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<p>Spectral indices emphasize pixels by combining spectral reflectance values from two or more spectral bands in multispectral imagery. These indices highlight features of interest by undergoing specific mathematical processing, often referred to as band math or map algebra. Many Digital Image Processing (DIP) software has tools to execute these indices automatically. ArcGIS Desktop, a widely used geospatial software, supports the reading and processing of satellite imagery. While it includes some tools for pre-processing and spectral index estimation, these are limited in scope comparted to dedicated DIP software and require extensive manual setup for repetitive tasks. As of today, these limitations remain a gap in ArcGIS Desktop’s capabilities. To address this gap, the present study develops a semi-automated approach for generating multiple spectral indices from multiband satellite imageries with minimal user intervention. The ArcIndices toolbox organizes different spectral indices into categories as script tools under a toolset. Each interactive script tool is programmed using ArcPy (Python site package for ArcGIS) to execute a sequential procedure of steps within the ArcGIS environment. Users can invoke these tools as functions in other Python scripts, Model Builder workflows, or even as geoprocessing services on ArcGIS Server for cloud-based operations. The tools are validated and tested using Landsat 8 Operational Land Imager (OLI) image bands across multiple study areas, with the accuracy of outputs confirmed by comparing the results with expected index ranges reported in scientific literature. Performance evaluation of the developed tools highlights their advantages over manual workflows using the ArcGIS Raster Calculator. Comparative measurements presented in this study show that the automated script tools significantly improve performance, achieving a 100% reduction in human errors and reducing processing times by up to 100% for most indices. These improvements make the tools not only faster and more accurate but also easier to use for both novice and expert users. The ArcIndices Toolbox bridges the gap between manual and automated workflows, addressing current limitations in ArcGIS Desktop for spectral index estimation and extending its functionality for modern geospatial analysis.</p>

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ArcIndices: a toolbox for computing spectral indices from multiband satellite imagery in ArcGIS environment

  • 期刊论文

  • 发表日期：

    2025年11月15日

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  • Rajaperumal Ramamoorthy,
  • Arunachalam Manimozhian,
  • Ajith Joseph Kochuparampil,
  • Saravanavel Jeyaraman,
  • Palanivel Kathiresan

摘要

Spectral indices emphasize pixels by combining spectral reflectance values from two or more spectral bands in multispectral imagery. These indices highlight features of interest by undergoing specific mathematical processing, often referred to as band math or map algebra. Many Digital Image Processing (DIP) software has tools to execute these indices automatically. ArcGIS Desktop, a widely used geospatial software, supports the reading and processing of satellite imagery. While it includes some tools for pre-processing and spectral index estimation, these are limited in scope comparted to dedicated DIP software and require extensive manual setup for repetitive tasks. As of today, these limitations remain a gap in ArcGIS Desktop’s capabilities. To address this gap, the present study develops a semi-automated approach for generating multiple spectral indices from multiband satellite imageries with minimal user intervention. The ArcIndices toolbox organizes different spectral indices into categories as script tools under a toolset. Each interactive script tool is programmed using ArcPy (Python site package for ArcGIS) to execute a sequential procedure of steps within the ArcGIS environment. Users can invoke these tools as functions in other Python scripts, Model Builder workflows, or even as geoprocessing services on ArcGIS Server for cloud-based operations. The tools are validated and tested using Landsat 8 Operational Land Imager (OLI) image bands across multiple study areas, with the accuracy of outputs confirmed by comparing the results with expected index ranges reported in scientific literature. Performance evaluation of the developed tools highlights their advantages over manual workflows using the ArcGIS Raster Calculator. Comparative measurements presented in this study show that the automated script tools significantly improve performance, achieving a 100% reduction in human errors and reducing processing times by up to 100% for most indices. These improvements make the tools not only faster and more accurate but also easier to use for both novice and expert users. The ArcIndices Toolbox bridges the gap between manual and automated workflows, addressing current limitations in ArcGIS Desktop for spectral index estimation and extending its functionality for modern geospatial analysis.