Evaluating Ecological Condition Using Soil Biogeochemical Parameters and Near Infrared Reflectance Spectra

Rapid, repeatable assessment of ecological condition is critical for quantitative ecosystem monitoring. Soils provide a sensitive, integrative indicator for which sampling and analysis techniques are well defined. We evaluated soil properties as indicators of ecological condition (subjectively classified into minimally/moderately/severely degraded based on vegetative, hydrologic and edaphic cues) at 526 sites within Ft. Benning military installation (Georgia, USA). For each sample, we measured 17 biogeochemical parameters, and collected high-resolution diffuse reflectance spectra using visible/near infrared reflectance spectroscopy (VNIRS). VNIR spectra have been related to numerous soil attributes — we examine them here for diagnosing integrated response (i.e., ecological condition). We used ordinal logistic regression (OLR) and classification trees (CT) to discriminate between condition categories using both sets of predictors (biogeochemistry and spectra). Sixteen biogeochemical parameters were significantly different across condition categories; however, multivariate models greatly improved discrimination ([calibration, validation] accuracy of [69%, 66%] and [96%, 73%] for OLT and CT models, respectively). Important predictors included total C, total P, and Mehlich K/Ca/Mg. VNIR spectra further improved discrimination ([calibration, validation] accuracy of [74%, 70%] and [96%, 75%] for OLR and CT models, respectively). While spectra were comparably effective at discriminating minimally degraded sites, they were significantly more effective at discriminating severely degraded sites. Error rates across confounding factors suggest that watershed of origin and landscape position were the only important confounders, likely due to imbalanced sampling. We conclude that multivariate diagnosis improves accuracy, and that VNIR spectroscopy, which yields substantial cost and logistical improvements over conventional analyses, provides an effective tool for rapid condition diagnosis.     

Visible-near infrared reflectance spectroscopy for rapid, nondestructive assessment of wetland soil quality

Recent evidence supports using visible-near infrared reflectance spectroscopy (VNIRS) for sensing soil quality; advantages include low-cost, nondestructive, rapid analysis that retains high analytical accuracy for numerous soil performance measures. Research has primarily targeted agricultural applications (precision agriculture, performance diagnostics), but implications for assessing ecological systems are equally significant. Our objective was to extend chemometrics for sensing soil quality to wetlands. Hydric soils posed two challenges. First, wetland soils exhibit a wider range of organic matter concentrations, particularly in riparian areas where levels range from <1% in sedimentation zones to >90% in backwater floodplains; this may mute spectral responses from other soil fractions. Second, spectral inference of cation concentrations in terrestrial soils is for oxidized species; under reducing conditions in wetlands, oxidation state variability is observed, which strongly affects chroma. Riparian soils (n = 273) from western Florida exhibiting substantial target parameter variability were compiled. After minimal pre-processing, soils were scanned under artificial illumination using a laboratory spectrometer. A multivariate data mining technique (regression trees) was used to relate post-processed reflectance spectra to laboratory observations (pH, organic content, cation concentrations, total N, C, and P, extracellular enzyme activity). High validation accuracy was generally observed (r2(validation) > 0.8, RPD > 2.0, where RPD is the ratio of the standard deviation of an attribute to the observed standard error of validation); where accuracy was lower, categorical models (classification trees) successfully screened samples based on diagnostic functional thresholds (validation odds ratio > 10). Graphical models verified significant association between predictions and observations for all parameters, conditioning on biogeochemical covariates. Visible-near infrared reflectance spectroscopy offers both cost and statistical power advantages; hydric conditions do not appear to constrain application.