Sensor mapping of Amazonian Dark Earths in deforested croplands

Amazonian Dark Earths (ADEs) are fertile soils for agricultural production as well as important archaeological resources for understanding the pre-Columbian past of the Neotropical lowland rainforest. ADEs are threatened by expanding land exploitation and there is a need to develop efficient approac...

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Main Authors: Söderström, Mats, Eriksson, Jan, Isendahl, Christian, Pahl Schaand, Denise, Stenborg, Per, Rebellato, Lilian, Piikki, Kristin
Format: Journal Article
Language:Inglés
Published: Elsevier 2016
Subjects:
Online Access:https://hdl.handle.net/10568/76298
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author Söderström, Mats
Eriksson, Jan
Isendahl, Christian
Pahl Schaand, Denise
Stenborg, Per
Rebellato, Lilian
Piikki, Kristin
author_browse Eriksson, Jan
Isendahl, Christian
Pahl Schaand, Denise
Piikki, Kristin
Rebellato, Lilian
Stenborg, Per
Söderström, Mats
author_facet Söderström, Mats
Eriksson, Jan
Isendahl, Christian
Pahl Schaand, Denise
Stenborg, Per
Rebellato, Lilian
Piikki, Kristin
author_sort Söderström, Mats
collection Repository of Agricultural Research Outputs (CGSpace)
description Amazonian Dark Earths (ADEs) are fertile soils for agricultural production as well as important archaeological resources for understanding the pre-Columbian past of the Neotropical lowland rainforest. ADEs are threatened by expanding land exploitation and there is a need to develop efficient approaches to soil mapping and analysis for documenting these soils. In this paper we assess the potential of satellite remote sensing and proximal soil sensing to map, predict and monitor ADEs in land affected by agro-industrial development. We use instruments based on portable x-ray fluorescence (PXRF) and electromagnetic induction (EMI) as well as high-resolution satellite data (Spot 6) for detailed soil surveys at a 10-ha ADE site now mainly used for soybean production on the Belterra Plateau, Pará, Brazil. We predict the regional occurrence of ADE in a c. 250 km2 test area centred on the known ADE site São Francisco using satellite data. Multivariate adaptive regression splines models were parameterised for predictions of soil organic carbon (SOC), cation exchange capacity (CEC), phosphorus (P) and depth of the A horizon in ADEs from sensor data – both from individual sensors and in sensor combinations. Combining sensors gave the best validation results: the highest modelling efficiencies (E) were 0.70 (SOC), 0.88 (CEC) and 0.74 (for both P and A depth). The most powerful single proximal sensor outputs in the predictions were Sr from the PXRF data and magnetic susceptibility (MSa) as measured by the EMI instrument. In the regional satellite based model we located 17 previously unrecorded ADE sites > 2 ha. Ground control checks showed that 10 out of 11 sites were correctly classified. We conclude that these sensors are useful in studies of ADE in deforested cropland and provide new opportunities for detailed studies of the archaeological record.
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spelling CGSpace762982025-03-13T09:44:18Z Sensor mapping of Amazonian Dark Earths in deforested croplands Söderström, Mats Eriksson, Jan Isendahl, Christian Pahl Schaand, Denise Stenborg, Per Rebellato, Lilian Piikki, Kristin cartography remote sensing soil Amazonian Dark Earths (ADEs) are fertile soils for agricultural production as well as important archaeological resources for understanding the pre-Columbian past of the Neotropical lowland rainforest. ADEs are threatened by expanding land exploitation and there is a need to develop efficient approaches to soil mapping and analysis for documenting these soils. In this paper we assess the potential of satellite remote sensing and proximal soil sensing to map, predict and monitor ADEs in land affected by agro-industrial development. We use instruments based on portable x-ray fluorescence (PXRF) and electromagnetic induction (EMI) as well as high-resolution satellite data (Spot 6) for detailed soil surveys at a 10-ha ADE site now mainly used for soybean production on the Belterra Plateau, Pará, Brazil. We predict the regional occurrence of ADE in a c. 250 km2 test area centred on the known ADE site São Francisco using satellite data. Multivariate adaptive regression splines models were parameterised for predictions of soil organic carbon (SOC), cation exchange capacity (CEC), phosphorus (P) and depth of the A horizon in ADEs from sensor data – both from individual sensors and in sensor combinations. Combining sensors gave the best validation results: the highest modelling efficiencies (E) were 0.70 (SOC), 0.88 (CEC) and 0.74 (for both P and A depth). The most powerful single proximal sensor outputs in the predictions were Sr from the PXRF data and magnetic susceptibility (MSa) as measured by the EMI instrument. In the regional satellite based model we located 17 previously unrecorded ADE sites > 2 ha. Ground control checks showed that 10 out of 11 sites were correctly classified. We conclude that these sensors are useful in studies of ADE in deforested cropland and provide new opportunities for detailed studies of the archaeological record. 2016-11 2016-07-27T17:05:55Z 2016-07-27T17:05:55Z Journal Article https://hdl.handle.net/10568/76298 en Open Access Elsevier Söderström, Mats; Eriksson, Jan; Isendahl, Christian; Pahl Schaand, Denise; Stenborg, Per; Rebellato, Lilian; Piikki, Kristin. 2016. Sensor mapping of Amazonian Dark Earths in deforested croplands . Geoderma 281: 58-68.
spellingShingle cartography
remote sensing
soil
Söderström, Mats
Eriksson, Jan
Isendahl, Christian
Pahl Schaand, Denise
Stenborg, Per
Rebellato, Lilian
Piikki, Kristin
Sensor mapping of Amazonian Dark Earths in deforested croplands
title Sensor mapping of Amazonian Dark Earths in deforested croplands
title_full Sensor mapping of Amazonian Dark Earths in deforested croplands
title_fullStr Sensor mapping of Amazonian Dark Earths in deforested croplands
title_full_unstemmed Sensor mapping of Amazonian Dark Earths in deforested croplands
title_short Sensor mapping of Amazonian Dark Earths in deforested croplands
title_sort sensor mapping of amazonian dark earths in deforested croplands
topic cartography
remote sensing
soil
url https://hdl.handle.net/10568/76298
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