Towards the development of a regional version of MOD17 for the determination of gross and net primary productivity of oil palm trees

Conducting quantitative studies on the carbon balance or productivity of oil palm is important for understanding the role of this ecosystem in global climate change. The MOD17 algorithm is used for processing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to generate the values of gross primary productivity (GPP) and net primary productivity for input to global carbon cycle modelling. In view of the increasing importance of data on carbon sequestration at regional and national levels, we have studied one important factor affecting the accuracy of the implementation of MOD17 at the sub-global level, namely the database of MODIS land cover (MOD12Q1) used by MOD17. By using a study area of approximately 7 km × 7 km (49 MODIS pixels) in semi-rural Johor in Peninsular Malaysia and using Google Earth 0.75 m resolution images as ground data, we found that the land-cover type for only 16 of these 49 MODIS pixels was correctly identified by MOD12Q1 using its 1 km resolution land-cover database. This leads to errors of 24% to 50% in the maximum light use efficiency, leading to corresponding errors of 24% to 50% in the GPP. We show that by using the Finer Resolution Observation and Monitoring – Global Land Cover (FROM-GLC) land-cover database developed by Gong et al., this particular error can be essentially eliminated, but at the cost of using extra computing resources.

[1]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[2]  M. M. Roslan,et al.  The role of leaf area index (LAI) in oil palm. , 2004 .

[3]  Arthur P. Cracknell,et al.  UK-DMC 2 satellite data for deriving biophysical parameters of oil palm trees in Malaysia , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Frédéric Achard,et al.  GLOBCOVER : The most detailed portrait of Earth , 2008 .

[5]  A. Cracknell,et al.  A review of remote sensing based productivity models and their suitability for studying oil palm productivity in tropical regions , 2012 .

[6]  Isaac Kwesi Nooni,et al.  Support vector machine to map oil palm in a heterogeneous environment , 2014 .

[7]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[8]  Cameron Hepburn,et al.  Carbon Trading: A Review of the Kyoto Mechanisms , 2007 .

[9]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[10]  Arthur P. Cracknell,et al.  Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth imagery , 2013 .

[11]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[12]  Arthur P. Cracknell,et al.  On the upstream inputs into the MODIS primary productivity products using biometric data from oil palm plantations , 2014 .

[13]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[14]  Maosheng Zhao,et al.  Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009 , 2010, Science.

[15]  Cristina Milesi,et al.  User's Guide GPP and NPP (MOD17A2/A3) Products NASA MODIS Land Algorithm , 2003 .

[16]  Le Yu,et al.  Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach , 2013 .

[17]  A. Cracknell,et al.  Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia , 2013 .

[18]  Arthur P. Cracknell,et al.  Evaluation of MODIS Gross Primary Productivity of tropical oil palm in southern Peninsular Malaysia , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[19]  Hankui K. Zhang,et al.  Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .