Incorporation of spectral data into multivariate geostatistical models to map soil phosphorus variability in a Florida wetland

Rivero, Rosanna G.; Grunwald, Sabine; Bruland, Gregory L.

doi:10.1016/j.geoderma.2007.04.026

Cited by 48 publications

(31 citation statements)

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“…To improve the mapping process, researchers have developed equipment to provide soil information in real time at lower costs (Viscarra Rossel et al, 2009). Since the 1980s, numerous initiatives have evaluated the potential of spectra in obtaining quantitative soil data and, in many cases, the results were promising (Rivero et al, 2007;Ben-Dor et al, 2008;Viscarra Rossel et al, 2009). Several researchers (Moran et al, 1997;Ben-Dor et al, 1999) have recommended the development of methods using optical sensors, which may lend support to both laboratory processes and routine fieldwork.…”

Section: Introductionmentioning

confidence: 99%

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Rizzo¹,

Demattê

Terra

2014

Rev. Bras. Ciênc. Solo

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Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

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Section: Introductionmentioning

confidence: 99%

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Rizzo¹,

Demattê

Terra

2014

Rev. Bras. Ciênc. Solo

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“…Th us, major advancements to quantify soil properties and processes are highly dependent on improvements in inferential delineation of soil observations and less so on improved methodologies or models. It has been demonstrated in various studies that soil predictions can be made at ≤30-m pixel resolutions (Th ompson and Kolka, 2005;Grunwald et al, 2007;Rivero et al, 2007;Vasques et al, 2010a) but within limited geographic domains, i.e., smaller study areas. …”

Section: Digital Soil Mapping Projected Into the Futurementioning

confidence: 99%

Digital Soil Mapping and Modeling at Continental Scales: Finding Solutions for Global Issues

Grunwald

Thompson

Boettinger

2011

Soil Science Soc of Amer J

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260

153

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SSSA 75th Anniversary PaperT he Big Bang, the point in space and time from which all matter and energy in the universe supposedly emanated, is thought to have occurred sometime around 13.7 billion yr ago (Bloom, 2010). During the past 4.5 billion yr since the Earth coalesced and cooled, the planet has undergone alterations and transformations via (for example) plate tectonics, volcanism, and orogenies, which spawned severe changes in the composition and structure of the atmosphere, the oceans, and the land surface-including the biosphere and pedosphere. Other major natural forcing factors have included solar processes and orbital and galactic variations, which changed the amount of solar energy the Earth received (Intergovernmental Panel on Climate Change, 2007). Solar insolation and the structure and composition of the Earth's surface drive many ecosystem processes that have formed the soils we observe on the landscapes of today. VALUE OF SOILS IN THE ANTHROPOCENEDuring the last three centuries, human actions have produced profound shift s in the Earth system, becoming the main driver of global environmental change.

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“…The landscape is composed of dominant sawgrass marshes, shrub and tree island communities, and invasive cattail communities (van der Valk and Rosburg 1997). WCA2A has been used extensively as a research site by the SFWMD, with extensive trial and monitoring programs for a number of biogeochemical components, especially soil phosphorus and vegetative structure (Rivero et al 2007a). The triangular mesh grid used for simulation is also displayed in Figure 1, with the green border cells used for numerical stability of the hydrological RSM component.…”

Section: Test Sitementioning

confidence: 99%

A spatially distributed, deterministic approach to modeling Typha domingensis (cattail) in an Everglades wetland

et al. 2012

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Introduction: The emergent wetland species Typha domingensis (cattail) is a native Florida Everglades monocotyledonous macrophyte. It has become invasive due to anthropogenic disturbances and is out-competing other vegetation in the region, especially in areas historically dominated by Cladium jamaicense (sawgrass). There is a need for a quantitative, deterministic model in order to accurately simulate the regional-scale cattail dynamics in the Everglades. Methods: The Regional Simulation Model (RSM), combined with the Transport and Reaction Simulation Engine (TARSE), was adapted to simulate ecology. This provides a framework for user-defineable equations and relationships and enables multiple theories with different levels of complexity to be tested simultaneously. Five models, or levels, of increasing complexity were used to simulate cattail dynamics across Water Conservation Area 2A (WCA2A), which is located just south of Lake Okeechobee, in Florida, USA. These levels of complexity were formulated to correspond with five hypotheses regarding the growth and spread of cattail. The first level of complexity assumed a logistic growth pattern to test whether cattail growth is density dependent. The second level of complexity built on the first and included a Habitat Suitability Index (HSI) factor influenced by water depth to test whether this might be an important factor for cattail expansion. The third level of complexity built on the second and included an HSI factor influenced by soil phosphorus concentration to test whether this is a contributing factor for cattail expansion. The fourth level of complexity built on the third and included an HSI factor influenced by (a level 1-simulated) sawgrass density to determine whether sawgrass density impacted the rate of cattail expansion. The fifth level of complexity built on the fourth and included a feedback mechanism whereby the cattail densities influenced the sawgrass densities to determine the impact of inter-species interactions on the cattail dynamics. Results: All the simulation results from the different levels of complexity were compared to observed data for the years 1995 and 2003. Their performance was analyzed using a number of different statistics that each represent a different perspective on the ecological dynamics of the system. These statistics include box-plots, abundance-area curves, Moran's I, and classified difference. The statistics were summarized using the Nash-Sutcliffe coefficient. The results from all of these comparisons indicate that the more complex level 4 and level 5 models were able to simulate the observed data with a reasonable degree of accuracy.

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Incorporation of spectral data into multivariate geostatistical models to map soil phosphorus variability in a Florida wetland

Cited by 48 publications

References 35 publications

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Digital Soil Mapping and Modeling at Continental Scales: Finding Solutions for Global Issues

A spatially distributed, deterministic approach to modeling Typha domingensis (cattail) in an Everglades wetland

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