Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remot...

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Autores principales:	Yingying Li, Zhengyong Zhao, Sunwei Wei, Dongxiao Sun, Qi Yang, Xiaogang Ding
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	forest soil prediction soil nutrients GF-1 satellite full-variable ANNs terrain-hydrology ANNs depth-specific soil Plant ecology QK900-989
Acceso en línea:	https://doaj.org/article/e1510fc1b1a34136af39dd6cb777e897
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spelling	oai:doaj.org-article:e1510fc1b1a34136af39dd6cb777e8972021-11-25T17:37:06ZPrediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data10.3390/f121114301999-4907https://doaj.org/article/e1510fc1b1a34136af39dd6cb777e8972021-10-01T00:00:00Zhttps://www.mdpi.com/1999-4907/12/11/1430https://doaj.org/toc/1999-4907The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (<i>R</i><sup>2</sup>), and less effective for AK at only 8% and 6% (<i>R</i><sup>2</sup>). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (<i>R</i><sup>2</sup>). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.Yingying LiZhengyong ZhaoSunwei WeiDongxiao SunQi YangXiaogang DingMDPI AGarticleforest soil predictionsoil nutrientsGF-1 satellitefull-variable ANNsterrain-hydrology ANNsdepth-specific soilPlant ecologyQK900-989ENForests, Vol 12, Iss 1430, p 1430 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	forest soil prediction soil nutrients GF-1 satellite full-variable ANNs terrain-hydrology ANNs depth-specific soil Plant ecology QK900-989
spellingShingle	forest soil prediction soil nutrients GF-1 satellite full-variable ANNs terrain-hydrology ANNs depth-specific soil Plant ecology QK900-989 Yingying Li Zhengyong Zhao Sunwei Wei Dongxiao Sun Qi Yang Xiaogang Ding Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data
description	The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (<i>R</i><sup>2</sup>), and less effective for AK at only 8% and 6% (<i>R</i><sup>2</sup>). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (<i>R</i><sup>2</sup>). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.
format	article
author	Yingying Li Zhengyong Zhao Sunwei Wei Dongxiao Sun Qi Yang Xiaogang Ding
author_facet	Yingying Li Zhengyong Zhao Sunwei Wei Dongxiao Sun Qi Yang Xiaogang Ding
author_sort	Yingying Li
title	Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data
title_short	Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data
title_full	Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data
title_fullStr	Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data
title_full_unstemmed	Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data
title_sort	prediction of regional forest soil nutrients based on gaofen-1 remote sensing data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e1510fc1b1a34136af39dd6cb777e897
work_keys_str_mv	AT yingyingli predictionofregionalforestsoilnutrientsbasedongaofen1remotesensingdata AT zhengyongzhao predictionofregionalforestsoilnutrientsbasedongaofen1remotesensingdata AT sunweiwei predictionofregionalforestsoilnutrientsbasedongaofen1remotesensingdata AT dongxiaosun predictionofregionalforestsoilnutrientsbasedongaofen1remotesensingdata AT qiyang predictionofregionalforestsoilnutrientsbasedongaofen1remotesensingdata AT xiaogangding predictionofregionalforestsoilnutrientsbasedongaofen1remotesensingdata
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Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data

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