scholarly journals MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA

2017 ◽  
Vol 12 (2) ◽  
pp. 85 ◽  
Author(s):  
Gathot Winarso ◽  
Yenni Vetrita ◽  
Anang D. Purwanto ◽  
Nanin Anggraini ◽  
Soni Darmawan ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Vegetation Indices ◽  
Emission Trading ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Alos Palsar ◽  
Sar Data ◽  
Estimation Models

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan.  Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.

scholarly journals BIOMASS AND CARBON STOCK ESTIMATION USING IN-SITU OBSERVATIONS AND GIS IN GILGIT BALTISTAN, PAKISTAN

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 49-55
Author(s):  
E. Fatima ◽  
S. S. Ali
Keyword(s):  
Carbon Stock ◽  
Anthropogenic Activities ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Accurate Method ◽  
Carbon Accounting ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Inventory Data ◽  
Gilgit Baltistan

Abstract. Carbon dioxide (CO2) emission and other greenhouse gases are rising day by day due to anthropogenic activities which lead to global warming and cause natural disasters. Thus REDD+ comes up with an initiative to reduce emissions from deforestation through Carbon accounting, in which the under developing countries Measure, Report, and Verify (MRV) the sum of Above Ground Biomass (AGB)/carbon stored in a particular forest. Nonetheless, the major challenge for REDD+ is to find an accurate method for biomass estimation. The purpose of this study was to model and map the AGB and carbon stock of Gilgit-Baltistan, Pakistan. For this purpose, we linked Landsat 8 and forest inventory data to assess the potential of Vegetation Indices (Vis) derived AGB estimation. Inventory data consisted of the tree measurements from 480 plots that data was collected in the year (June–Oct) 2016 in a 72,971 km2 (28,174 sq mi) study area, in Gilgit-Baltistan. Out of these plots, 287 was used in Calibration and 191 is used for Validation. This paper provides a regression equation between the reflection values from the Landsat-8 satellite image and sample areas where terrestrial aboveground biomass (AGB) was calculated by direct measurement method. As a result of the calculations made, a positive linear correlation between AGB and NDVI was relatively high compared to other vegetation indices i.e 0.59 in the year 2016 or for the year 2013.

scholarly journals Contribution of some Acacia Species on Carbon Sequestration in Blue Nile State, Sudan

2021 ◽  
pp. 97-105
Keyword(s):  
Carbon Sequestration ◽  
Wood Density ◽  
Carbon Stock ◽  
Tree Height ◽  
Biomass Estimation ◽  
Expansion Factor ◽  
Wood Volume ◽  
Crown Diameter ◽  
Breast Height ◽  
Acacia Seyal

Background: The current challenge is to reduce the uncertainties in obtaining accurate and reliable data of carbon stock changes and emission factors essential for reporting national inventories. Improvements in above ground biomass estimation can also help account for changes in carbon stock in forest areas that may potentially participate in the Reducing emissions from deforestation and forest degradation and other initiatives. Current objectives for such estimates need a unified approach which can be measurable, reportable, and verifiable. This might result to a geographically referenced biomass density database for Sudanese forests that would reduce uncertainties in estimating forest aboveground biomass. The main objective: of this study is to assess potential of some selected forest variables for modeling carbon sequestration for Acacia seyal, vr. Seyal, Acacia seyal, vr. fistula, Acacia Senegal. The specific objectives include development of empirical allometric models for forest biomass estimation, estimation of carbon sequestration for these tree species, estimation of carbon sequestration per hectare and comparing the amount with that reported to the region. A total of 10 sample trees for biomass and carbon determination were selected for each of the three species from El Nour Natural Forest Reserve of the Blue Nile State, Sudan. Data of diameter at breast height, total tree height, tree crown diameter, crown height, and upper stem diameters were measured. Then sample trees were felled and sectioned to their components, and weighed. Subsamples were selected from each component for oven drying at 105 ˚C. Finally allometric models were developed and the aboveground dry weight (dwt) and carbon sequestered per hector were calculated. The results: presents biomass equations, biomass expansion factor and wood density that developed for the trees. In case of inventoried wood volume, corrections for biomass expansion factor and wood density value were done, and new values are suggested for use to convert wood volume to biomass estimates. The results also, indicate that diameter at breast height, crown diameter and tree height are good predictors for estimation of tree dwt and carbon stock. Conclusion: The developed allometric equations in this study gave better estimation of dwt than default value. The average carbon stock was found to be 22.57 t/ha.

scholarly journals A study on deforestation in hilly areas of Haryana using Remote Sensing and GIS technique

2020 ◽  
pp. 69-77
Author(s):  
Anju Jangra ◽  
Anurag Airon ◽  
Ram Niwas
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Special Focus ◽  
Landsat 8 ◽  
Deforestation Rate ◽  
Gis Technique ◽  
Prime Concern

Forest is an essential part or backbone of the earth ecological system. In a country like India, the people and the economy of nation is mainly relies on the diversity of natural resources. In today's world degradation of forest resources is a prime concern for many of the scientists and environmentalists because the canvas had been transformed from last few decades to cultivated and non-cultivated land. In India, Haryana state has lowest forest cover i.e. 3.59% followed by Punjab 3.65%. Over the several decades, the advancement of Remote Sensing and Geographical Information System (GIS) technique has emerged as an efficient tool to monitor and analyse deforestation rate in hilly areaor over a variety of location. Remote sensing based vegetation indices show better sensitivity than individual band reflectance and hence are more preferred for assessment and monitoring of tress. The aim of the present study was to analyse the deforestation in hilly areas in Haryana State (India) by remote sensing data with a special focus on Panchkula and Yamunanagar. The information was collected through the LANDSAT 8 satellite of NASA. The result revealed that the deforestation rate is high in Hilly areas of Haryana. The study shows that the forest cover in hilly areas of Haryana in 2013 was 50,879.07 hectares and in 2019 it was 44,445.51 hectares of land. Thereby decrease in forest cover of 6,433.56 hectares had been observed in the study period of 2013-2019 i.e. 6 years. Spatial variations in deforestation were also mapped in GIS for the hilly areas in Panchkula and Yamunanagar districts of Haryana.  

Application of optical and radar data for lineaments mapping in Kerdous inlier of the Anti Atlas belt, Morocco

2021 ◽  
Author(s):  
Amine Jellouli ◽  
Abderrazak El Harti ◽  
Zakaria Adiri ◽  
Mohcine Chakouri ◽  
Jaouad El Hachimi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Radar Data ◽  
Ore Deposits ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Alos Palsar ◽  
Geological Map ◽  
Color Composite ◽  
Manual Extraction

<p>Lineament mapping is an important step for lithological and hydrothermal alterations mapping. It is considered as an efficient research task which can be a part of structural investigation and mineral ore deposits identification. The availability of optical as well as radar remote sensing data, such as Landsat 8 OLI, Terra ASTER and ALOS PALSAR data, allows lineaments mapping at regional and national scale. The accuracy of the obtained results depends strongly on the spatial and spectral resolution of the data. The aim of this study was to compare Landsat 8 OLI, Terra ASTER, and radar ALOS PALSAR satellite data for automatic and manual lineaments extraction. The module Line of PCI Geomatica software was applied on PC1 OLI, PC3 ASTER and HH and HV polarization images to automatically extract geological lineaments. However, the manual extraction was achieved using the RGB color composite of the directional filtered images N - S (0°), NE - SW (45°) and E - W (90°) of the OLI panchromatic band 8. The obtained lineaments from automatic and manual extraction were compared against the faults and photo-geological lineaments digitized from the existing geological map of the study area. The extracted lineaments from PC1 OLI and ALOS PALSAR polarizations images showed the best correlation with faults and photo-geological lineaments. The results indicate that the lineaments extracted from HH and HV polarizations of ALOS PALSAR radar data used in this study, with 1499 and 1507 extracted lineaments, were more efficient for structural lineament mapping, as well as the PC1 OLI image with 1057 lineaments.</p><p><strong>Keywords</strong> Remote Sensing . OLI. ALOS PALSAR . ASTER . Kerdous Inlier . Anti Atlas</p>

scholarly journals Mapping Paleohydrologic Features in the Arid Areas of Saudi Arabia Using Remote-Sensing Data

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 417 ◽  
Author(s):  
Mohamed Abdelkareem ◽  
Fathy Abdalla ◽  
Samar Y. Mohamed ◽  
Farouk El-Baz
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Volcanic Activity ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Alos Palsar ◽  
Sensing Data ◽  
The Past ◽  
Aeolian Deposits

At present, the Arabian Peninsula is one of the driest regions on Earth; however, this area experienced heavy rainfall in the past thousand years. During this period, catchments received substantial amounts of surface water and sustained vast networks of streams and paleolakes, which are currently inactive. The Advanced Land Observing Satellite (ALOS) Phased Array Type L-band Synthetic Aperture Radar (PALSAR) data reveal paleohydrologic features buried under shallow aeolian deposits in many areas of the ad-Dawasir, Sahba, Rimah/Batin, and as-Sirhan wadis. Optical remote-sensing data support that the middle of the trans-peninsula Wadi Rimah/Batin, which extends for ~1200 km from the Arabian Shield to Kuwait and covers ~200,000 km2, is dammed by linear sand dunes formed by changes in climate conditions. Integrating Landsat 8 Operational Land Imager (OLI), Geo-Eye, Shuttle Radar Topography Mission (SRTM) digital elevation model, and ALOS/PALSAR data allowed for the characterization of paleodrainage reversals and diversions shaped by structural and volcanic activity. Evidence of streams abruptly shifting from one catchment to another is preserved in Wadi ad-Dawasir along the fault trace. Volcanic activity in the past few thousand years in northern Saudi Arabia has also changed the slope of the land and reversed drainage systems. Relics of earlier drainage directions are well maintained as paleoslopes and wide upstream patterns. This study found that paleohydrologic activity in Saudi Arabia is impacted by changes in climate and by structural and volcanic activity, resulting in changes to stream direction and activity. Overall, the integration of radar and optical remote-sensing data is significant for deciphering past hydrologic activity and for predicting potential water resource areas.

scholarly journals ESTIMATING ABOVEGROUND BIOMASS OF BAMBOO AND MIXED BAMBOO FOREST IN THUA THIEN-HUE PROVINCE, VIET NAM USING PALSAR-2 AND LANDSAT OLI DATA

2019 ◽  
Vol XLII-4/W20 ◽  
pp. 11-19
Author(s):  
T. T. Cat Tuong ◽  
H. Tani ◽  
X. F. Wang ◽  
V.-M. Pham
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Landsat Data ◽  
Bamboo Forest ◽  
Landsat 8 Oli ◽  
L Band

Abstract. In this study, above-ground biomass (AGB) performance was evaluated by PALSAR-2 L-band and Landsat data for bamboo and mixed bamboo forest. The linear regression model was chosen and validated for forest biomass estimation in A Luoi district, Thua Thien Hue province, Vietnam. A Landsat 8 OLI image and a dual-polarized ALOS/PALSAR-2 L-band (HH, HV polarizations) were used. In addition, 11 diferrent vegetation indices were extracted to test the performance of Landsat data in estimating forest AGB Total of 54 plots were collected in the bamboo and mixed bamboo forest in 2016. The linear regression is used to evaluate the sensitivity of biomass to the obtained parameters, including radar polarization, optical properties, and some vegetation indices which are extracted from Landsat data. The best-fit linear regression is selected by using the Bayesian Model Average for biomass estimation. Leave-one-out cross-validation (LOOCV) was employed to test the robustness of the model through the coefficient of determination (R squared – R2) and Root Mean Squared Error (RMSE). The results show that Landsat 8 OLI data has a slightly better potential for biomass estimation than PALSAR-2 in the bamboo and mixed bamboo forest. Besides, the combination of PALSAR-2 and Landsat 8 OLI data also has a no significant improvement (R2 of 0.60) over the performance of models using only SAR (R2 of 0.49) and only Landsat data (R2 of 0.58–0.59). The univariate model was selected to estimate AGB in the bamboo and mixed bamboo forest. The model showed good accuracy with an R2 of 0.59 and an RMSE of 29.66 tons ha−1. The comparison between two approaches using the entire dataset and LOOCV demonstrates no significant difference in R (0.59 and 0.56) and RMSE (29.66 and 30.06 tons ha−1). This study performs the utilization of remote sensing data for biomass estimation in bamboo and mixed bamboo forest, which is a lack of up-to-date information in forest inventory. This study highlights the utilization of the linear regression model for estimating AGB of the bamboo forest with a limited number of field survey samples. However, future research should include a comparison with non-linear and non-parametric models.

scholarly journals DETECTING THE LAVA FLOW DEPOSITS FROM 2018 ANAK KRAKATAU ERUPTION USING DATA FUSION LANDSAT-8 OPTIC AND SENTINEL-1 SAR

2019 ◽  
Vol 15 (2) ◽  
pp. 157
Author(s):  
NFn Suwarsono ◽  
Indah Prasasti ◽  
Jalu Tejo Nugroho ◽  
Jansen Sitorus ◽  
Djoko Triyono
Keyword(s):  
Data Fusion ◽  
Lava Flow ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
Landsat 8 ◽  
Major Disaster ◽  
Sar Data ◽  
Using Data ◽  
Display Quality ◽  
Krakatau Volcano

The increasing volcanic activity of Anak Krakatau volcano has raised concerns about a major disaster in the area around the Sunda Strait. The objective of the research is to fuse Landsat-8 OLI (Operational Land Imager) and Sentinel-1 TOPS (Terrain Observation with Progressive Scans), an integration of SAR and optic remote sensing data, in observing the lava flow deposits resulted from Anak Krakatau eruption during the middle 2018 eruption. RGBI and the Brovey transformation were conducted to merge (fuse) the optical and SAR data.  The results showed that optical and SAR data fusion sharpened the appearance of volcano morphology and lava flow deposits. The regions are often constrained by cloud cover and volcanic ash, which occurs at the time of the volcanic eruption.  The RGBI-VV and Brovey RGB-VV methods provide better display quality results in revealing the morphology of volcanic cone and lava deposits. The entire slopes of Anak Krakatau Volcano, with a radius of about 1 km from the crater is an area prone to incandescent lava and pyroclastic falls. The direction of the lava flow has the potential to spread in all directions. The fusion method of optical Landsat-8 and Sentinel-1 SAR data can be used continuously in monitoring the activity of Anak Krakatau volcano and other volcanoes in Indonesia both in cloudy and clear weather conditions.

scholarly journals Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops

2020 ◽  
Vol 12 (17) ◽  
pp. 2683
Author(s):  
David Alejandro Jimenez-Sierra ◽  
Hernán Darío Benítez-Restrepo ◽  
Hernán Darío Vargas-Cardona ◽  
Jocelyn Chanussot
Keyword(s):  
Data Fusion ◽  
Change Detection ◽  
Mean Squared Error ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Biomass Estimation ◽  
Fusion Algorithm ◽  
Simple Task ◽  
Recent Approach ◽  
Complementary Nature

The complementary nature of different modalities and multiple bands used in remote sensing data is helpful for tasks such as change detection and the prediction of agricultural variables. Nonetheless, correctly processing a multi-modal dataset is not a simple task, owing to the presence of different data resolutions and formats. In the past few years, graph-based methods have proven to be a useful tool in capturing inherent data similarity, in spite of different data formats, and preserving relevant topological and geometric information. In this paper, we propose a graph-based data fusion algorithm for remotely sensed images applied to (i) data-driven semi-unsupervised change detection and (ii) biomass estimation in rice crops. In order to detect the change, we evaluated the performance of four competing algorithms on fourteen datasets. To estimate biomass in rice crops, we compared our proposal in terms of root mean squared error (RMSE) concerning a recent approach based on vegetation indices as features. The results confirm that the proposed graph-based data fusion algorithm outperforms state-of-the-art methods for change detection and biomass estimation in rice crops.

STAND AGE MODEL FOR MAPPING SPATIAL DISTRIBUTION OF RUBBER TREE USING REMOTELY SENSED DATA IN KEDAH, MALAYSIA

2016 ◽  
Vol 78 (5) ◽  
Author(s):  
Iqbal Putut Ash Shidiq ◽  
Mohd Hasmadi Ismail
Keyword(s):  
Spatial Distribution ◽  
Vegetation Indices ◽  
Rubber Tree ◽  
Remote Sensing Data ◽  
Stand Age ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remotely Sensed Data ◽  
Age Model ◽  
The Relationship

This study attempt to develop stand age model of rubber tree by using remote sensing data. Rubber tree is one of the important biomass that has been considered as the essential part in global warming reduction plan due to its beneficial carbon sequestration capability. The spatial distribution of rubber tree based on different age was most highlighted as the focus of this study. Felda Lubuk Merbau in state of Kedah has been selected as a study area and Landsat 8 OLI-TIRS data was utilized to map rubber tree and differentiate them based on age group. The relationship between vegetation indices namely NDVI, SAVI and EVI to different age stages of rubber tree were discussed.

scholarly journals IMPACT ASSESSMENT OF A MINE SUBSIDENCE ON NATIVE VEGETATION OF SOUTH EASTERN COALFIELDS, INDIA

2018 ◽  
Vol XLII-5 ◽  
pp. 487-490
Author(s):  
A. K. Vishwakarma ◽  
A. K. Agnihotri ◽  
R. Rai ◽  
B. K. Shrivastva ◽  
S. Mishra
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Adverse Impact ◽  
Native Vegetation ◽  
Landsat 8 ◽  
Vegetation Growth ◽  
South Eastern ◽  
Wide Range

<p><strong>Abstract.</strong> This study aims to evaluate the effect of underground coal mining subsidence on the growth of native vegetation. For this study, an underground coal mine of South Eastern Coalfields Limited (SECL), India was selected. Changes in vegetation indices were analyzed using three remote sensing data of the previous five years. Three period’s Landsat 8 OLI resolution image data were used to calculate Normalized Difference Vegetation Index (NDVI) of the years 2014, 2016 and 2018 in QGIS environment. The study showed that the local grassland and forest were affected by the mining exploitation and subsidence but those effects were not significant to have an adverse impact on the same. The short-term mining was having an impact on the vegetation growth but the effects gradually disappeared with the gradual stabilization of the subsided land and in absence of human interference, vegetation recovered well. In long-term, subsidence was not having a major impact on the vegetation growth. Thus, coal resources exploitation and subsidence of the said mine of SECL did not bring out an adverse impact on a wide range of forest and grassland ecosystems, and these ecosystems could carry the partial destruction and ultimately stabilized ecosystems by self-repair.</p>

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