Abstract
Airborne laser scanning (ALS) is considered as the most accurate remote sensing data for the predictive modelling of AGB. However, tropical landscapes experiencing land use changes are typically heterogeneous mosaics of various land cover types with high tree species richness and trees outside forests, making them challenging environments even for ALS. Therefore, combining ALS data with other remote sensing data, or stratification by land cover type could be particularly beneficial in terms of modelling accuracy in such landscapes. Our objective was to test if spectral-temporal metrics from the Landsat time series (LTS), simultaneously acquired hyperspectral (HS) data, or stratification to the forest and non-forest classes improves accuracy of the AGB modelling across an Afromontane landscape in Kenya. The combination of ALS and HS data improved the cross-validated RMSE from 51.5 Mg ha−1 (42.7%) to 47.7 Mg ha−1 (39.5%) in comparison to the use of ALS data only. Furthermore, the combination of ALS data with LTS and HS data improved accuracies of the models for the forest and non-forest classes, and the overall best results were achieved when using ALS and HS data with stratification (RMSE 40.0 Mg ha−1, 33.1%). We conclude that ALS data alone provides robust models for AGB mapping across tropical mosaic landscapes, even without stratification. However, ALS and HS data together, and additional forest classification for stratification, can improve modelling accuracy considerably in similar, tree species rich areas.
Highlights
Large amounts of carbon are stored in the aboveground biomass (AGB) in tropical forests, and change in the tropical forest cover is a major source of carbon emissions (Houghton et al, 2009; Ciais et al, 2013)
The tropical mosaic landscapes have got increasing attention as those are vulnerable to land use and land cover change and large fraction of landscape-level AGB can reside outside forests
The results demonstrate a clear improvement in the modelling accuracy if combining Airborne laser scanning (ALS) and HS data, and applying a forest and non-forest stratification
Summary
Large amounts of carbon are stored in the aboveground biomass (AGB) in tropical forests, and change in the tropical forest cover is a major source of carbon emissions (Houghton et al, 2009; Ciais et al, 2013). Most of the AGB resides in the forests, the importance of the trees outside forests has been increasingly underlined (Schnell et al, 2015; Sloan and Sayer, 2015; Vanderhaegen et al, 2015). With the agricultural expansion and intensification, tropical landscapes become more affected by human activities, and turn into the mosaics of agricultural land, plantations and secondary, logged and fragmented forests (Mertz et al, 2012; Laurance et al, 2014). In order to better understand AGB distributions and climate change mitigation possibilities within the landscapes, it is important to depict AGB variations outside forests. Remote sensing is required for mapping AGB at most of the scales
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