Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction
Increasing temperatures and drought occurrences recently led to soil moisture depletion and increasing tree mortality. In the interest of sustainable forest management, the monitoring of forest soil properties will be of increasing importance in the future. Vis-NIR spectroscopy can be used as fast,...
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/11/3927 |
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| author | Felix Thomas Rainer Petzold Carina Becker Ulrike Werban |
| author_facet | Felix Thomas Rainer Petzold Carina Becker Ulrike Werban |
| author_sort | Felix Thomas |
| collection | DOAJ |
| description | Increasing temperatures and drought occurrences recently led to soil moisture depletion and increasing tree mortality. In the interest of sustainable forest management, the monitoring of forest soil properties will be of increasing importance in the future. Vis-NIR spectroscopy can be used as fast, non-destructive and cost-efficient method for soil parameter estimations. Microelectromechanical system devices (MEMS) have become available that are suitable for many application fields due to their low cost as well as their small size and weight. We investigated the performance of MEMS spectrometers in the visual and NIR range to estimate forest soil samples total C and N content of Ah and Oh horizons at the lab. The results were compared to a full-range device using PLSR and Cubist regression models at local (2.3 ha, n: Ah = 60, Oh = 50) and regional scale (State of Saxony, Germany, 184,000 km<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>, n: Ah = 186 and Oh = 176). For each sample, spectral reflectance was collected using MEMS spectrometer in the visual (Hamamatsu C12880MA) and NIR (NeoSpetrac SWS62231) range and using a conventional full range device (Veris Spectrophotometer). Both data sets were split into a calibration (70%) and a validation set (30%) to evaluate prediction power. Models were calibrated for Oh and Ah horizon separately for both data sets. Using the regional data, we also used a combination of both horizons. Our results show that MEMS devices are suitable for C and N prediction of forest topsoil on regional scale. On local scale, only models for the Ah horizon yielded sufficient results. We found moderate and good model results using MEMS devices for Ah horizons at local scale (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>≥</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula> 0.71, RPIQ <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≥</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula> 2.41) using Cubist regression. At regional scale, we achieved moderate results for C and N content using data from MEMS devices in Oh (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>≥</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula> 0.57, RPIQ ≥ 2.42) and Ah horizon (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>≥</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula> 0.54, RPIQ <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≥</mo><mn>2</mn><mo>.</mo><mn>15</mn></mrow></semantics></math></inline-formula>). When combining Oh and Ah horizons, we achieved good prediction results using the MEMS sensors and Cubist (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>≥</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula> 0.85, RPIQ ≥ 4.69). For the regional data, models using data derived by the Hamamatsu device in the visual range only were least precise. Combining visual and NIR data derived from MEMS spectrometers did in most cases improve the prediction accuracy. We directly compared our results to models based on data from a conventional full range device. Our results showed that the combination of both MEMS devices can compete with models based on full range spectrometers. MEMS approaches reached between 68% and 105% of the corresponding full ranges devices <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> values. Local models tended to be more accurate than regional approaches for the Ah horizon. Our results suggest that MEMS spectrometers are suitable for forest soil C and N content estimation. They can contribute to improved monitoring in the future as their small size and weight could make in situ measurements feasible. |
| first_indexed | 2024-03-10T10:39:16Z |
| format | Article |
| id | doaj.art-a1dc1400b77f4580898f10b3d88c8f03 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T10:39:16Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a1dc1400b77f4580898f10b3d88c8f032023-11-21T23:07:14ZengMDPI AGSensors1424-82202021-06-012111392710.3390/s21113927Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties PredictionFelix Thomas0Rainer Petzold1Carina Becker2Ulrike Werban3Helmholtz Centre for Environmental Research—UFZ, Department Monitoring & Exploration Technologies, Permoser Straße 15, 04318 Leipzig, GermanyPublic Enterprise Sachsenforst, Unit Site Survey, Soil Monitoring and Laboratory, Bonnewitzer Straße 34, 01796 Pirna, GermanyPublic Enterprise Sachsenforst, Unit Site Survey, Soil Monitoring and Laboratory, Bonnewitzer Straße 34, 01796 Pirna, GermanyHelmholtz Centre for Environmental Research—UFZ, Department Monitoring & Exploration Technologies, Permoser Straße 15, 04318 Leipzig, GermanyIncreasing temperatures and drought occurrences recently led to soil moisture depletion and increasing tree mortality. In the interest of sustainable forest management, the monitoring of forest soil properties will be of increasing importance in the future. Vis-NIR spectroscopy can be used as fast, non-destructive and cost-efficient method for soil parameter estimations. Microelectromechanical system devices (MEMS) have become available that are suitable for many application fields due to their low cost as well as their small size and weight. We investigated the performance of MEMS spectrometers in the visual and NIR range to estimate forest soil samples total C and N content of Ah and Oh horizons at the lab. The results were compared to a full-range device using PLSR and Cubist regression models at local (2.3 ha, n: Ah = 60, Oh = 50) and regional scale (State of Saxony, Germany, 184,000 km<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>, n: Ah = 186 and Oh = 176). For each sample, spectral reflectance was collected using MEMS spectrometer in the visual (Hamamatsu C12880MA) and NIR (NeoSpetrac SWS62231) range and using a conventional full range device (Veris Spectrophotometer). Both data sets were split into a calibration (70%) and a validation set (30%) to evaluate prediction power. Models were calibrated for Oh and Ah horizon separately for both data sets. Using the regional data, we also used a combination of both horizons. Our results show that MEMS devices are suitable for C and N prediction of forest topsoil on regional scale. On local scale, only models for the Ah horizon yielded sufficient results. We found moderate and good model results using MEMS devices for Ah horizons at local scale (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>≥</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula> 0.71, RPIQ <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≥</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula> 2.41) using Cubist regression. At regional scale, we achieved moderate results for C and N content using data from MEMS devices in Oh (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>≥</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula> 0.57, RPIQ ≥ 2.42) and Ah horizon (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>≥</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula> 0.54, RPIQ <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≥</mo><mn>2</mn><mo>.</mo><mn>15</mn></mrow></semantics></math></inline-formula>). When combining Oh and Ah horizons, we achieved good prediction results using the MEMS sensors and Cubist (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>≥</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula> 0.85, RPIQ ≥ 4.69). For the regional data, models using data derived by the Hamamatsu device in the visual range only were least precise. Combining visual and NIR data derived from MEMS spectrometers did in most cases improve the prediction accuracy. We directly compared our results to models based on data from a conventional full range device. Our results showed that the combination of both MEMS devices can compete with models based on full range spectrometers. MEMS approaches reached between 68% and 105% of the corresponding full ranges devices <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> values. Local models tended to be more accurate than regional approaches for the Ah horizon. Our results suggest that MEMS spectrometers are suitable for forest soil C and N content estimation. They can contribute to improved monitoring in the future as their small size and weight could make in situ measurements feasible.https://www.mdpi.com/1424-8220/21/11/3927forest soilhumusproximal soil sensingvis-NIR spectroscopyMEMS-spectrometer |
| spellingShingle | Felix Thomas Rainer Petzold Carina Becker Ulrike Werban Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction Sensors forest soil humus proximal soil sensing vis-NIR spectroscopy MEMS-spectrometer |
| title | Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction |
| title_full | Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction |
| title_fullStr | Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction |
| title_full_unstemmed | Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction |
| title_short | Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction |
| title_sort | application of low cost mems spectrometers for forest topsoil properties prediction |
| topic | forest soil humus proximal soil sensing vis-NIR spectroscopy MEMS-spectrometer |
| url | https://www.mdpi.com/1424-8220/21/11/3927 |
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