Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data

Wang, Xinchuang; Wang, Shidong; Dai, Limin

doi:10.1007/s11676-017-0504-6

Cited by 22 publications

(14 citation statements)

References 33 publications

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“…Our finding of a positive effect of elevation on C d in all ecoregions of Northeast Asia, consistent with two recent studies (Wang et al., 2008, 2018), can be attributed to the fact that high‐elevation mountainous regions are relatively less affected by historical logging than the low elevation plains (Tang et al., 2006) due to excessive slope gradient and small compartment surface (McEwan et al., 2020). Meanwhile, our finding of a positive effect of human footprint ( H 1 ) on C d in all ecoregions but Eco5 (Figure 8) indicates that the influence of human interference on forest biomass C stock can go both ways.…”

Section: Discussionsupporting

confidence: 91%

“…Our finding of a positive effect of elevation on C d in all ecoregions of Northeast Asia, consistent with two recent studies (Wang et al, 2008(Wang et al, , 2018, can be attributed to the fact that high-elevation mountainous regions are relatively less affected by historical logging than the low elevation plains (Tang et al, 2006) due to excessive slope gradient and small compartment surface (McEwan et al, 2020).…”

Section: Ta B L E 3 Comparisons Between the Biomass C Density And C Ssupporting

confidence: 92%

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New forest biomass carbon stock estimates in Northeast Asia based on multisource data

Luo

Kim

Zhao

et al. 2020

Global Change Biology

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Forests play an important role in both regional and global C cycles. However, the spatial patterns of biomass C density and underlying factors in Northeast Asia remain unclear. Here, we characterized spatial patterns and important drivers of biomass C density for Northeast Asia, based on multisource data from in situ forest inventories, as well as remote sensing, bioclimatic, topographic, and human footprint data. We derived, for the first time, high-resolution (1 km × 1 km) maps of the current and future forest biomass C density for this region. Based on these maps, we estimated that current biomass C

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Section: Discussionsupporting

confidence: 91%

Section: Ta B L E 3 Comparisons Between the Biomass C Density And C Ssupporting

confidence: 92%

New forest biomass carbon stock estimates in Northeast Asia based on multisource data

Luo

Kim

Zhao

et al. 2020

Global Change Biology

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“…Among some studies, it was found that the spatial resolution has a key role in the accuracy of the biomass. The spatial resolution of Landsat can achieve a low accuracy [112], while the SPOT satellites [113], GeoEye [114], and Quickbird [55] can achieve medium to high accuracy, WorldView [115] and spatial resolution of LiDAR can achieve high accuracy [116]. Overall, the more accurate the modeling, the greater the approximation to the observed values [117].…”

Section: Biomass Estimationmentioning

confidence: 99%

The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

Isbaex¹,

Coelho²

2021

Forest Biomass - From Trees to Energy

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Mapping land-cover/land-use (LCLU) and estimating forest biomass using satellite images is a challenge given the diversity of sensors available and the heterogeneity of forests. Copernicus program served by the Sentinel satellites family and the Google Earth Engine (GEE) platform, both with free and open services accessible to its users, present a good approach for mapping vegetation and estimate forest biomass on a global, regional, or local scale, periodically and in a repeated way. The Sentinel-2 (S2) systematically acquires optical imagery and provides global monitoring data with high spatial resolution (10–60 m) images. Given the novelty of information on the use of S2 data, this chapter presents a review on LCLU maps and forest above-ground biomass (AGB) estimates, in addition to exploring the efficiency of using the GEE platform. The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates. The GEE platform is a promising tool for executing complex workflows of satellite data processing.

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“…Vegetation spectral reflectance characteristics Accurate estimation of forest biomass is of important values to the protection of forest ecological resources [15], which can be realized by remote sensing technology. Remote sensing technology obtains remote sensing images through acquiring electromagnetic wave information by detecting ground objects with the remote sensor.…”

Section: Spectral Signal Of Vegetation and Data Selectionmentioning

confidence: 99%

Monitoring and Protection of Forest Ecological Tourism Resources by Dynamic Monitoring System

Lin

2019

Ecological Chemistry and Engineering S

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Forest can adjust climate and provide resources for the development of the society and tourism as well as promote the progress of human civilization, which is of great significance to the survival and development of human beings. With the industrial development and the improvement of people’s living standard, the development strength on forest resources is becoming higher than ever before. As forest resources are important resources which can maintain the ecological balance of the earth, its monitoring and protection is necessary. Hence, remote sensing technology has been developed for monitoring the changes of forest resources, which has the quickness characteristics and real-time spatial information acquisition and analysis capacities. This paper firstly introduced the geographical location, geomorphology, climate status, soil and vegetation types of Zhangjiakou City, Hebei Province and the basic theory of remote sensing monitoring such as vegetation spectral reflectance and normalized differential vegetation index (NDVI). Then, the NDVI was used to analyse the vegetation coverage and area ratio of Zhangjiakou City in 2006, 2010 and 2016. It was found that the vegetation coverage during the ten years from 2006 to 2016 showed an overall trend of growth. Conclusions: It is concluded that dynamic monitoring can effectively monitor and protect forest vegetation, which provided ideas for the follow-up forestry planning and ecological tourism development in Zhangjiakou.

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Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data

Cited by 22 publications

References 33 publications

New forest biomass carbon stock estimates in Northeast Asia based on multisource data

New forest biomass carbon stock estimates in Northeast Asia based on multisource data

The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

Monitoring and Protection of Forest Ecological Tourism Resources by Dynamic Monitoring System

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