Natural Forest Biomass Estimation Based on Plantation Information Using PALSAR Data

Avtar, Ram; Suzuki, Rikie; Sawada, Haruo

doi:10.1371/journal.pone.0086121

Cited by 52 publications

(32 citation statements)

References 49 publications

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“…Most of the triple-rice cropping area comes under the full-dike system whereas, the double-rice cropping area comes under the semi-dike system. The most common method to assess the accuracy of classification is the confusion matrix [32,33]. The error matrix shows a tabulated view of map accuracy, which allows the calculation of specific measures such as overall accuracy and user's and producer's accuracies [87].…”

Section: Delineation Of the Rice Cropping Areamentioning

confidence: 99%

Monitoring and Mapping of Rice Cropping Pattern in Flooding Area in the Vietnamese Mekong Delta Using Sentinel-1A Data: A Case of An Giang Province

Minh

Avtar

Mohan

et al. 2019

IJGI

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Cropping intensity is one of the most important decisions made independently by farmers in Vietnam. It is a crucial variable of various economic and process-based models. Rice is grown under irrigated triple- and double-rice cropping systems and a rainfed single-rice cropping system in the Vietnamese Mekong Delta (VMD). These rice cropping systems are adopted according to the geographical location and water infrastructure. However, little work has been done to map triple-cropping of rice using Sentinel-1 along with the effects of water infrastructure on the rice cropping intensity decision. This study is focused on monitoring rice cropping patterns in the An Giang province of the VMD from March 2017 to March 2018. The fieldwork was carried out on the dates close to the Sentinel-1A acquisition. The results of dual-polarized (VV and VH) Sentinel-1A data show a strong correlation with the spatial patterns of various rice growth stages and their association with the water infrastructure. The VH backscatter (σ°) is strongly correlated with the three rice growth stages, especially the reproductive stage when the backscatter is less affected by soil moisture and water in the rice fields. In all three cropping patterns, σ°VV and σ°VH show the highest value in the maturity stage, often appearing 10 to 12 days before the harvesting of the rice. A rice cropping pattern map was generated using the Support Vector Machine (SVM) classification of Sentinel-1A data. The overall accuracy of the classification was 80.7% with a 0.78 Kappa coefficient. Therefore, Sentinel-1A can be used to understand rice phenological changes as well as rice cropping systems using radar backscattering.

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Section: Delineation Of the Rice Cropping Areamentioning

confidence: 99%

Monitoring and Mapping of Rice Cropping Pattern in Flooding Area in the Vietnamese Mekong Delta Using Sentinel-1A Data: A Case of An Giang Province

Minh

Avtar

Mohan

et al. 2019

IJGI

Self Cite

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“…In situ AGB was estimated from the field measurements. As mentioned above, allometric equations have been developed at various spatial scales (e.g., global and regional), but it has been argued that using region-specific allometric equations produces better results than using a general, global allometric equation [36]. In this study, we used one region-and species-specific allometric equation developed for our particular study area by UN-REDD (The United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation) [62].…”

Section: In Situ Agb Estimationmentioning

confidence: 99%

“…Selection of a suitable allometric equation is an important step in estimating AGB [2,35]. Allometric equations have been developed for different spatial domains, and it has been argued that using region-specific allometric equations produces better results than using a generalized global allometric equation [36]. Application of allometric equations is often highly uncertain and site dependent [37]; therefore, we used regional-and species-based allometric equations pertinent to our particular study area rather than commonly used, generic allometric equations so that our estimations of biomass might concur as closely as possible with available field-based biomass measurements.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Inclusion of Small Regenerating Trees to Improve Above-Ground Forest Biomass Estimation Using Geospatial Data

Paull

Griffin

2018

Remote Sensing

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Research on the contribution of understory components to the total above ground biomass (AGB) has to date received very little attention because most prior biomass estimation studies have ignored small regenerating trees beneath the main canopy with the assumption that their contribution to biomass is generally negligible. Only a few biomass studies have emphasized a considerable contribution to biomass of understory components in forest ecosystems. However, this study of native, tropical, deciduous forest biomass in the Central Highlands of Vietnam was able to explore the contribution of small regenerating trees to total biomass by exploiting a large field inventory of hundreds to thousands of individually-counted small regenerating trees per hectare. Thus, this study investigated the influence of small regenerating tree biomass on models of the relationship between total AGB and remote sensing data. These analyses were trained with and without topographic variables derived from ASTER-GDEM. Our results demonstrate that the inclusion of small regenerating understory trees (R2 = 0.42, NRMSE or %RMSE = 30.5%) provides a quantifiable improvement in total estimated AGB compared to using only large woody canopy trees (R2 = 0.21, NRMSE or %RMSE = 36.6%) when correlating field-based biomass measurements with optical image-derived variables. All analyses show that the inclusion of terrain factors made an important contribution to biomass modeling. This study suggests that for young, open forests where there are many small regenerating trees, the contribution of understory biomass should be taken into consideration to improve total AGB estimation.

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“…Remote sensing is being used either in the estimation of biomass potential, availability and feasibility for production or the site selection of the power plants and the biomass plantation, nearest to the grid line and biomass transportation cost on the basis of the quantity of biomass and transportation distance [30]. The forest residual biomass is a source of renewable energy, which includes branches, and un-merchantable stem tops that are commercially not suitable for timber exploitation [56]. This biomass can be used as a source of energy in heating applications (fuel for domestic or industrial stoves and boilers) and also for the electricity generation.…”

Section: Role Of Remote Sensing and Gis In Exploring Renewable Energymentioning

confidence: 99%

Exploring Renewable Energy Resources Using Remote Sensing and GIS—A Review

et al. 2019

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Renewable energy has received noteworthy attention during the last few decades. This is partly due to the fact that fossil fuels are depleting and the need for energy is soaring because of the growing population of the world. This paper attempts to provide an idea of what is being done by researchers in remote sensing and geographical information system (GIS) field for exploring the renewable energy resources in order to get to a more sustainable future. Several studies related to renewable energy resources viz. geothermal energy, wind energy, hydropower, biomass, and solar energy, have been considered in this paper. The focus of this review paper is on exploring how remote sensing and GIS-based techniques have been beneficial in exploring optimal locations for renewable energy resources. Several case studies from different parts of the world which use such techniques in exploring renewable energy resource sites of different kinds have also been included in this paper. Though each of the remote sensing and GIS techniques used for exploration of renewable energy resources seems to efficiently sell itself in being the most effective among others, it is important to keep in mind that in actuality, a combination of different techniques is more efficient for the task. Throughout the paper, many issues relating to the use of remote sensing and GIS for renewable energy are examined from both current and future perspectives and potential solutions are suggested. The authors believe that the conclusions and recommendations drawn from the case studies and the literature reviewed in the present study will be valuable to renewable energy scientists and policymakers.

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Natural Forest Biomass Estimation Based on Plantation Information Using PALSAR Data

Cited by 52 publications

References 49 publications

Monitoring and Mapping of Rice Cropping Pattern in Flooding Area in the Vietnamese Mekong Delta Using Sentinel-1A Data: A Case of An Giang Province

Monitoring and Mapping of Rice Cropping Pattern in Flooding Area in the Vietnamese Mekong Delta Using Sentinel-1A Data: A Case of An Giang Province

Exploring the Inclusion of Small Regenerating Trees to Improve Above-Ground Forest Biomass Estimation Using Geospatial Data

Exploring Renewable Energy Resources Using Remote Sensing and GIS—A Review

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