Olive (Olea europaea L.) is the
main perennial Spanish crop. Soil
manage
ment in olive orchards is
mainly based on intensive and tillage operations, which have a great relevancy in ter
ms of negative environ
mental i
mpacts. Due to this reason, the European Union (EU) only subsidizes cropping syste
ms which require the i
mple
mentation of conservation agro-environ
mental techniques such as cover crops between the rows. Re
motely sensed data could offer the possibility of a precise follow-up of presence of cover crops to control these agrarian policy actions, but firstly, it is crucial to
explore the potential for classifying variations in spectral signatures of olive trees, bare soil and cover crops using field spectroscopy. In this paper, we used hyperspectral signatures of bare soil, olive trees, and sown and dead cover crops taken in spring and su
mmer in two locations to evaluate the potential of two
methods (MultiLogistic regression with Initial and Radial Basis Function covariates, MLIRBF; and Si
mpleLogistic regression with Initial and Radial Basis Function covariates, SLIRBF) for classifying the
m in the 400–900 n
m spectru
m. These
methods are based on a MultiLogistic regression
model for
med by a co
mbination of linear and radial basis function neural network
models. The esti
mation of the coefficients of the
model is carried out basically in two phases. First, the nu
mber of radial basis functions and the radii and centres’ vector are deter
mined by
means of an evolutionary neural network algorith
m. A
maxi
mu
m likelihood opti
mization
method deter
mines the rest of the coefficients of a MultiLogistic regression with a set of covariates that include the initial variables and the radial basis functions previously esti
mated. Finally, we apply forward stepwise techniques of structural si
mplification.
We compare the performance of these methods with robust classification methods: Logistic Regression without covariate selection, MLogistic; Logistic Regression with covariate selection, SLogistic; Logistic Model Trees algorithm (LMT); the C4.5 induction tree; Naïve Bayesian tree algorithm (NBTree); and boosted C4.5 trees using AdaBoost.M1 with 10 and 100 boosting iterations. MLIRBF and SLIRBF models were the best discriminant functions in classifying sown or dead cover crops from olive trees and bare soil in both locations and seasons by using a seven-dimensional vector with green (575 nm), red (600, 625, 650 and 675 nm), and near-infrared (700 and 725 nm) wavelengths as input variables. These models showed a correct classification rate between 95.56 % and 100 % in both locations and seasons. These results suggest that mapping covers crops in olive trees could be feasible by the analysis of high resolution airborne imagery acquired in spring or summer for monitoring the presence or absence of cover crops by the EU or local administrations in order to make the decision on conceding or not the subsidy.