Kian Pang Tan

Satellite Images for Monitoring Mangrove Cover Changes in a Fast Growing Economic Region in Southern Peninsular Malaysia

Effective monitoring is necessary to conserve mangroves from further loss in Malaysia. In this context, remote sensing is capable of providing information on mangrove status and changes over a large spatial extent and in a continuous manner. In this study we used Landsat satellite images to analyze the changes over a period of 25 years of mangrove areas in Iskandar Malaysia (IM), the fastest growing national special economic region located in southern Johor, Malaysia. We tested the use of two widely used digital classification techniques to classify mangrove areas. The Maximum Likelihood Classification (MLC) technique provided significantly higher user, producer and overall accuracies and less “salt and pepper effects” compared to the Support Vector Machine (SVM) technique. The classified satellite images using the MLC technique showed that IM lost 6740 ha of mangrove areas from 1989 to 2014. Nevertheless, a gain of 710 ha of mangroves was observed in this region, resulting in a net loss of 6030 ha or 33%. The loss of about 241 ha per year of mangroves was associated with a steady increase in urban land use (1225 ha per year) from 1989 until 2014. Action is necessary to protect the existing mangrove cover from further loss. Gazetting of the remaining mangrove sites as protected areas or forest reserves and introducing tourism activities in mangrove areas can ensure the continued survival of mangroves in IM.

Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia

This article demonstrates some techniques for studying the age of oil palm trees (Elaeis guineensis Jacq.) using the Disaster Monitoring Constellation 2 from the UK (UK-DMC 2) and Advanced Land Observing Satellite phased array L-band synthetic aperture radar (ALOS PALSAR) remote-sensing data at a private oil palm estate in southern peninsular Malaysia. Several techniques were explored with UK-DMC 2 data, namely (1) radiance, vegetation indices, and fraction of shadow; (2) texture measurement; (3) classifications, namely Iterative Self-Organizing Data Analysis Technique (ISODATA) classification, maximum-likelihood classification (MLC), and random forest (RF) classification; (4) in terms of ALOS PALSAR data, the correlation of polarizations (i.e. horizontal transmitting and horizontal receiving (termed HH polarization) and horizontal transmitting and vertical receiving (termed HV polarization)) and the ratio of these polarizations to the age of oil palm trees. From the results, band 1 (near-infrared) of UK-DMC 2, fraction of shadow, and mean filter from the grey-level co-occurrence matrix (GLCM) demonstrated strong correlation of determination (R 2 = 0.76–0.80) with the age of oil palm trees, while the ALOS PALSAR HH polarization could correlate moderately strongly (R 2 = 0.49) with the age of oil palm trees. Adding fraction of shadow and UK-DMC 2 data using the RF method further improved the overall accuracy of age classification from 45.3% (MLC method) to 52.9%. This study concluded that texture measurement (GLCM mean) and fraction of shadow are useful for studying the age of oil palm trees, although discriminating variation in age between mature oil palm trees is difficult because the leaf area index development of mature oil palm trees stabilizes at about 10 years of age. Future studies should involve height information, because this has the potential to be used as one of the most important variables for studying the age of oil palm trees.

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

Conducting quantitative studies on the carbon balance or productivity of oil palm is important in understanding the role of this ecosystem in global climate change. In this study, we evaluated the accuracy of MODIS (Moderate Resolution Imaging Spectroradiometer) annual gross primary productivity (GPP) (the product termed MOD-17) and its upstream products, especially the MODIS land cover product (the product termed MOD-12). We used high-resolution Google Earth images to classify the land cover classes and their percentage cover within each 1 km spatial resolution MODIS pixel. We used field-based annual GPP for 2006 to estimate GPP for each pixel based on percentage cover. Both land cover and GPP were then compared to MODIS land cover and GPP products. The results show that for pure pixels that are 100% covered by mature oil palm trees, the RMSE (root mean square error) between MODIS and field-based annual GPP is 18%, and that this is increased to 27% for pixels containing mostly oil palm. Overall, for an area of about 42 km2 the RMSE is 26%. We conclude that land cover classification (at 1 km resolution) is one of the main factors for the discrepancy between MODIS and field-based GPP. We also conclude that the accuracy of the MODIS GPP product could be improved significantly by using higher-resolution land cover maps.

A review of remote sensing based productivity models and their suitability for studying oil palm productivity in tropical regions

Oil palm (Elaeis guineensis Jacq.) cultivation has been expanding and has become one of the fastest developing agricultural crops in tropical regions. Therefore, it is critical to understand the carbon balance and dynamics within oil palm estates to determine its role in the global carbon cycle. Estimating oil palm productivity on a large scale is most feasible with remote sensing based models. Thus, the objective of this paper is to review existing remote sensing based models (i.e. CASA, GLO-PEM, VPM, C-Fix, TURC, EC-LUE, VI, TG, 3-PGS and MOD17) that use light use efficiency (LUE) logic, and subsequently to evaluate the suitability of these models for estimating oil palm productivity. This paper also highlights the limitation of current remote sensing based models for estimating oil palm productivity. From the review of existing literature, it is clear that the existing remote sensing based models need to be modified in terms of meteorological inputs, maximum LUE and environmental constraints in order to improve the estimation of oil palm productivity.

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.

On the upstream inputs into the MODIS primary productivity products using biometric data from oil palm plantations

This study evaluated the influence of upstream inputs into the Moderate Resolution Imaging Spectroradiometer (MODIS) primary productivity products, termed the MOD17, at tropical oil palm plantations (Elaeis guineensis Jacq.). Evaluation of MOD17 using oil palm plantations as test sites is ideal because the plantations are cultivated on large areas which are comparable with the size of MODIS pixels. It is difficult to find test sites covered by other single species in a whole pixel. The upstream inputs studied included (1) MODIS land cover, (2) the National Centers for Environmental Prediction–Department of Energy (NCEP-DOE) Reanalysis 2 meteorological data set, (3) MODIS leaf area index/fraction of photosynthetically active radiation (LAI/fPAR), and (4) MODIS maximum light-use efficiency (maximum LUE). Oil palm biometric and local meteorological data were utilized as ground data. Furthermore, scaling up oil palm LAI and fPAR from plot scale to regional scale (Peninsular Malaysia) was done empirically by correlating oil palm LAI derived from the hemispherical photography technique with radiance information from the Disaster Monitoring Constellation 2 satellite (UK-DMC 2). The upscaled LAI/fPAR developed in this study was used to evaluate the MODIS LAI/fPAR. The results showed that the MODIS land-cover product has an overall accuracy of 78.8% when compared to the Peninsular Malaysia land-use map produced by the Department of Agriculture, Malaysia. Regarding the NCEP-DOE Reanalysis 2 data set, vapour pressure deficit (VPD) and photosynthetically active radiation (PAR) contain large uncertainties in our study area. However, MODIS LAI and fPAR were correlated relatively well with the upscaled LAI (R2 = 0.50) and the upscaled fPAR (R2 = 0.60), respectively. The constant values of maximum LUE for croplands and evergreen broadleaf forest ecosystems are lower than the maximum LUE of oil palm. The relative predictive error assessment showed that the MOD17 net primary productivity (NPP) overestimated oil palm NPP derived from biometric methods by 142–204%. We replaced the upstream inputs of MOD17 by the local inputs for estimating oil palm GPP and NPP in Peninsular Malaysia. This was done by (1) assigning maximum LUE for oil palm plantations as a constant at 1.68 g C m−2 day−1, (2) utilizing meteorological data from local meteorological stations, and (3) using the upscaled fPAR of oil palm plantations. The amount of oil palm GPP and NPP for Peninsular Malaysia in 2010 were estimated to be ~0.09 Pg C year−1 (or equivalent to ~0.33 Pg CO2 year−1) and ~0.03 Pg C year−1 (~0.11 Pg CO2 year−1), respectively, indicating that oil palm plantations in Peninsular Malaysia can play an important role in global carbon sequestration. In the future there is likely to be a demand for MODIS GPP and NPP products that are more accurate than those currently generated by MOD17. We recommend future developments of the MOD17 processing system to allow improvements in the upstream input parameters, in the manner described in this article, both for global processing and for the production of more accurate values for GPP and NPP at regional and local scales.

Evaluation of MODIS Gross Primary Productivity of tropical oil palm in southern Peninsular Malaysia

Evaluation of the MODIS Gross Primary Productivity (GPP) or the MOD17A2 product has not been carried out for croplands, such as oil palm ecosystems in tropical regions. Thus, evaluation of the MOD17A2 is important in order to determine its usefulness for carbon related studies of oil palm. MOD17A2 and its inputs, such as MODIS land cover (MOD12Q1), meteorological data from Global NCEP/DOE II reanalysis dataset, LAI/fPAR (MOD15A2) and light use efficiency (LUE) were evaluated. Of the area of oil palm identified from a land use map, only 40.22% was correctly identified by croplands of MOD12Q1 as oil palm and the remaining 59.78% was misclassified as croplands and forest. From Global NCEP/DOE II reanalysis dataset, photosynthetic active radiation shows the lowest correlation (R2=0.02) whilst daily minimum temperature (Tmin) only agreed moderately (R2=0.26) compared to in-situ meteorological data. LAI and fPAR were underestimated by 10.00–16.79% and 9.66–10.80% respectively by MOD15A2. GPP was underestimated 19.10–29.48% by MOD17A2. LUE from site is higher than MODIS LUE of croplands for 0.19 g/m2/MJ/day.

A review of remote sensing applications for oil palm studies

Abstract Oil palm becomes an increasingly important source of vegetable oil for its production exceeds soybean, sunflower, and rapeseed. The growth of the oil palm industry causes degradation to the environment, especially when the expansion of plantations goes uncontrolled. Remote sensing is a useful tool to monitor the development of oil palm plantations. In order to promote the use of remote sensing in the oil palm industry to support their drive for sustainability, this paper provides an understanding toward the use of remote sensing and its applications to oil palm plantation monitoring. In addition, the existing knowledge gaps are identified and recommendations for further research are given.

UK-DMC 2 satellite data for deriving biophysical parameters of oil palm trees in Malaysia

The leaf area index (LAI) and fraction of Photosynthetically Active Radiation (fPAR) are key biophysical variables to estimate gross/net primary productivity of ecosystems which in turn can be used to identify their role in the global carbon cycle and climate change. This is the first study to estimate LAI and fPAR of oil palm ecosystem in Malaysia using remote sensing techniques. An empirical model relating radiance from band 1 (near infra red) of UK-DMC 2 remotely sensed data and LAI computed from hemispherical photos (oil palm LAI= -0.156 × radiance of band 1+16.95, R2=0.78) was developed m tins study. The model was then used to scale up LAI from plot to region (southern part of Peninsular Malaysia). Validation of the estimated LAI against LAI computed from hemispherical photos (independent samples) shows a good relationship (R2= 0.85 and RMSE= 0.52 m2 m-2). From the estimated LAI, fPAR was computed. LAI and fPAR derived in this study were compared against MOD15A (Moderate Resolution Imaging Spectroradiometer sensor onboard Terra satellite) LAI/fPAR product. It was found that LAI and fPAR from UK-DMC 2 and MODIS match well with R2= 0.70 and 0.66 for fPAR and LAI respectively.

The potential of MODIS derived Photochemical Reflectance Index for studying gross primary productivity of oil palm trees

Many studies found that the Photochemical Reflectance Index (PRI) is sensitive to the light use efficiency (LUE) of biomes. LUE is one of the important variables for estimating gross primary productivity (GPP) which is concerned with the rate of carbon sequestration. Previous studies demonstrated that Moderate Resolution Imaging Spectroradiometer (MODIS) derived PRI can be used as a surrogate for LUE of vegetation at a regional scale. However, the relationship between MODIS PRI and LUE is not universal; it varies with biomes. This study investigated the potential of MODIS derived PRI for studying 1) oil palm GPP, and 2) the environmental factors namely temperature, vapour pressure deficit (VPD) and photosynthetically active radiation (PAR) in the tropics (Peninsular Malaysia). The relationship of PRI (surrogate for LUE) with the tropical environmental factors deserves studies because these regions have unique climate which is different from other latitudes and which plays an important role in the global climate system. In this study, MODIS daily PRI was derived from MODIS daily calibrated radiances products (MOD021km), oil palm GPP was derived from MODIS GPP products, and daily meteorological data (daily mean temperature, daily mean VPD and daily PAR) were collected from the Malaysian Meteorological Department. Our results showed that the MODIS PRI has relatively weak and negative asymptotic relationships with MODIS oil palm GPP (R2=0.21, p <; 0.001) and the environmental factors (R2 ranges between 0.14 and 0.25, p <; 0.001).