Spectral normalization of SPOT 4 data to adjust for changing leaf phenology within seasonal forests in Cambodia Andreas Langner a, ⁎, Yasumasa Hirata a , Hideki Saito a , Heng Sokh b , Chivin Leng b , Chealy Pak b , Rastislav Raši c,d a Bureau of Climate Change, Forestry and Forest Products Research Institute (FFPRI), 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan b Forestry Administration, 40 Preah Norodom Blvd. Phsar Kandal 2, Khann Daun Penh, Phnom Penh, Cambodia c Joint Research Centre of the European Commission, Institute for Environment and Sustainability, TP 440, 21027 Ispra, VA, Italy d National Forest Centre, Forest Research Institute, 96092 Zvolen, Slovak Republic abstract article info Article history: Received 17 December 2012 Received in revised form 26 November 2013 Accepted 20 December 2013 Available online 21 January 2014 Keywords: Cambodia Transformed divergence Seasonality Artifacts Forest monitoring Deciduous forest SPOT 4 Normalization Seam line Object-based classification As cloud cover exacerbates the application of optical satellite data for forest monitoring in tropical wet and dry regions during the rainy season, data acquisition is mainly restricted to the dry season. When analyzing wide areas, large numbers of single scenes obtained at different times of the dry season are often handled. Such imagery is characterized by changes of spectral reflectance due to vegetation phenology, varying atmospheric effects and solar geometries. In order to allow batch processing with automatic classification techniques, inter- scene comparability is required and data have to be radiometrically normalized. Cambodia is characterized by a mixture of evergreen, semi-evergreen and deciduous forest types, the latter two experiencing at least partial leaf shedding over the course of the dry season. Using spatial medium resolution SPOT 4 data and a manually delineated base map a season adjustment model was developed. The model is adapting the land cover specific spectral signatures of a slave scene (acquired in the middle of the dry season with its seasonal forests defoliated) to an adjacent master scene (from the beginning of the dry season, showing the same forest types with leafs). The relative position of every pixel reflectance was determined in relation to the mean reflectance and its standard deviation for each land cover type and sensor band of the unadjusted slave scene. For seasonality adjustment these pixel reflectance values were transformed (rescaled) to the corresponding position in spectral space defined by the band mean reflectance and standard deviation derived from the corresponding land cover class of the master scene. While the variability of spectral profiles of the pixels in the slave scene is rescaled, the mean reflectance value of the land cover class in the slave scene is conformed to the mean reflectance of the cor- responding land cover class in the master scene. The Transformed Divergence (TD) separability index was used to indicate the performance of the adjustment process by characterizing the spectral distance for each land cover type comparing a reference dataset to the uncorrected and to the seasonality corrected scene respectively. While the TD values of all forest types showed a sharp decline, highlighting the good performance of the model, the TD values of the agriculture/urban class remained high, indicating limited normalization of this het- erogeneous land cover type. In order to further demonstrate the performance of the model, an object-based land cover classification was applied to the unadjusted as well as to the corresponding adjusted scene. A compar- ison of the results showed a highly significant improvement of overall accuracy from 32.2% to 75.8% when apply- ing seasonality adjustment. © 2014 Elsevier Inc. All rights reserved. 1. Introduction Tropical forest monitoring using optical satellite data is complicated due to often persistent cloud cover, strongly restricting satellite acquisi- tion (Asner, 2001; Trigg, Curran, & McDonald, 2006). Gaps in the data record due to cloud cover have to be filled by other scenes of the same geographic position not affected by clouds but acquired at another point of time (Trigg et al., 2006; Wulder et al., 2008). Monitoring of larger areas using spatially medium or high resolution data can be very challenging due to the correlation between spatial resolution and revisit cycles for most satellite systems (Beuchle et al., 2011; Wulder et al., 2008). However, only such data are able to resolve small-scale land cover changes such as illegal logging activities (Fuller, 2006). In contrast to the tropical rainforest climate of the inner tropics, areas at the outer margins of the tropical zone experience a more pronounced seasonality (Peel, Finlayson, & McMahon, 2007), with lower cloud frequency during the dry season (Wylie, Jackson, Menzel, & Bates, 2005), thus slightly enhancing the probability to obtain less cloud-affected scenes (Asner, 2001). However, the vegetation of the Remote Sensing of Environment 143 (2014) 122–130 ⁎ Corresponding author at: European Commission – Joint Research Centre, Institute for Environment & Sustainability, Forest Resources and Climate Unit, Via E. Fermi, 2749, I-21027 Ispra, VA, Italy. Tel.: +81 29 829 8317, +39 0332 78 3995; fax: +81 29 874 3720. E-mail addresses: andi.langner@gmail.com (A. Langner), hirat09@affrc.go.jp (Y. Hirata), rslsaito@ffpri.affrc.go.jp (H. Saito), sokhhengpiny@yahoo.com (H. Sokh), lengchivin@gmail.com (C. Leng), pak_chealy@yahoo.com (C. Pak), rastislav.rasi@jrc.ec.europa.eu (R. Raši). 0034-4257/$ – see front matter © 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.12.012 Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse