Assessment of selective logging impacts using UAV, Landsat, and Sentinel data in the Brazilian Amazon rainforest

Abstract

Several studies have assessed forest disturbance in tropical forests using Landsat imagery. However, the spatial resolution (30 m) of Landsat images has often been considered too coarse to accurately detect the extent and impacts of selective logging. The Sentinel-2 satellite launched in 2015 has been providing images at spatial resolutions of 10 to 20 m and those images have shown an improved potential for detecting forest disturbances in tropical regions. We compared Landsat-8 and Sentinel-2 imagery for detecting selective logging in a rain forest site in the Brazilian Amazon. The aerosol-free modified soil adjusted vegetation index (MSAVI_af) was retrieved from the satellite images acquired in August 2020 immediately following logging. A robust reference dataset of very-high-resolution imagery (0.5 m) acquired using a complementary metal oxide semiconductor sensor (visible bands) onboard of an unmanned aerial vehicle was used to image the area of interest and a map derived from it was used to assess the classification accuracies made using satellite-derived data. The overall accuracy of the classified Sentinel-2 and Landsat-8 images varied between 54% and 83%, depending on the applied classification parameters for distinguishing undisturbed from disturbed forest canopy. Images acquired using the UAV allowed us to detect subtle impacts of canopy openings by selective logging activities. Images acquired using the UAV allowed the detection of small canopy openings, but not Sentinel-2 or Landsat-8. Sentinel-2 provided more details of canopy disturbances than Landsat image. Our classification approach is fully implementable on the Google Earth Engine platform and is a promising technique to monitor selective logging impacts in tropical forests.

Bias correction of Sentinel-2 with unmanned aerial vehicle multispectral data for use in monitoring walnut fruit forest in western Tien Shan, Kyrgyzstan

“…55 This software uses the coordinates of exposure centers of each image performing aerial triangulation in each cell and reconstructs the photogrammetric blocks. 19 It also conducts automatic lightning adjustments of scenes based on the information from the UAV lightning sensor. These adjustments ensure data consistency among flights.…”

“…18 As selective logging and collection of nontimber forest products are widespread, it is imperative to develop a national or subnational system that accurately monitors forest canopy openings, intensity of use, and contributes to improved forest management to recover forest biomass (carbon stocks) and natural regeneration. 19 Remotely sensed data provide a great potential for analyzing Earth surface dynamics at various spatiotemporal scales, particularly in areas that are challenging to access. 20 Knowledge of spatial variability among and within forest parcels is a key factor for the users of walnut fruit forests to estimate yield and quality.…”

“…21 In this context, remote sensing (RS) has already shown its potential and effectiveness in spatiotemporal vegetation monitoring. 19,[22][23][24] Additionally, many satellite platforms (e.g., Landsat, MODIS, Aster, SPOT, Sentinel-1, and Sentinel-2) are now providing free datasets, thus promoting satellite imagery for many agricultural applications, 14,22,23,[25][26][27] including those with multisensor data fusion approachs. 28 For example, remotely sensed images from Sentinel-2 used in our research offer decametric resolution in terms of space and time, with a ground sample distance of up to 10 m and a revisit time of 6 days.…”

“…Recent studies applied UAVs in assessing forest condition, 19,[32][33][34] precision agriculture, 21,23,24,35 and mapping, modeling, and monitoring landslides. [36][37][38][39][40][41][42][43] Several approaches have been employed to calibrate satellite imagery with UAV multispectral data and field radiometric measurements.…”