Proximal sensor integration for land use classification and soil analysis in a coastal environment

Q1 Environmental Science
Sabrina Sharmeen Alam , Somsubhra Chakraborty , Fariha Chowdhury Jain , Shovik Deb , Rachna Singh , David C. Weindorf
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引用次数: 0

Abstract

Portable X-ray fluorescence (PXRF) and Nix Pro sensors are efficient tools for rapid in-situ soil analysis. This study combined PXRF and Nix Pro to classify land use and characterize soils from Sandwip Island, Bangladesh. Soil samples from agricultural, abandoned, and seashore areas were analyzed for EC, pH, organic carbon, and texture. Random forest model achieved 84 % classification accuracy, outperforming support vector machines (72 %). Significant soil salinity and management variations were noted, particularly in seashore areas. The findings highlight the potential of these sensors for sustainable soil monitoring, with future work needed to expand applicability to diverse regions and soil types.
近端传感器集成在海岸带土地利用分类和土壤分析中的应用
便携式x射线荧光(PXRF)和Nix Pro传感器是快速原位土壤分析的有效工具。本研究结合PXRF和Nix Pro对孟加拉国Sandwip岛的土地利用进行分类和土壤特征分析。来自农业、废弃和海滨地区的土壤样本进行了EC、pH、有机碳和质地的分析。随机森林模型的分类准确率达到84%,优于支持向量机(72%)。注意到显著的土壤盐度和管理变化,特别是在海滨地区。这些发现强调了这些传感器在可持续土壤监测方面的潜力,未来的工作需要扩大其在不同地区和土壤类型的适用性。
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来源期刊
Case Studies in Chemical and Environmental Engineering
Case Studies in Chemical and Environmental Engineering Engineering-Engineering (miscellaneous)
CiteScore
9.20
自引率
0.00%
发文量
103
审稿时长
40 days
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