Abstract
Remote sensing-based woody biomass quantification in sparsely-vegetated areas is often
limited when using only common broadband vegetation indices as input data for correlation with
ground-based measured biomass information. Red edge indices and texture attributes are often
suggested as a means to overcome this issue. However, clear recommendations on the suitability of
specific proxies to provide accurate biomass information in semi-arid to arid environments are still
lacking. This study contributes to the understanding of using multispectral high-resolution satellite
data (RapidEye), specifically red edge and texture attributes, to estimate wood volume in semi-arid
ecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and Selection
Operator) and random forest were used as predictive models relating in situ-measured aboveground
standing wood volume to satellite data. Model performance was evaluated based on cross-validation
bias, standard deviation and Root Mean Square Error (RMSE) at the logarithmic and non-logarithmic
scales. Both models achieved rather limited performances in wood volume prediction. Nonetheless,
model performance increased with red edge indices and texture attributes, which shows that they
play an important role in semi-arid regions with sparse vegetation.
limited when using only common broadband vegetation indices as input data for correlation with
ground-based measured biomass information. Red edge indices and texture attributes are often
suggested as a means to overcome this issue. However, clear recommendations on the suitability of
specific proxies to provide accurate biomass information in semi-arid to arid environments are still
lacking. This study contributes to the understanding of using multispectral high-resolution satellite
data (RapidEye), specifically red edge and texture attributes, to estimate wood volume in semi-arid
ecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and Selection
Operator) and random forest were used as predictive models relating in situ-measured aboveground
standing wood volume to satellite data. Model performance was evaluated based on cross-validation
bias, standard deviation and Root Mean Square Error (RMSE) at the logarithmic and non-logarithmic
scales. Both models achieved rather limited performances in wood volume prediction. Nonetheless,
model performance increased with red edge indices and texture attributes, which shows that they
play an important role in semi-arid regions with sparse vegetation.
| Original language | English |
|---|---|
| Article number | 540 |
| Journal | Remote Sensing |
| Volume | 8 |
| Issue number | 7 |
| Number of pages | 19 |
| ISSN | 2072-4292 |
| DOIs | |
| Publication status | Published - Jul 2016 |