Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia

Lucas, Richard; Cronin, N.; Moghaddam, Mahta; Lee, Alex; Armston, John; Bunting, Peter; Witte, Christian

doi:10.1016/j.rse.2005.09.020

Cited by 70 publications

(57 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is also interest in using finer scale biomass mapping to monitor regeneration and growth in low density woodlands. For example, ALOS PALSAR data have been combined with Landsat data by [34,35] to characterize re-growth in open Brigalow woodlands in Australia to assist management strategies, such as thinning and weed control, illustrating practical management actions that can be supported by this finer scale information.…”

Section: The Use Of More Detailed Mapping Of Woody Biomass In Savannasmentioning

confidence: 99%

Local-Scale Mapping of Biomass in Tropical Lowland Pine Savannas Using ALOS PALSAR

Michelakis

Stuart

Lopez

et al. 2014

Forests

View full text Add to dashboard Cite

Fine-scale biomass maps offer forest managers the prospect of more detailed and locally accurate information for measuring, reporting and verification activities in contexts, such as sustainable forest management, carbon stock assessments and ecological studies of forest growth and change. In this study, we apply a locally validated method for estimating aboveground woody biomass (AGWB) from Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) data to produce an AGWB map for the lowland pine savannas of Belize at a spatial resolution of 100 m. Over 90% of these woodlands are predicted to have an AGWB below 60 tha −1 , with the average woody biomass of these savannas estimated at 23.5 tha −1 . By overlaying these spatial estimates upon previous thematic mapping of national land cover, we derive representative average biomass values of ~32 tha −1 and ~18 tha −1 for the previously qualitative classes of "denser" and "less dense" tree savannas. The predicted average biomass, from the mapping for savannas woodlands occurring within two of Belize's larger protected areas, agree closely with previous biomass estimates for these areas based on ground surveys and forest inventories (error ≤20%). However, biomass estimates derived for these protected areas from two biomass maps produced at coarser resolutions (500 m and 1000 m) from global datasets overestimated biomass (errors ≥275% in each dataset). OPEN ACCESSForests 2014, 5 2378The finer scale biomass mapping of both protected and unprotected areas provides evidence to suggest that protection has a positive effect upon woody biomass, with the mean AGWB higher in areas protected and managed for biodiversity (protected and passively managed (PRPM), 29.5 tha −1 ) compared to unprotected areas (UPR, 23.29 tha −1 ).These findings suggest that where sufficient ground data exists to build a reliable local relationship to radar backscatter, the more detailed biomass mapping that can be produced from ALOS and similar satellite data at resolutions of ~100 m provides more accurate and spatially detailed information that is more appropriate for supporting the management of forested areas of ~10,000 ha than biomass maps that can be produced from lower resolution, but freely available global data sets.

show abstract

Section: The Use Of More Detailed Mapping Of Woody Biomass In Savannasmentioning

confidence: 99%

Local-Scale Mapping of Biomass in Tropical Lowland Pine Savannas Using ALOS PALSAR

Michelakis

Stuart

Lopez

et al. 2014

Forests

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

“…This editing was a time-consuming process, which future research should look at reducing. Two main options are apparent: increasing the temporal frequency of the imagery, or combining the multi-spectral data with information on vegetation structure derived from L-Band radar [69][70][71].…”

Section: Future Researchmentioning

confidence: 99%

“…Secondly, data from L-Band radar satellites, such as the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR), are able to map differences in vegetation structure relating to the presence of tree trunks and branches [69][70][71]. The combination of PALSAR and Landsat data has previously improved vegetation mapping in QLD [69][70][71], and future research should investigate methods of combining with Sentinel-2 data.…”

Section: Future Researchmentioning

confidence: 99%

See 1 more Smart Citation

Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

Fisher

Day²,

Gill

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

Tree cover maps are used for many purposes, such as vegetation mapping, habitat connectivity and fragmentation studies. Small remnant patches of native vegetation are recognised as ecologically important, yet they are underestimated in remote sensing products derived from Landsat. High spatial resolution sensors are capable of mapping small patches of trees, but their use in large-area mapping has been limited. In this study, multi-temporal Satellite pour l'Observation de la Terre 5 (SPOT5) High Resolution Geometrical data was pan-sharpened to 5 m resolution and used to map tree cover for the Australian state of New South Wales (NSW), an area of over 800,000 km 2 . Complete coverages of SPOT5 panchromatic and multispectral data over NSW were acquired during four consecutive summers (2008-2011) for a total of 1256 images. After pre-processing, the imagery was used to model foliage projective cover (FPC), a measure of tree canopy density commonly used in Australia. The multi-temporal imagery, FPC models and 26,579 training pixels were used in a binomial logistic regression model to estimate the probability of each pixel containing trees. The probability images were classified into a binary map of tree cover using local thresholds, and then visually edited to reduce errors. The final tree map was then attributed with the mean FPC value from the multi-temporal imagery. Validation of the binary map based on visually assessed high resolution reference imagery revealed an overall accuracy of 88% (˘0.51% standard error), while comparison against airborne lidar derived data also resulted in an overall accuracy of 88%. A preliminary assessment of the FPC map by comparing against 76 field measurements showed a very good agreement (r 2 = 0.90) with a root mean square error of 8.57%, although this may not be representative due to the opportunistic sampling design. The map represents a regionally consistent and locally relevant record of tree cover for NSW, and is already widely used for natural resource management in the state.

show abstract

Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia

Abstract: Integration of Radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia Moghaddam, M.; Witte, C.; Cronin, N.; Lucas, Richard M.

Cited by 70 publications

References 30 publications

Local-Scale Mapping of Biomass in Tropical Lowland Pine Savannas Using ALOS PALSAR

Local-Scale Mapping of Biomass in Tropical Lowland Pine Savannas Using ALOS PALSAR

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

Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

Contact Info

Product

Resources

About