Optimization Model to Estimate Mount Tai Forest Biomass Based on Remote Sensing

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.

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Estimation of the North–South Transect of Eastern China forest biomass using remote sensing and forest inventory data

International Journal of Remote Sensing ◽

10.1080/01431161.2013.794985 ◽

2013 ◽

Vol 34 (15) ◽

pp. 5598-5610 ◽

Cited By ~ 9

Author(s):

Yanhua Gao ◽

Xinxin Liu ◽

Chengcheng Min ◽

Honglin He ◽

Guirui Yu ◽

...

Keyword(s):

Remote Sensing ◽

Forest Inventory ◽

Eastern China ◽

Forest Biomass ◽

Inventory Data ◽

Forest Inventory Data ◽

The North

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Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Biogeosciences ◽

10.5194/bg-11-6827-2014 ◽

2014 ◽

Vol 11 (23) ◽

pp. 6827-6840 ◽

Cited By ~ 51

Author(s):

M. Réjou-Méchain ◽

H. C. Muller-Landau ◽

M. Detto ◽

S. C. Thomas ◽

T. Le Toan ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Spatial Scales ◽

Forest Biomass ◽

Remote Sensing Data ◽

Field Measurements ◽

Forest Degradation ◽

Forest Carbon ◽

Mitigation Strategies ◽

Large Plot

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

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Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms

Forests ◽

10.3390/f10121073 ◽

2019 ◽

Vol 10 (12) ◽

pp. 1073 ◽

Cited By ~ 10

Author(s):

Li ◽

Liu

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Variable Selection ◽

Aboveground Biomass ◽

Forest Type ◽

Learning Algorithms ◽

Forest Biomass ◽

Machine Learning Algorithms ◽

Biomass Estimation ◽

Landsat 8

Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensing-based forest biomass estimation in general is well developed and extensively used, improving the accuracy of biomass estimation remains challenging. In this paper, we used China’s National Forest Continuous Inventory data and Landsat 8 Operational Land Imager data in combination with three algorithms, either the linear regression (LR), random forest (RF), or extreme gradient boosting (XGBoost), to establish biomass estimation models based on forest type. In the modeling process, two methods of variable selection, e.g., stepwise regression and variable importance-base method, were used to select optimal variable subsets for LR and machine learning algorithms (e.g., RF and XGBoost), respectively. Comfortingly, the accuracy of models was significantly improved, and thus the following conclusions were drawn: (1) Variable selection is very important for improving the performance of models, especially for machine learning algorithms, and the influence of variable selection on XGBoost is significantly greater than that of RF. (2) Machine learning algorithms have advantages in aboveground biomass (AGB) estimation, and the XGBoost and RF models significantly improved the estimation accuracy compared with the LR models. Despite that the problems of overestimation and underestimation were not fully eliminated, the XGBoost algorithm worked well and reduced these problems to a certain extent. (3) The approach of AGB modeling based on forest type is a very advantageous method for improving the performance at the lower and higher values of AGB. Some conclusions in this paper were probably different as the study area changed. The methods used in this paper provide an optional and useful approach for improving the accuracy of AGB estimation based on remote sensing data, and the estimation of AGB was a reference basis for monitoring the forest ecosystem of the study area.

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Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data

Remote Sensing of Environment ◽

10.1016/j.rse.2019.02.021 ◽

2019 ◽

Vol 225 ◽

pp. 77-92 ◽

Cited By ~ 7

Author(s):

Christine I.B. Wallis ◽

Jürgen Homeier ◽

Jaime Peña ◽

Roland Brandl ◽

Nina Farwig ◽

...

Keyword(s):

Remote Sensing ◽

Forest Biomass ◽

Remote Sensing Data ◽

Biomass Productivity ◽

Montane Forest ◽

Tropical Montane Forest ◽

Sensing Data ◽

Multispectral Remote Sensing

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Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers

Remote Sensing of Environment ◽

10.1016/j.rse.2008.09.009 ◽

2009 ◽

Vol 113 (1) ◽

pp. 182-196 ◽

Cited By ~ 215

Author(s):

Kaiguang Zhao ◽

Sorin Popescu ◽

Ross Nelson

Keyword(s):

Remote Sensing ◽

Forest Biomass ◽

Scale Invariant ◽

Invariant Estimation

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Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques

Agriculture ◽

10.3390/agriculture9030054 ◽

2019 ◽

Vol 9 (3) ◽

pp. 54 ◽

Cited By ~ 9

Author(s):

Mohamad Awad

Keyword(s):

Remote Sensing ◽

Crop Yield ◽

Spatial Resolution ◽

Optimization Model ◽

Crop Yields ◽

Potato Crop ◽

Optimization Techniques ◽

Yield Estimation ◽

Climate Conditions ◽

New Model

Many crop yield estimation techniques are being used, however the most effective one is based on using geospatial data and technologies such as remote sensing. However, the remote sensing data which are needed to estimate crop yield are insufficient most of the time due to many problems such as climate conditions (% of clouds), and low temporal resolution. There have been many attempts to solve the lack of data problem using very high temporal and very low spatial resolution images such as Modis. Although this type of image can compensate for the lack of data due to climate problems, they are only suitable for very large homogeneous crop fields. To compensate for the lack of high spatial resolution remote sensing images due to climate conditions, a new optimization model was created. Crop yield estimation is improved and its precision is increased based on the new model that includes the use of the energy balance equation. To verify the results of the crop yield estimation based on the new model, information from local farmers about their potato crop yields for the same year were collected. The comparison between the estimated crop yields and the actual production in different fields proves the efficiency of the new optimization model.

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Uncertainty of Forest Biomass Estimates in North Temperate Forests Due to Allometry: Implications for Remote Sensing

Remote Sensing ◽

10.3390/rs5063007 ◽

2013 ◽

Vol 5 (6) ◽

pp. 3007-3036 ◽

Cited By ~ 20

Author(s):

Razi Ahmed ◽

Paul Siqueira ◽

Scott Hensley ◽

Kathleen Bergen

Keyword(s):

Remote Sensing ◽

Temperate Forests ◽

Forest Biomass

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