Abstract
Mapping land-cover/land-use (LCLU) and estimating forest biomass using satellite images is a challenge given the diversity of sensors available and the heterogeneity of forests. Copernicus program served by the Sentinel satellites family and the Google Earth Engine (GEE) platform, both with free and open services accessible to its users, present a good approach for mapping vegetation and estimate forest biomass on a global, regional, or local scale, periodically and in a repeated way. The Sentinel-2 (S2) systematically acquires optical imagery and provides global monitoring data with high spatial resolution (10–60 m) images. Given the novelty of information on the use of S2 data, this chapter presents a review on LCLU maps and forest above-ground biomass (AGB) estimates, in addition to exploring the efficiency of using the GEE platform. The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates. The GEE platform is a promising tool for executing complex workflows of satellite data processing.
Highlights
In the last decades, remote sensing techniques have been applied in several studies of monitoring and classification of agricultural, forest, environmental, and socioeconomic resources [1–5]
This chapter is organized as follows: Section 2 present the satellites image classification and the potential of the Sentinel-2 satellites; Section 3 describes forest LCLU maps and the assessment of accuracy; Section 4 presents the Google Earth Engine (GEE) platform, advantages, and disadvantages; Section 5 describes the estimative of biomass via remote sensing; and Section 6 concludes with a platform performance overview and an outlook for the future
In tests with S2 images and the random forest (RF) for forest type mapping in the Mediterranean, Italy, using four vegetation indices (NDVI, SRI, RENDVI, and ARI1) in three phenological periods, Puletti et al [88] reported that the forest categories, had an overall accuracy of 86.2% and a Kappa coefficient of 0.86
Summary
Remote sensing techniques have been applied in several studies of monitoring and classification of agricultural, forest, environmental, and socioeconomic resources [1–5]. In the LCLU classification and forest biomass estimation studies, the proper selection of the sensor is crucial, given the variation of spatial, radiometric, spectral, and temporal resolutions available [9]. For these studies, the use of Sentinel-2 images and a free processing platform lack information about the advantages and disadvantages between different landscapes, classification methods, and biomass estimation models. This chapter is organized as follows: Section 2 present the satellites image classification and the potential of the Sentinel-2 satellites; Section 3 describes forest LCLU maps and the assessment of accuracy; Section 4 presents the GEE platform, advantages, and disadvantages; Section 5 describes the estimative of biomass via remote sensing; and Section 6 concludes with a platform performance overview and an outlook for the future
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