Preprint Article Version 1 This version is not peer-reviewed

The Effect of Temporal Gradient of Vegetation Indices on Early-Season Wheat Area Estimation Using Random Forest Classification

Version 1 : Received: 9 January 2018 / Approved: 10 January 2018 / Online: 10 January 2018 (09:13:02 CET)

How to cite: Saei Jamal Abad, M.; Abkar, A.A.; Mojaradi, B. The Effect of Temporal Gradient of Vegetation Indices on Early-Season Wheat Area Estimation Using Random Forest Classification. Preprints 2018, 2018010088 (doi: 10.20944/preprints201801.0088.v1). Saei Jamal Abad, M.; Abkar, A.A.; Mojaradi, B. The Effect of Temporal Gradient of Vegetation Indices on Early-Season Wheat Area Estimation Using Random Forest Classification. Preprints 2018, 2018010088 (doi: 10.20944/preprints201801.0088.v1).

Abstract

The early-season area estimation of the winter wheat crop as a strategic product is important for decision makers. Classification of multi-temporal images is an approach which is affected by many factors like appropriate training sample size, proper frequency and acquisition times, vegetation indices (VIs) type, temporal gradient of spectral bands and VIs, appropriate classifier and missed values because of cloudy conditions. This paper addresses the impact of appropriate frequency and acquisition times and VIs type along with the spectral and VI gradient on random forest (RF) classifier when missed values exist in multi-temporal images. To investigate the appropriate temporal resolution for image acquisition, the study area was selected on an overlapping area between two LDCM paths. In our developed method, the miss values of cloudy bands for each pixel are retrieved by the mean of k-nearest ordinary pixels. Then the multi-temporal image analysis is performed by considering different scenarios provided by decision makers in terms of desired crop types that should be extracted at early-season in the study areas. The classification results obtained by the RF decrease by 1.6% when temporally missed values retrieved by the proposed method, which is an acceptable result. Moreover, the experimental results demonstrated that if temporal resolution of Landsat 8 increased to one week the classification task can be conducted earlier with almost better results in terms of OA and kappa. The impact of incorporating VIs along with the temporal gradients of spectral bands and VIs as new features in RF demonstrated that the OA and Kappa are improved 3.1% and 6.6%, respectively. Furthermore, the obtained result showed that if only one image from seasonal changes of crops is available, the temporal gradient of VIs and spectral bands play the main role to discriminate remarkably wheat from barley. The experiments also demonstrated that if both wheat and barley merge to a single class the crop area can be estimated two months earlier with 97.1 and 93.5 in terms of OA and kappa, respectively.

Subject Areas

wheat classification; random forest; spectral gradient difference; vegetation indices

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