|Title||Hyper-temporal SPOT-NDVI dataset parameterization captures species distributions|
|Author(s)||Girma, Atkilt; Bie, C.A.J.M. de; Skidmore, Andrew K.; Venus, Valentijn; Bongers, Frans|
|Source||International Journal of Geographical Information Science 30 (2016)1. - ISSN 1365-8816 - p. 89 - 107.|
Forest Ecology and Forest Management
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Boswellia papyrifera - HANTS - hyper-temporal - MAXENT - SPOT-NDVI|
Hyper-temporal SPOT NDVI images contain useful information about the environment in which a species occurs, including information such as the beginning, end, peak, and curvature of photosynthetically active vegetation (PAV) greenness signatures. This raises the question: can parameterization of hyper-temporal SPOT NDVI images be useful to predict species distribution? A set of SPOT-NDVI images for the whole of Ethiopia covering nine years was classified using the unsupervised ISODATA clustering algorithm to group similar NDVI pixel values. The HANTS (Harmonic ANalysis of Time Series) algorithm, that fits series of smoothing cosine waves, was then applied to the time series for each of the NDVI classes to generate seven output Fourier components. These components, together with the topographic parameters slope and elevation, were used as predictors in a species distribution model using MAXENT. Presence-only data of one test species, Boswellia papyrifera, were modelled. This species is diminishing at an alarming rate and requires conservation. The performance of the model was evaluated by the area under curve (AUC) of the receiver-operating characteristics value. The output distribution map was tested for its agreement with the NDVI-clustering approach and conventional B. papyrifera distribution map using Kappa. The relative contributions of the first four predictors to the MAXENT in sequence were: 2nd harmonic phase, elevation, amplitude of the 1st harmonics, and amplitude of the 2nd harmonics. The average AUC test result for the 100 runs was 0.98 with a standard deviation of 0.002. The probability distribution map clearly shows high correlation with the B. papyrifera occurrence data. In addition, the distribution map was found to be in agreement with the NDVI-clustered and conventional map with improved details. Classifying hyper-temporal NDVI images and extracting their parameters through the use of the HANTS algorithm captures the PAV greenness behaviour (parameters) of the environment of the species studied. These parameters have proved successful in predicting the distribution of B. papyrifera.