Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia

Lucas, Richard; Cronin, N.; Lee, Alex; Moghaddam, Mahta; Witte, Christian; Tickle, P.

doi:10.1016/j.rse.2005.10.019

Cited by 126 publications

(81 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The observed correlation with forest biophysical attributes and saturation levels differed significantly across different forest types and environmental imaging conditions, even for a given wavelength. In the case of L-band, the reported saturation levels with respect to aboveground biomass retrieval varied between 40 and 180 t/ha [19,[23][24][25][26][27][28][29][30][31]. A spaceborne P-band SAR, which would be less affected by saturation at higher biomass levels, is planned for launch in the coming years in the frame of the Earth Explorer Program of the European Space Agency (ESA) [32].…”

Section: Spatially Explicit Mapping Of Forest Aboveground Biomass Andmentioning

confidence: 99%

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

et al. 2014

View full text Add to dashboard Cite

show abstract

Section: Spatially Explicit Mapping Of Forest Aboveground Biomass Andmentioning

confidence: 99%

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Many studies have estimated LAI and biomass based on SAR images data acquired from either airborne or space-borne platforms [27][28][29][30][31]. Some studies have shown that SAR backscattering was well correlated with biomass, especially that characterized by medium fractional cover [32][33][34]. Since optical and SAR image data respond to crop characteristics differently, their complementary information content can support the estimation of crop conditions [12].…”

Section: Introductionmentioning

confidence: 99%

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

View full text Add to dashboard Cite

Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) , p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data.

show abstract

“…The most common parameter modelled is above ground biomass (AGB). In a similar manner to visible-near infrared reflectance-based indices, the SAR backscatter-AGB relationship is curvilinear and many studies have found that it typically saturates in the range of 100-150 t/ha (from the reviews of [165,167,169]), whereas others have found lower thresholds of saturation [170,171]. Thus, biomass and biomass loss are difficult to model and estimate using backscatter or backscatter-based indices in dense forests.…”

Section: Radarmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

View full text Add to dashboard Cite

Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time-and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

show abstract

Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia

Cited by 126 publications

References 45 publications

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Contact Info

Product

Resources

About