Premium
Spatial Gradients of Ecosystem Health Indicators across a Human‐Impacted Semiarid Savanna
Author(s) -
Vågen TorGunnar,
Winowiecki Leigh Ann,
Twine Wayne,
Vaughan Karen
Publication year - 2018
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq2017.07.0300
Subject(s) - edaphic , environmental science , topsoil , soil carbon , soil health , ecosystem , soil science , hydrology (agriculture) , soil organic matter , physical geography , soil water , ecology , geography , geology , geotechnical engineering , biology
Drivers of soil organic carbon (SOC) dynamics involve a combination of edaphic, human, and climatic factors that influence and determine SOC distribution across the landscape. High‐resolution maps of key indicators of ecosystem health can enable assessments of these drivers and aid in critical management decisions. This study used a systematic field‐based approach coupled with statistical modeling and remote sensing to develop accurate, high‐resolution maps of key indicators of ecosystem health across savanna ecosystems in South Africa. Two 100‐km 2 landscapes in Bushbuckridge Local Municipality were surveyed, and 320 composite topsoil samples were collected. Mid‐infrared spectroscopy was used to predict soil properties, with good performance for all models and root mean squared error of prediction (RMSEP) values of 1.3, 0.2, 5, and 3.6 for SOC, pH, sand, and clay, respectively. Validation results for the mapping of soil erosion prevalence and herbaceous cover using RapidEye imagery at 5‐m spatial resolution showed good model performance with area under the curve values of 0.80 and 0.86, respectively. The overall (out‐of‐bag) random forest model performance for mapping of soil properties, reported using R 2 , was 0.8, 0.77, and 0.82 for SOC, pH, and sand, respectively. Calibration model performance was good, with RMSEP values of 2.6 g kg −1 for SOC, 0.2 for pH, and 6% for sand content. Strong gradients of increasing SOC and pH corresponded with decreasing sand content between the study sites. Although both sites had low SOC overall, important driving factors of SOC dynamics included soil texture, soil erosion prevalence, and climate. These data will inform strategic land management decisions focused particularly on improving ecosystem conditions. Core Ideas Systematic field sampling coupled with remote sensing produces spatially explicit assessments. Key indicators of ecosystem health include soil erosion prevalence and herbaceous cover. Soil organic C dynamics are key indicators of ecosystem health. There are several key edaphic drivers of SOC, including soil texture and climate.