%0 Articles
%T Mapping of growing stock and stand delineation for tropical forests using remote sensing
%A Hou, Zhengyang
%D 2014
%J Dissertationes Forestales
%V 2014
%N 184
%R doi:10.14214/df.184
%U http://dissertationesforestales.fi/article/1966
%X This package aims to advance remote-sensing-based mapping of growing stock and stand delineation for tropical forests in an attempt to respond to the call for the methodological development of forest inventory by the collaborative initiatives on Reducing Emissions from Deforestation and forest Degradation (REDD) and on sustainable forest management by REDD +. Tropical forests in Laos were taken as the study area, and remote sensing materials were collected from ALOS AVNIR-2, airborne colour infrared (CIR) photography, and airborne laser scanning (ALS).

In Study I, the relative efficacy of these three types of remote sensing materials was evaluated for mapping stem volume and basal area based on established methodologies that were originally developed for boreal forests. The results showed that ALS data processed with the conventional area-based approach (ABA) outperformed optical data in mapping of the stem volume (RMSE 36.9%) and basal area (RMSE 47.3%). Airborne CIR was built, with models performing slightly better than models based on ALOS AVNIR-2, although both remained at a similar level of accuracy and fell considerably behind ALS. In general, boreal methodologies proved effective for tropical forests, but the efficacy was far lower than that achieved in boreal conditions.

In Study II, the focus was therefore put on how to adapt the conventional ABA to the tropics, where forest structures are much more diverse and complex. The adaptation relied on applying global or plot-adaptive cut-off thresholding to filter and denoise the raw normalized point cloud. By doing so, information on structural variability was enhanced. The results showed that the adapted ABA effectively improved to a new level the predictability of stem volume compared with the conventional ABA adopted in Study I. The thresholding height of the optimal global cut-off for filtering was detected at 3.6 m and the correspondingly extracted features helped to improve the RMSE by about 7% compared to the conventional ABA. The plot-adaptive cut-off thresholding further improved it by nearly another 2% compared to the optimal global cut-off height.

In Study III, an empirical model-based segmentation approach was developed to extract forest stands of tropical forests from remote sensing materials and empirical models derived in Study I. The results showed that the homogeneity of the delineated stands mostly conformed to the quality of the corresponding empirical models obtained in Study I. With the cost-effectiveness of the tested remote-sensing materials corresponding well to the three-tier standard of IPCC, forest attributes used for segmentation can be generalized to any variable retrievable from an empirical model, such as stem volume, net present value of economic returns, amount of biomass, or even carbon stock for REDD +.