ABOVEGROUND BIOMASS AND CARBON STOCK ESTIMATION USING DOUBLE SAMPLING APPROACH AND REMOTELY-SENSED DATA

2016 ◽  
Vol 78 (5-4) ◽  
Author(s):  
Nurul Ain Mohd Zaki ◽  
Zulkiflee Abd Latif ◽  
Mohd Zainee Zainal
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Carbon Stock ◽  
Field Data ◽  
Double Sampling ◽  
Lidar Data ◽  
Field Sampling ◽  
Remotely Sensed Data ◽  
Sampling Approach ◽  
Carbon Inventory

Tropical forest embraces a large stock of carbon and contributes to the enormous amount of aboveground biomass (AGB) in the global carbon cycle. In order to quantify the carbon inventory, field data is vital for accurately determining the forest parameter such as diameter at the breast height (DBH), height  of the tree (h) ,crown diameter (CD) and tree species. The merging of the multi-sensory remote sensing which is LiDAR (Light Detection and Ranging) and very high resolution satellite imagery can reduce the labor intensive of field sampling for a large area of carbon inventory data. Double sampling approach which is combination of the field sampling plot measurement with ancillary remote sensing data used to improve the precision of AGB estimation compared by using field data alone. Hence, this study aims: (1) to describe the use of field data plots in a statistical way, and (2) to determine the potential of LiDAR data in a double sampling forest aboveground biomass and carbon stock inventories and (3) to compare the used of field data plot itself or combination with LiDAR data to quantify the aboveground biomass and carbon stock for upcoming inventories.

scholarly journals Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 692
Author(s):  
MD Abdul Mueed Choudhury ◽  
Ernesto Marcheggiani ◽  
Andrea Galli ◽  
Giuseppe Modica ◽  
Ben Somers
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Tree Species ◽  
Carbon Stock ◽  
Dominant Species ◽  
Remote Sensing Data ◽  
Data Sources ◽  
Lidar Data ◽  
Urban Tree ◽  
Sensing Data

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

scholarly journals ABOVE GROUND TREES BIOMASS OF LORE LINDU NATIONAL PARK-CENTRAL SULAWESI : A STUDY COMBINING FIELD MEASUREMENT AND REMOTE SENSING

Agromet ◽  
2010 ◽  
Vol 24 (1) ◽  
pp. 33
Author(s):  
Naimatu Solicha ◽  
Tania June ◽  
M. Ardiansyah ◽  
Antonius B. W.
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Field Data ◽  
Forest Cover ◽  
Satellite Image ◽  
Forest Biomass ◽  
Allometric Equation ◽  
Natural Forest ◽  
Forest Cover Type ◽  
Cover Type

Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.

scholarly journals Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods

2019 ◽  
Vol 11 (8) ◽  
pp. 949 ◽  
Author(s):  
Salim Malek ◽  
Franco Miglietta ◽  
Terje Gobakken ◽  
Erik Næsset ◽  
Damiano Gianelle ◽  
...  
Keyword(s):  
Active Learning ◽  
Data Collection ◽  
Aboveground Biomass ◽  
Field Data ◽  
Random Selection ◽  
Support Vector ◽  
Lidar Data ◽  
Individual Tree ◽  
Field Data Collection ◽  
Collection Cost

Light detection and ranging (lidar) data are nowadays a standard data source in studies related to forest ecology and environmental mapping. Medium/high point density lidar data allow to automatically detect individual tree crowns (ITCs), and they provide useful information to predict stem diameter and aboveground biomass of each tree represented by a detected ITC. However, acquisition of field data is necessary for the construction of prediction models that relate field data to lidar data and for validation of such models. When working at ITC level, field data collection is often expensive and time-consuming as accurate tree positions are needed. Active learning (AL) can be very useful in this context as it helps to select the optimal field trees to be measured, reducing the field data collection cost. In this study, we propose a new method of AL for regression based on the minimization of the field data collection cost in terms of distance to navigate between field sample trees, and accuracy in terms of root mean square error of the predictions. The developed method is applied to the prediction of diameter at breast heights (DBH) and aboveground biomass (AGB) of individual trees by using their height and crown diameter as independent variables and support vector regression. The proposed method was tested on two boreal forest datasets, and the obtained results show the effectiveness of the proposed selecting strategy to provide substantial improvements over the different iterations compared to a random selection. The obtained RMSE of DBH/AGB for the first dataset was 5.09 cm/95.5 kg with a cost equal to 8256/6173 m by using the proposed multi-objective method of selection. However, by using a random selection, the RMSE was 5.20 cm/102.1 kg with a cost equal to 28,391/30,086 m. The proposed approach can be efficient in order to get more accurate predictions with smaller costs, especially when a large forest area with no previous field data is subject to inventory and analysis.

scholarly journals Exploring the Potential of Unmanned Aerial Vehicle (UAV) Remote Sensing for Mapping Plucking Area of Tea Plantations

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1214
Author(s):  
Qingfan Zhang ◽  
Bo Wan ◽  
Zhenxiu Cao ◽  
Quanfa Zhang ◽  
Dezhi Wang
Keyword(s):  
Remote Sensing ◽  
Survey Methods ◽  
Remotely Sensed ◽  
Lidar Data ◽  
Remotely Sensed Data ◽  
Tea Plantation ◽  
Aerial Photogrammetry ◽  
Tea Plantations ◽  
Tea Garden ◽  
Optical Imagery

Mapping plucking areas of tea plantations is essential for tea plantation management and production estimation. However, on-ground survey methods are time-consuming and labor-intensive, and satellite-based remotely sensed data are not fine enough for plucking area mapping that is 0.5–1.5 m in width. Unmanned aerial vehicles (UAV) remote sensing can provide an alternative. This paper explores the potential of using UAV-derived remotely sensed data for identifying plucking areas of tea plantations. In particular, four classification models were built based on different UAV data (optical imagery, digital aerial photogrammetry, and lidar data). The results indicated that the integration of optical imagery and lidar data produced the highest overall accuracy using the random forest algorithm (94.39%), while the digital aerial photogrammetry data could be an alternative to lidar point clouds with only a ~3% accuracy loss. The plucking area of tea plantations in the Huashan Tea Garden was accurately measured for the first time with a total area of 6.41 ha, which accounts for 57.47% of the tea garden land. The most important features required for tea plantation mapping were the canopy height, variances of heights, blue band, and red band. Furthermore, a cost–benefit analysis was conducted. The novelty of this study is that it is the first specific exploration of UAV remote sensing in mapping plucking areas of tea plantations, demonstrating it to be an accurate and cost-effective method, and hence represents an advance in remote sensing of tea plantations.

scholarly journals Factors influencing the estimation of aboveground biomass (AGB) in tropical forests using RADAR remote sensing.

2018 ◽  
Author(s):  
Victoria E Espinoza-Mendoza
Keyword(s):  
Remote Sensing ◽  
Tropical Forests ◽  
Aboveground Biomass ◽  
Field Data ◽  
Spatial Information ◽  
Alos Palsar ◽  
Radar Remote Sensing ◽  
Radar Sensors ◽  
Factors Influencing ◽  
Optical Images

Despite the large amount of accessible spatial information, the issue of estimating aboveground biomass through remote sensing, especially radar, remains a challenge in complex ecosystems such as tropical forests. One of the advantages of radar sensors is that of "crossing clouds" (capacity that does not have optical images like Landsat), facilitating their use in areas with permanent cloud cover. This work defines, from several studies conducted in tropical forests using ALOS PALSAR, which are the factors with the most influence on the signal of the radar. This can be useful in the development and/or improvement of methodologies to estimate aboveground biomass in tropical forests, combining field data and satellite imagery of radar.

scholarly journals Integrating field sampling, geostatistics and remote sensing to map wetland vegetation in the Pantanal, Brazil

2011 ◽  
Vol 8 (3) ◽  
pp. 667-686 ◽  
Author(s):  
J. Arieira ◽  
D. Karssenberg ◽  
S. M. de Jong ◽  
E. A. Addink ◽  
E. G. Couto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Cross Validation ◽  
Remotely Sensed ◽  
Wetland Vegetation ◽  
Universal Kriging ◽  
Field Sampling ◽  
Remotely Sensed Data ◽  
Vegetation Communities

Abstract. Development of efficient methodologies for mapping wetland vegetation is of key importance to wetland conservation. Here we propose the integration of a number of statistical techniques, in particular cluster analysis, universal kriging and error propagation modelling, to integrate observations from remote sensing and field sampling for mapping vegetation communities and estimating uncertainty. The approach results in seven vegetation communities with a known floral composition that can be mapped over large areas using remotely sensed data. The relationship between remotely sensed data and vegetation patterns, captured in four factorial axes, were described using multiple linear regression models. There were then used in a universal kriging procedure to reduce the mapping uncertainty. Cross-validation procedures and Monte Carlo simulations were used to quantify the uncertainty in the resulting map. Cross-validation showed that accuracy in classification varies according with the community type, as a result of sampling density and configuration. A map of uncertainty derived from Monte Carlo simulations revealed significant spatial variation in classification, but this had little impact on the proportion and arrangement of the communities observed. These results suggested that mapping improvement could be achieved by increasing the number of field observations of those communities with a scattered and small patch size distribution; or by including a larger number of digital images as explanatory variables in the model. Comparison of the resulting plant community map with a flood duration map, revealed that flooding duration is an important driver of vegetation zonation. This mapping approach is able to integrate field point data and high-resolution remote-sensing images, providing a new basis to map wetland vegetation and allow its future application in habitat management, conservation assessment and long-term ecological monitoring in wetland landscapes.

scholarly journals Modelling Aboveground Biomass Carbon Stock of the Bohai Rim Coastal Wetlands by Integrating Remote Sensing, Terrain, and Climate Data

2021 ◽  
Vol 13 (21) ◽  
pp. 4321
Author(s):  
Shaobo Sun ◽  
Yafei Wang ◽  
Zhaoliang Song ◽  
Chu Chen ◽  
Yonggen Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Aboveground Biomass ◽  
Coastal Wetlands ◽  
Carbon Stock ◽  
Coastal Wetland ◽  
Data Sets ◽  
Special Role ◽  
Climate Data ◽  
Bohai Rim

Remotely sensed vegetation indices (VIs) have been widely used to estimate the aboveground biomass (AGB) carbon stock of coastal wetlands by establishing Vis-related linear models. However, these models always have high uncertainties due to the large spatial variation and fragmentation of coastal wetlands. In this paper, an efficient coastal wetland AGB model for the Bohami Rim coastal wetlands was presented based on multiple data sets. The model was developed statistically with 7 independent variables from 23 metrics derived from remote sensing, topography, and climate data. Compared to previous models, it had better performance, with a root mean square error and r value of 188.32 g m−2 and 0.74, respectively. Using the model, we firstly generated a regional coastal wetland AGB map with a 10 m spatial resolution. Based on the AGB map, the AGB carbon stock of the Bohai Rim coastal wetland was 2.11 Tg C in 2019. The study demonstrated that integrating emerging high spatial resolution multi-remote sensing data and several auxiliary metrics can effectively improve VIs-based coastal wetland AGB models. Such models with emerging freely available data sets will allow for the rapid monitoring and better understanding of the special role that “blue carbon” plays in global carbon cycle.

scholarly journals REMOTELY SENSED DATA FUSION IN MODERN AGE ARCHAEOLOGY AND MILITARY HISTORICAL RECONSTRUCTION

2016 ◽  
Vol XLI-B5 ◽  
pp. 281-286 ◽  
Author(s):  
A. Juhász ◽  
H. Neuberger
Keyword(s):  
Remote Sensing ◽  
Point Clouds ◽  
Lidar Data ◽  
Historical Reconstruction ◽  
Remotely Sensed Data ◽  
Typical Sample ◽  
3D Point Clouds ◽  
Potential Applications ◽  
Edge Method ◽  
Remote Sensing Methods

LiDAR technology has become one of the major remote sensing methods in the last few years. There are several areas, where the scanned 3D point clouds can be used very efficiently. In our study we review the potential applications of LiDAR data in military historical reconstruction. Obviously, the base of this kind of investigation must be the archive data, but it is an interesting challenge to integrate a cutting edge method into such tasks. The LiDAR technology can be very useful, especially in vegetation covered areas, where the conventional remote sensing technologies are mostly inefficient. We review two typical sample projects where we integrated LiDAR data in military historical GIS reconstruction. Finally, we summarize, how laser scanned data can support the different parts of reconstruction work and define the technological steps of LiDAR data processing.

Sentinel SAR Data and In-Situ-Based High-Resolution Above-Ground Carbon Stocks Estimation Within the Open Forests of Ramgarh District

2020 ◽  
pp. 180-205
Author(s):  
Akash Anand
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Field Data ◽  
Forest Cover ◽  
Allometric Equation ◽  
Total Carbon ◽  
Biomass Estimation ◽  
Biophysical Parameters ◽  
Sar Data

The present study deals with an approach to estimate the above ground biomass (AGB) to assess the total carbon stock of forest cover present in Ramgarh district using remote sensing and GIS techniques. Due to the fact that biomass estimation is one of the most influential biophysical parameters in traditional carbon sequestration techniques, satellite remote sensing plays an important role in AGB and carbon stock estimation. Presently, AGB is estimated using Sentinel1A SAR data in conjunction with in-situ field data, which is conducted in 20 different sites within the forest area. Biomass is calculated for each plot, and a correlation analysis is performed with the backscatter value obtained from SAR data to generate an allometric equation that is used to calculate the AGB and carbon stock for the entire forest cover. Both Polarization VV and VH are correlated with field data in which cross-polarized backscatter value shown a stronger correlation of 0.75 (R2 Value). C-band is proved to be the best band for the estimation of biomass and carbon stock in tropical mixed forests.

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