Abstract
Knowledge of the spatial patterns of successional stages (i.e., primary and secondary forest) in tropical forests allows to monitor forest preservation, mortality and regeneration in relation to natural and anthropogenic disturbances. Different successional stages have also different capabilities of re-establishing carbon stocks. Therefore, a successful discrimination of successional stages over wide areas can lead to an improved quantification of above ground biomass and carbon stocks. The reduction of the mapping uncertainties is especially a challenge due to high heterogeneity of the tropical vegetation. In this framework, the development of innovative remote sensing approaches is required. Forests (top) height (and its spatial distribution) are an important structural parameter that can be used to differentiate between different successional stages, and can be provided by Interferometric Synthetic Aperture Radar (InSAR) acquisitions. In this context, this paper investigates the potential of forest heights estimated from TanDEM-X InSAR data and a LiDAR digital terrain model (DTM) for separating successional stages (primary or old growth and secondary forest at different stages of succession) by means of a maximum likelihood classification. The study was carried out in the region of the Tapajós National Forest (Pará, Brazil) in the Amazon biome. The forest heights for three years (2012, 2013 and 2016) were estimated from a single-polarization in bistatic mode using InSAR model-based inversion techniques aided by the LiDAR digital terrain model. The validation of the TanDEM-X forest heights with independent LiDAR H100 datasets was carried out in the location of seven field inventory plots (measuring 50 × 50 m, equivalent to 0.25 ha), also allowing for the validation of the LiDAR datasets against the field data. The validation of the estimated heights showed a high correlation (r = 0.93) and a low uncertainty (RMSE = 3 m). The information about the successional stages and forest heights from field datasets was used to select training samples in the LiDAR and TanDEM-X forest heights to classify successional stages with a maximum likelihood classifier. The identification of different stages of forest succession based on TanDEM-X forest heights was possible with an overall accuracy of about 80%.
Highlights
Many tropical forest regions are a mosaic made up of large areas of primary forest and degraded forest patches, the latter for example created by logging, fire or timber harvesting
Notice that in (1) the dependence on the range – azimuth coordinate has been dropped for notation simplicity, the dependence of the coherence on the baseline through the vertical Interferometric Synthetic Aperture Radar (InSAR) wavenumber κZ has been explicitly included on the left-hand side. κZ expresses the sensitivity of the phase difference between the two acquisitions with respect to the height in the volume
The validation of TanDEM-X interferometric heights showed a high performance with a low uncertainty with respect to the Light Detection and Ranging (LiDAR) H100 height
Summary
Many tropical forest regions are a mosaic made up of large areas of primary forest and degraded forest patches, the latter for example created by logging, fire or timber harvesting. Depending on the length of regeneration and its history in terms of land-use, a secondary forest can be classified into three different successional stages (Mesquita et al, 2001; Araújo et al, 2005; Chazdon et al, 2007; Salomão et al, 2012), namely initial, intermediate and advanced stages. These stages are characterized by different forest structure patterns and species compositions (Lu et al, 2003; Chazdon et al, 2007; Silva et al, 2016)
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