Regional distribution of forest height and biomass from multisensor data fusion

Yu, Yifan; Saatchi, Sassan; Heath, Linda S.; LaPoint, Elizabeth B.; Myneni, Ranga B.; Knyazikhin, Yuri

doi:10.1029/2009jg000995

Cited by 23 publications

(15 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Statistical analysis has been a common method of fitting the biomass-height relationship. Widely used regression models include power [Chave et al, 2005;Köhler and Huth, 2010;Mitchard et al, 2011;Saatchi et al, 2011;Wang et al, 2013], linear [Fang et al, 2006;Skowronski et al, 2007], the quadratic polynomial [Lefsky et al, 2005b], and exponential functions [Yu et al, 2010]. Previous studies have shown that biomass-height relationships differ greatly across environmental gradients and among different forest types [Drake et al, 2003;Pan et al, 2004;Wang et al, 2013].…”

Section: Methods 221 Estimating Forest Stand Agementioning

confidence: 99%

Mapping forest stand age in China using remotely sensed forest height and observation data

Zhang

Chen

et al. 2014

JGR Biogeosciences

View full text Add to dashboard Cite

Forest stand age plays a crucial role in determining the terrestrial carbon source or sink strength and reflects major disturbance information. Forests in China have changed drastically in recent decades, but quantification of spatially explicit forest age at national level has been lacking to date. This study generated a national map of forest age at 1 km spatial resolution using the remotely sensed forest height and forest type data in 2005, as well as relationships between age and height retrieved from field observations. These relationships include biomass as an intermediate parameter for major forest types in different regions of China. Biomass-height and age-biomass relationships were well fitted using field observations, with respective R 2 values greater than 0.60 and 0.71 (P < 0.01), indicating the viability of age-height relationships developed for age estimation in China. The resulting map was evaluated by comparison with national, provincial, and county forest inventories. The validation had high regional (R 2 = 0.87, 2-8 years errors in six regions), provincial (R 2 = 0.53, errors less than 10 years and consistent age structure in most provinces), and plot (R 2 values of 0.16À0.32, P < 0.01) agreement between map values and inventory-based estimates. This confirms the reliability and applicability of the age-height approach demonstrated in this study for quantifying forest age over large regions. The map reveals a large spatial heterogeneity of forest age in China: old in southwestern, northwestern, and northeastern areas, and young in southern and eastern regions.

show abstract

Section: Methods 221 Estimating Forest Stand Agementioning

confidence: 99%

Mapping forest stand age in China using remotely sensed forest height and observation data

Zhang

Chen

et al. 2014

JGR Biogeosciences

View full text Add to dashboard Cite

show abstract

“…SAR remote sensing is no exception regarding the issue of uncertainty in the analysis in addition to other complexities and ambiguities that are specific to SAR techniques. Several other limitations of SAR are as follows: (1) costly data, (2) temporal repeativity, (3) Santoro et al 2006;Yu et al 2010;Le Toan et al 2011;Hamdan et al 2011;Englhart et al 2012;etc. Fransson et al 2001;Santoro et al 2003;Kumar 2009;etc. Approach 2: Based on sensor type Single Multiple/ Synergic Nizalapur et al 2010;Hamdan et al 2011;Wollersheim et al 2011;Carreiras et al 2013;etc.…”

Section: Limitations and Uncertaintiesmentioning

confidence: 99%

A review of radar remote sensing for biomass estimation

Sinha¹,

Jeganathan²,

Sharma

et al. 2015

Int. J. Environ. Sci. Technol.

251

116

View full text Add to dashboard Cite

Forest plays a vital role in regulating climate through carbon sequestration in its biomass. Biomass reflects the health and environmental conditions of a forest ecosystem. In context to the climate change mitigation mechanisms like REDD (reducing emissions from deforestation and forest degradation), an extensive forest monitoring campaign is especially important. Remote sensing of forest structure and biomass with synthetic aperture radar (SAR) bears significant potential for mapping and understanding forest ecological processes. Limitations of the conventional forest inventory procedures, like the extensive cost, labor and time, can be overcome through integrated geospatial techniques. Optical sensor or SAR data are suitable for extracting information about simple and homogeneous forest stand sites. However, optical sensors face serious limitations, specifically in tropical regions, like the cloud cover that SAR can overcome along with targeting saturation and penetration aspects. Simultaneous use of spectral information and image texture parameters improves the biomass assessment over undulating terrain and in radical conditions. Also, synergic use of multi-sensor optical and SAR has better potential than single sensor. Interferometric (InSAR) and polarimetric (PolSAR) SAR or a combination of the both (PolInSAR) serves as effective alternatives. These techniques could serve as valuable methods for biomass assessment of heterogeneous complex biophysical environments. However, SAR data have its own limitations and complexities. Identifying, understanding and solving major uncertainties in different stages of the biomass estimation procedure are critical. In this regard, the current study provides a review of radar remote sensing-based studies in forest biomass estimation.

show abstract

“…Equation (4) indicates that a large forest patch with great PCH has a larger weight and thus a larger structuring element size.…”

Section: Refined Gld: Gldmentioning

confidence: 99%

Extracting Ground-Level DEM From SRTM DEM in Forest Environments Based on Mathematical Morphology

Liu¹,

Liu

Alsdorf

2014

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

The demands for bare-earth or ground-level digital elevation models (DEMs) are significant for various environmental and ecological studies. As one of the most widely used global DEMs, the Shuttle Radar Topography Mission (SRTM) DEM is available to the public. However, the SRTM DEM is not a bare-earth DEM because it includes man-made structures and vegetation. The objective of this paper is to develop a mathematical morphology-based approach to generate ground-level DEM (GLD) from the SRTM DEM in forest environments. The proposed algorithm is implemented as follows. First, an initial GLD is derived from the SRTM DEM by using morphological operations with a single structuring element. Second, homogeneous forest patches are generated by applying watershed segmentation to pseudocanopy height (PCH) that is obtained by subtracting the initial GLD from the SRTM DEM. Based on segmented patches, a refined GLD is derived by using morphological operations with adaptive structuring elements of different sizes. Third, PCH is updated with the refined GLD and then resegmented into forest patches, from which a final GLD is obtained by subtracting the updated mean PCH from the SRTM DEM. Finally, bare-earth DEMs from the National Elevation Data Set (NED) are used to validate the extracted GLD. The results show that the root mean square error of the final GLD compared with the NED is significantly reduced for two study sites. This type of GLD would be applicable to large-scale environmental studies where accurate topographical information is not available. Index Terms-Ground-level digital elevation model (DEM), mathematical morphology, National Elevation Data Set (NED), Shuttle Radar Topography Mission (SRTM) DEM.

show abstract

Regional distribution of forest height and biomass from multisensor data fusion

Cited by 23 publications

References 39 publications

Mapping forest stand age in China using remotely sensed forest height and observation data

Mapping forest stand age in China using remotely sensed forest height and observation data

A review of radar remote sensing for biomass estimation

Extracting Ground-Level DEM From SRTM DEM in Forest Environments Based on Mathematical Morphology

Contact Info

Product

Resources

About