『Abstract
Human-induced soil erosion has severe economic and environmental
impacts throughout the world. It is more severe in the tropics
than elsewhere and results in diminished food production and security.
Kenya has limited arable land and 30％ of the country experiences
severe to very severe human-induced soil degradation. The purpose
of this research was to test visible near infrared diffuse reflectance
spectroscopy (VNIR) as a tool for rapid assessment and benchmarking
of soil condition and erosion severity class. The study was conducted
in the Saiwa River watershed in the northern Rift Valley Province
of western Kenya, a tropical highland area. Soil 137Cs
concentration was measured to validate spectrally derived erosion
classes and establish the background levels for different land
use types. Results indicate VNIR could be used to accurately evaluate
a large and diverse soil data set and predict soil erosion characteristics.
Soil condition was spectrally assessed and modeled. Analysis of
mean raw spectra indicated significant reflectance differences
between soil erosion classes. The largest differences occurred
between 1350 and 1950 nm with the largest separation occurring
at 1920 nm. Classification and Regression Tree (CART) analysis
indicated that the spectral model had practical predictive success
(72％) with Receiver Operating Characteristic (ROC) of 0.74. The
change in 137Cs concentrations supported the premise
that VNIR is an effective tool for rapid screening of soil erosion
condition.
Keywords: Erosion; Soil degradation; CART; 137Cesium;
Kenya』
1. Introduction
2. Methods
2.1. Geographic and ecological setting
2.2. Soil sampling plan and analysis
2.3. Soil reflectance measurement
2.4. Soil reflectance analysis and prediction of soil properties
2.5. Soil erosion index development
2.6. Soil erosion index spectral relationship
2.7. 137Cesium sample selection and analysis
2.8. Statistical comparison of soil erosion index and field parameters
3. Results
3.1. Spectral analysis of erosion class
3.2. 137Cs analysis
3.3. CART analysis
4. Conclusions
Acknowledgements
References