CO2Flux Model Assessment and Comparison between an Airborne Hyperspectral Sensor and Orbital Multispectral Imagery in Southern Amazonia

Abstract

In environmental research, remote sensing techniques are mostly based on orbital data, which are characterized by limited acquisition and often poor spectral and spatial resolutions in relation to suborbital sensors. This reflects on carbon patterns, where orbital remote sensing bears devoted sensor systems for CO2 monitoring, even though carbon observations are performed with natural resources systems, such as Landsat, supported by spectral models such as CO2Flux adapted to multispectral imagery. Based on the considerations above, we have compared the CO2Flux model by using four different imagery systems (Landsat 8, PlanetScope, Sentinel-2, and AisaFenix) in the northern part of the state of Mato Grosso, southern Brazilian Amazonia. The study area covers three different land uses, which are primary tropical forest, bare soil, and pasture. After the atmospheric correction and radiometric calibration, the scenes were resampled to 30 m of spatial resolution, seeking for a parametrized comparison of CO2Flux, as well as NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index). The results obtained here suggest that PlanetScope, MSI/Sentinel-2, OLI/Landsat-8, and AisaFENIX can be similarly scaled, that is, the data variability along a heterogeneous scene in evergreen tropical forest is similar. We highlight that the spatial-temporal dynamics of rainfall seasonality relation to CO2 emission and uptake should be assessed in future research. Our results provide a better understanding on how the merge and/or combination of different airborne and orbital datasets that can provide reliable estimates of carbon emission and absorption within different terrestrial ecosystems in southern Amazonia.

Maize Crop Detection through Geo-Object-Oriented Analysis Using Orbital Multi-Sensors on the Google Earth Engine Platform

“…The PlanetScope imagery, despite its high spatial resolution, reached inferior overall results compared to OLI/Landsat-8 [33]. This result is probably related to the lower temporal and spectral resolution of this dataset and to the training points being collected in a concentrated manner and not spread over the entire area of interest [56]. This combination of factors did not produce enough information to adequately differentiate the classes, suggesting that the spatial resolution of 4.77 m did not provide enough textural information for the effective separation of the classes studied.…”

Dynamics of major environmental disasters involving fire in the Brazilian Pantanal

“…The dynamics of carbon sequestration in the Pantanal biome were evaluated over the years of the time series. For this purpose, the CO 2 flux index model was used 73 , 74 . The purpose of this model is to measure the efficiency of the carbon sequestration process by vegetation, i.e., the photosynthetic rate during the photosynthesis process.…”

Changes in Carbon Dioxide Balance Associated with Land Use and Land Cover in Brazilian Legal Amazon Based on Remotely Sensed Imagery

“…This is a remote sensing-based empirical model based on the Normalized Difference Vegetation Index (NDVI) (Equation ( 1)) and the scaled Photochemical Reflectance Index (sPRI) (Equation ( 2)) vegetation indices. Negative values represent a CO 2 sink, while positive values represent a CO 2 source [18,34,42,61]. While NDVI reveals the vigor of photosynthetically active vegetation, in which it may be able to absorb carbon [34,42], the sPRI estimates the carotenoid pigments of the leaves, indicating their rate of carbon dioxide storage [41].…”

“…Negative values represent a CO 2 sink, while positive values represent a CO 2 source [18,34,42,61]. While NDVI reveals the vigor of photosynthetically active vegetation, in which it may be able to absorb carbon [34,42], the sPRI estimates the carotenoid pigments of the leaves, indicating their rate of carbon dioxide storage [41]. For the calculation of NDVI, spectral bands 1 (620-670 nm) for red and 2 (841-876 nm) for NIR were used, based on the product MOD09A1 (Equation ( 1)).…”

“…Remote sensing techniques are often used to better understand land use and land cover changes, as they reduce the cost and time necessary to detect environmental degradation [38][39][40] and climate and carbon fluxes, as extensively covered in the recent literature [18,20,21,34,[41][42][43]. Remote sensing has recently been applied to updating the boundaries between the Brazilian Cerrado-Amazon biomes, due to land use and land cover changes, by using vegetation indices such as Gross Primary Productivity (GPP) and carbon dioxide fluxes (CO 2 Flux), allowing the development of public policies to curb deforestation and degradation by anthropogenic activities [44].…”