the University campus. Consequently, Landsat 8 TM Satellite images of year 2014 and 2020 of the study area were obtained from United States Geological Survey (USGS). The spectral vegetation index (normalised difference vegetation index) was estimate using spectral bands 4 and 5. Ten areas were chosen for this study and their coordinates obtained using GPS receiver when visited. These areas were not developed in 2014 and not fully developed as at ...

Assessment of Carbon Stocks in the Topsoil Using Random Forest and Remote Sensing Images

Wetland soils are able to exhibit both consumption and production of greenhouse gases, and they play an important role in the regulation of the global carbon (C) cycle. Still, it is challenging to accurately evaluate the actual amount of C stored in wetlands. The incorporation of remote sensing data into digital soil models has great potential to assess C stocks in wetland soils. Our objectives were (i) to develop C stock prediction models utilizing remote sensing images and environmental ancillary data, (ii) to identify the prime environmental predictor variables that explain the spatial distribution of soil C, and (iii) to assess the amount of C stored in the top 20-cm soils of a prominent nutrient-enriched wetland. We collected a total of 108 soil cores at two soil depths (0-10 cm and 10-20 cm) in the Water Conservation Area 2A, FL. We developed random forest models to predict soil C stocks using field observation data, environmental ancillary data, and spectral data derived from remote sensing images, including Satellite Pour l'Observation de la Terre (spatial resolution: 10 m), Landsat Enhanced Thematic Mapper Plus (30 m), and Moderate Resolution Imaging Spectroradiometer (250 m). The random forest models showed high performance to predict C stocks, and variable importance revealed that hydrology was the major environmental factor explaining the spatial distribution of soil C stocks in Water Conservation Area 2A. Our results showed that this area stores about 4.2 Tg (4.2 Mt) of C in the top 20-cm soils.

A Review of Terrestrial Carbon Assessment Methods Using Geo-Spatial Technologies with Emphasis on Arid Lands

Remote Sensing

Geo-spatial technologies (i.e., remote sensing (RS) and Geographic Information Systems (GIS)) offer the means to enable a rapid assessment of terrestrial carbon stock (CS) over large areas. The utilization of an integrated RS-GIS approach for above ground biomass (AGB) estimation and precision carbon management is a timely and cost-effective solution for implementing appropriate management strategies at a localized and regional scale. The current study reviews various RS-related techniques used in the CS assessment, with emphasis on arid lands, and provides insight into the associated challenges, opportunities and future trends. The study examines the traditional methods and highlights their limitations. It explores recent and developing techniques, and identifies the most significant RS variables in depicting biophysical predictors. It further demonstrates the usefulness of geo-spatial technologies for assessing terrestrial CS, especially in arid lands. RS of vegetation in these ec...

Estimation of Biomass Carbon Stocks over Peat Swamp Forests using Multi-Temporal and Multi-Polratizations SAR Data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015

The capability of L-band radar backscatter to penetrate through the forest canopy is useful for mapping the forest structure, including above ground biomass (AGB) estimation. Recent studies confirmed that the empirical AGB models generated from the L-band radar backscatter can provide favourable estimation results, especially if the data has dual-polarization configuration. Using dual polarimetry SAR data the backscatter signal is more sensitive to forest biomass and forest structure because of tree trunk scattering, thus showing better discriminations of different forest successional stages. These SAR approaches, however, need to be further studied for the application in tropical peatlands ecosystem We aims at estimating forest carbon stocks and stand biophysical properties using combination of multi-temporal and multi-polarizations (quad-polarimetric) L-band SAR data and focuses on tropical peat swamp forest over Kampar Peninsula at Riau Province, Sumatra, Indonesia which is one of the most peat abundant region in the country. Applying radar backscattering (Sigma nought) to model the biomass we found that co-polarizations (HH and VV) band are more sensitive than cross-polarization channels (HV and VH). Individual HH polarization channel from April 2010 explained > 86% of AGB. Whereas VV polarization showed strong correlation coefficients with LAI, tree height, tree diameter and basal area. Surprisingly, polarimetric anisotropy feature from April 2007 SAR data show relatively high correlations with almost all forest biophysical parameters. Polarimetric anisotropy, which explains the ratio between the second and the first dominant scattering mechanism from a target has reduced at some extent the randomness of scattering mechanism, thus improve the predictability of this particular feature in estimating the forest properties. These results may be influenced by local seasonal variations of the forest as well as moisture, but available quad-pol SAR data were unable to show these patterns, since all the SAR data were acquired during the rainy season. The results of multi-regression analysis in predicting above ground biomass shows that ALOS PALSAR data acquired in 2010 has outperformed other time series data. This is probably due to the fact that land cover change in the area from 2007 -2009 was highly dynamic, converting natural forests into rubber and Acacia plantations, thus SAR data of 2010 which was acquired in between of two field campaigns has provided significant results (F = 40.7, P < 0.005). In general, we found that polarimetric features have improved the models performance in estimating AGB. Surprising results come from single HH polarization band from April 2010 that has a strong correlation with AGB (r = 0.863). Also, HH polarization band of 2009 SAR image resulted in a moderate correlation with AGB (r = 0.440).

Utilization of Sentinel-2 Imagery in Mapping the Distribution and Estimation of Mangroves' Carbon Stocks in Bengkulu City

Geosfera Indonesia

The mangroves' aboveground biomass significantly contributes to the global carbon cycle or economic and ecological values. This makes knowledge about the spatial extent of the mangroves indispensable for policymakers. The sequence of mangroves’ condition range also requires remote sensing data to update the geographical information and synthesize carbon stock in Bengkulu. Therefore, this study aims to create a spatial distrribution of mangroves and evaluate their carbon stock in Bengkulu City using Sentinel-2 imagery. The semi-empirical method uses Sentinel-2 imagery through NDVI to appraise and picture the mangroves' aboveground carbon stock. An allometric equation was used to compute the mangroves' aboveground carbon stock from field measurements. Non-linear regression was used to establish a connection between the NDVI calculated from the Sentinel-2 imagery and the mangroves' aboveground biomass measured in the field, which was subsequently used for aboveground ca...

Mapping and monitoring carbon stocks with satellite observations: a comparison of methods

Carbon Balance and Management, 2009

Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions, and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient. Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon monitoring and markets.