High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão

Abstract

Semi-arid ecosystems play a key role in global agricultural production, seasonal carbon cycle dynamics, and longer-run climate change. Because semi-arid landscapes are heterogeneous and often sparsely vegetated, repeated and large-scale ecosystem assessments of these regions have to date been impossible. Here, we assess the potential of high-spatial resolution visible band imagery for semi-arid ecosystem mapping. We use WorldView satellite imagery at 0.3-0.5 m resolution to develop a reference data set of nearly 10,000 labeled examples of three classes-trees, shrubs/grasses, and bare land-across 1000 km 2 of the semi-arid Sertão region of northeast Brazil. Using Google Earth Engine, we show that classification with low-spectral but high-spatial resolution input (WorldView) outperforms classification with the full spectral information available from Landsat 30 m resolution imagery as input. Classification with high spatial resolution input improves detection of sparse vegetation and distinction between trees and seasonal shrubs and grasses, two features which are lost at coarser spatial (but higher spectral) resolution input. Our total tree cover estimates for the study area disagree with recent estimates using other methods that may underestimate treecover because they confuse trees with seasonal vegetation (shrubs and grasses). This distinction is important for monitoring seasonal and long-run carbon cycle and ecosystem health. Our results suggest that newer remote sensing products that promise high frequency global coverage at high spatial but lower spectral resolution may offer new possibilities for direct monitoring of the world's semi-arid ecosystems, and we provide methods that could be scaled to do so.

Hydrothermal areas, microbial mats and sea grass in Paleochori Bay, Milos, Greece

“…The aerial imagery ( Figure 2) was classified using a supervised classification in ArcGIS (Main Map). This approach has been tried in the past by several studies, especially for monitoring vegetation health and land cover classification (Dell et al, 2019;Goldblatt et al, 2017;Mattupalli et al, 2018;Upadhyay et al, 2016). The maximum likelihood classification rule was used in the classification of the white areas.…”

Flood Prevention and Emergency Response System Powered by Google Earth Engine

“…After the release of Google Earth API in 2008, FPERS and many other web-based geospatial systems were developed and powered by GEE to support a variety of management decisions [16][17][18]. As experiences were gained and systems were improved gradually, innovative and reliable services were created and implemented.…”

Google Earth Engine Applications

“…Missing data due to clouds was filled by using a climate driven modelling approach and data was produced at multiple scales [5]. Using Google Engine, an ecosystem assessment study in a Brazilian semi-arid landscape showed that high spatial resolution data (Worldview) could yield higher classification accuracy compared to medium resolution Landsat TM, with full spectral resolution information [6]. Trees, shrubs, and bare land were classified, with a clear distinction between trees and shrubs, a mammoth task using prior data sets.…”