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Spatial resolution effects of remote sensing images on digital soil models in aquatic ecosystems
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kim, Jongsung Grunwald, Sabine Osborne, Todd Z. Robbins, R. Yamataki, Hajime Rivero, Rebeca E. |
| Copyright Year | 2012 |
| Abstract | The incorporation of remote sensing (RS) data into digital soil models has shown success to improve soil predictions. However, the effects of multi-resolution imagery on modeling of biogeochemical soil properties in aquatic ecosystems are still poorly understood. The objectives of this study were to (i) develop prediction models for various soil properties (total phosphorus, nitrogen, and carbon) utilizing RS images and environmental ancillary data and (ii) elucidate the effect of different spatial resolutions of RS images on inferential modeling of soil properties in a subtropical wetland: Water Conservation Area-2A, the Florida Everglades, USA. Soil cores were collected (n=108) from the top 10 cm. The spectral data and derived indices from remote sensing images, which have different spatial resolutions, included: MODIS (250 m), Landsat ETM+ (30 m), and SPOT (10 m). Block kriging (BK) and random forests (RF) modeling approaches were compared using a leave-one-out cross-validation method. The RF using RS images derived input variables showed accurate prediction results (> 89%) when compared to BK. Results suggest that the spectral data derived from RS images can improve the predictive quality of soil properties in aquatic ecosystem. However, there was no noticeable distinction among different spatial resolutions of RS images to develop prediction models of soil properties. Figure 1. Location of the Water Conservation Area-2A within the Everglades in Florida, U.S. and soil core sampling points. A total of 108 soil cores were collected in July and October 2009 and mid March 2010 using airboats, to a depth of 10 cm beneath the soil surface based on a random-stratified sampling design described in Rivero et al. (2007). Sampling locations within the study area are shown in Figure 1. The soil samples were analyzed for (i) TP utilizing association of official analytical chemists (AOAC) 978.01 method, (ii) TN utilizing LECO combustion method, and (iii) TC utilizing dry combustion method using a Shimadzu SSM-5000a. 2.2 Spectral/geospatial environmental ancillary data and modeling approach Three satellite images were selected to investigate the effects of different spatial resolutions: MODIS, Landsat ETM+, and SPOT 5 images. The MODIS image (February 2010) obtained from Land Processes Distributed Active Archive Center, U.S. Geological Survey Earth Resources Observation and Science (EROS) Center has blue, red, near-infrared (NIR), and mid-infrared (MIR) bands with 250 m spatial resolution. The Landsat ETM+ image (February 2010) obtained from the USGS EROS Center has blue, green, red, NIR, and two MIR bands with 30 m and a thermal infrared band with 60 m spatial resolution. The SPOT image (January 2009) donated by Planet Action, a non-profit ASTRIUM GEO initiative, has green, red, NIR, and shortwave-infrared (SWIR) bands with 10 m spatial resolution. All images (MODIS, Landsat ETM+, and SPOT) were projected to the Universal Transverse Mercator (UTM) map projection (Zone: 17; Datum: World Geographic System, WGS 84) and geometircally rectified with USGS digital orthophoto quadrangles (DOQQ) using ERDAS Imagine 2010 (Earth Resource Data Analysis System Inc., Atlanta, GA). The root mean square error (RMSE) was less than 0.5 pixel for all images. The derived spectral vegetation indices for different satellite images depending on their spectral bands were the following: (i) from MODIS – Enhanced Vegetation Index (EVI), Moisture Stress Index (MSI), Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), and Transformed Vegetation Index (TVI); (ii) from Landsat ETM+ – Midinfrared index (MidIR), MSI, NDVI, Normalized Difference Vegetation Green Index (NDVI green), Normalized difference water index (NDWI), Reduced simple ratio (RSR), SR, and TVI; (iii) from SPOT – MSI, NDVI, NDVI green, NDWI, RSR, SR, and TVI. The formulas to derive indices are given in Table 1. Table 1. Summary of remote sensing spectral indices. Indices† Formula References EVI‡ ) 1 ( L Blue 2 Red 1 NIR Red NIR L C C G Huete et al., 1997 Midinfrared index Band7 Landsat MidIR Band5 Landsat MidIR Musick and Pelletier, 1988 MSI NIR MidIR Rock et al., 1986 NDVI Red NIR Red NIR Rouse et al., 1974 NDVI green Green NIR Green NIR Gitelson et al., 1996 NDWI SWIR NIR SWIR NIR |
| Starting Page | 121 |
| Ending Page | 125 |
| Page Count | 5 |
| File Format | PDF HTM / HTML |
| DOI | 10.1201/b12728-25 |
| Alternate Webpage(s) | http://ufgrunwald.com/wp-content/uploads/2016/05/Kim-et-al-2012-Digital-Soil-Assessments-and-Beyond-Book.pdf |
| Alternate Webpage(s) | http://ufgrunwald.com/wp-content/uploads/2016/09/Kim-et-al-2012.-DSM-Sydney..pdf |
| Alternate Webpage(s) | http://soils.ifas.ufl.edu/faculty/grunwald/home/PDFs/Jongsung%20Kim_publication%20II%20-%20Textbook%20DSM.pdf |
| Alternate Webpage(s) | https://doi.org/10.1201/b12728-25 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
