Relating SAR image texture to the biomass of regenerating tropical forests

Kuplich, Tatiana Mora; Curran, Paul J.; Atkinson, Peter M.

doi:10.1080/01431160500239107

Cited by 116 publications

(70 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Higher correlation of model P5 compared to the other models based on PALSAR data could be explained by the sensitivity of SAR textures to forest canopy [90]. Our results are in agreement with the finding of previous studies [90][91][92][93]. In the final model, ETM+ and PALSAR data, NDVI and GLCM textures are used ( Table 4).…”

Section: Forest Agb Estimation Model Based On Corrected Datasupporting

confidence: 91%

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Attarchi

Gloaguen

2014

Remote Sensing

View full text Add to dashboard Cite

Abstract:The objective of this study is to develop models based on both optical and L-band Synthetic Aperture Radar (SAR) data for above ground dry biomass (hereafter AGB) estimation in mountain forests. We chose the site of the Loveh forest, a part of the Hyrcanian forest for which previous attempts to estimate AGB have proven difficult. Uncorrected ETM+ data allow a relatively poor AGB estimation, because topography can hinder AGB estimation in mountain terrain. Therefore, we focused on the use of atmospherically and topographically corrected multispectral Landsat ETM+ and Advanced Land-Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) to estimate forest AGB. We then evaluated 11 different multiple linear regression models using different combinations of corrected spectral and PolSAR bands and their derived features. The use of corrected ETM+ spectral bands and GLCM textures improves AGB estimation significantly (adjusted R 2 = 0.59; RMSE = 31.5 Mg/ha). Adding SAR backscattering coefficients as well as PolSAR features and textures increase substantially the accuracy of AGB estimation (adjusted R 2 = 0.76; RMSE = 25.04 Mg/ha). Our results confirm that topographically and atmospherically corrected data are indispensable for the estimation of mountain forest's physical properties. We also demonstrate that only the joint use of PolSAR and multispectral data allows a good estimation of AGB in those regions. OPEN ACCESSRemote Sens. 2014, 6 3694

show abstract

Section: Forest Agb Estimation Model Based On Corrected Datasupporting

confidence: 91%

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Attarchi

Gloaguen

2014

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Other research using textures from the optical or SAR data provided similar conclusions [20,[25][26][27]53]. The critical point is to identify specific textures for given vegetation types.…”

Section: Data Saturation Problem In Landsat Imagery and Potential Solmentioning

confidence: 69%

“…They also suggested that the obtained maximum estimate was 500 t/ha, much higher than the saturation levels in other studies using optical sensors [6,31,44]. Other studies also indicated the importance of combining spectral responses and textures from optical or radar data in improving AGB estimation [21,[25][26][27]53].…”

Section: Introductionmentioning

confidence: 91%

“…Various methods have been explored to reduce the impacts of data saturation in Landsat imagery on AGB estimation accuracy (see the review paper by Lu et al [6]). Vegetation indices and textures are often used [21,[25][26][27][49][50][51][52][53]. In the moist tropical region of the Brazilian Amazon, Lu et al [51] compared different vegetation indices-e.g., normalized difference vegetation index (NDVI), perpendicular vegetation index (PVI), soil adjusted vegetation index (SAVI)-from Landsat Thematic Mapper (TM) imagery and found that the vegetation indices including shortwave infrared spectral bands (SWIR) have higher correlation with AGB than others when the forest stand structure is complex, but the vegetation indices including near-infrared wavelength (NIR) improved correlations with AGB in relatively simple forest stand structures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

et al. 2016

View full text Add to dashboard Cite

Abstract:The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different vegetation types in a subtropical region and to explore approaches to improving forest AGB estimation. Landsat Thematic Mapper imagery, digital elevation model data, and field measurements in Zhejiang province of Eastern China were used. Correlation analysis and scatterplots were first used to examine specific spectral bands and their relationships with AGB. A spherical model was then used to quantitatively estimate the saturation value of AGB for each vegetation type. A stratification of vegetation types and/or slope aspects was used to determine the potential to improve AGB estimation performance by developing a specific AGB estimation model for each category. Stepwise regression analysis based on Landsat spectral signatures and textures using grey-level co-occurrence matrix (GLCM) was used to develop AGB estimation models for different scenarios: non-stratification, stratification based on either vegetation types, slope aspects, or the combination of vegetation types and slope aspects. The results indicate that pine forest and mixed forest have the highest AGB saturation values (159 and 152 Mg/ha, respectively), Chinese fir and broadleaf forest have lower saturation values (143 and 123 Mg/ha, respectively), and bamboo forest and shrub have the lowest saturation values (75 and 55 Mg/ha, respectively). The stratification based on either vegetation types or slope aspects provided smaller root mean squared errors (RMSEs) than non-stratification. The AGB estimation models based on stratification of both vegetation types and slope aspects provided the most accurate estimation with the smallest RMSE of 24.5 Mg/ha. Relatively low AGB (e.g., less than 40 Mg/ha) sites resulted in overestimation and higher AGB (e.g., greater than 140 Mg/ha) sites resulted in underestimation. The smallest RMSE was obtained when AGB was 80-120 Mg/ha. This research indicates the importance of stratification in mitigating the data saturation problem, thus improving AGB estimation.

show abstract

“…Texture measures derived from the near-and mid-infrared bands of TM allow for a finer distinction of the structural detail (i.e., canopy height, canopy openness, basal area, LAI) of forests. These measures can maximize the discrimination of spatial information independent of tone, thereby potentially increasing the range of biomass to higher levels (Kuplich et al, 2005;Luckman et al, 1997;Latifur and Janet, 2011). Additionally, IRI has proved to be sensitive to photosynthetic activity and, thus, to growing stock volume or biomass (Hardisky et al, 1983;Chirici et al, 2008).…”

Section: The Biome-bgc Modelmentioning

confidence: 99%

Modeling of forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the Qilian mountains

Tian

Chen

et al. 2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

View full text Add to dashboard Cite

A B S T R A C TIn this work, we present a strategy for obtaining forest above-ground biomass (AGB) dynamics at a fine spatial and temporal resolution. Our strategy rests on the assumption that combining estimates of both AGB and carbon fluxes results in a more accurate accounting for biomass than considering the terms separately, since the cumulative carbon flux should be consistent with AGB increments. Such a strategy was successfully applied to the Qilian Mountains, a cold arid region of northwest China.Based on Landsat Thematic Mapper 5 (TM) data and ASTER GDEM V2 products (GDEM), we first improved the efficiency of existing non-parametric methods for mapping regional forest AGB for 2009 by incorporating the Random Forest (RF) model with the k-Nearest Neighbor (k-NN). Validation using forest measurements from 159 plots and the leave-one-out (LOO) method indicated that the estimates were reasonable (R 2 = 0.70 and RMSE = 24.52 tones ha −1 ). We then obtained one seasonal cycle ( ) using the MODIS MOD_17 GPP (MOD_17) model that was calibrated to Eddy Covariance (EC) flux tower data (2010). After that, we calibrated the ecological process model (BiomeBioGeochemical Cycles (Biome-BGC)) against above GPP estimates (for 2010) for 30 representative forest plots over an ecological gradient in order to simulate AGB changes over time. Biome-BGC outputs of GPP and net ecosystem exchange (NEE) were validated against EC data (R 2 = 0.75 and RMSE = 1. 27 gC m −2 d −1 for GPP, and R 2 = 0.61 and RMSE = 1.17 gC m −2 d −1 for NEE). The calibrated Biome-BGC was then applied to produce a longer time series for net primary productivity (NPP), which, after conversion into AGB increments according to site-calibrated coefficients, were compared to dendrochronological measurements (R 2 = 0.73 and RMSE = 46.65 g m −2 year −1 ). By combining these increments with the AGB map of 2009, we were able to model forest AGB dynamics. In the final step, we conducted a Monte Carlo analysis of uncertainties for interannual forest AGB estimates based on errors in the above forest AGB map, NPP estimates, and the conversion of NPP to an AGB increment.

show abstract

Relating SAR image texture to the biomass of regenerating tropical forests

Cited by 116 publications

References 43 publications

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

Modeling of forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the Qilian mountains

Contact Info

Product

Resources

About