Mapping rubber trees based on phenological analysis of Landsat time series data-sets

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1 Geocarto International ISSN: (Print) (Online) Journal homepage: Mapping rubber trees based on phenological analysis of Landsat time series data-sets Janatul Aziera binti Abd Razak, Abdul Rashid bin M. Shariff, Noordin bin Ahmad & Maher Ibrahim Sameen To cite this article: Janatul Aziera binti Abd Razak, Abdul Rashid bin M. Shariff, Noordin bin Ahmad & Maher Ibrahim Sameen (2018) Mapping rubber trees based on phenological analysis of Landsat time series data-sets, Geocarto International, 33:6, , DOI: / To link to this article: Informa UK Limited, trading as Taylor & Francis Group Accepted author version posted online: 08 Feb Published online: 13 Feb Submit your article to this journal Article views: 1512 View Crossmark data Citing articles: 3 View citing articles Full Terms & Conditions of access and use can be found at

2 GEOCARTO INTERNATIONAL, 2018 VOL. 33, NO. 6, Mapping rubber trees based on phenological analysis of Landsat time series data-sets Janatul Aziera binti Abd Razak a, Abdul Rashid bin M. Shariff b, Noordin bin Ahmad a and Maher Ibrahim Sameen a a Faculty of Engineering, Department of Civil Engineering, Universiti Putra Malaysia, Serdang, Malaysia; b Faculty of Engineering, Department of Biological and Agriculture, Universiti Putra Malaysia, Serdang, Malaysia ABSTRACT This study proposes a strategy for accurate mapping of rubber trees through the analysis of Landsat time series datasets. The phenological dynamics of rubber trees were derived from the Normalized Difference Vegetation Index (NDVI) to verify the three important phenological metrics of rubber trees; defoliation, foliation and their growing stages. A decade ( ) ago, Landsat time series NDVIs were used to study the strength of relationship between rubber trees, evergreen trees and oil palm trees. Two important results that could discriminate these three types of vegetation were found; firstly, a weak relationship of NDVIs between rubber trees and evergreen trees during the defoliation period (r 2 = ) and secondly between rubber trees and oil palm trees during the growing period (r 2 = ). This analysis was verified using Support Vector Machine to map the distribution of the three types of vegetation. An accurate mapping strategy of rubber trees was successfully formulated. ARTICLE HISTORY Received 26 July 2016 Accepted 28 January 2017 KEYWORDS Landsat time series; rubber mapping; oil palm; tropical evergreen 1. Introduction Rubber tree is a major crop for smallholders in South-east Asia and serves as one of the important commercial crop apart from oil palm plantation. Rubber was first introduced in Asia in 1876, when seeds were first shipped from the Amazonas to the United Kingdom and further to Ceylon and planted there (Rantala 2006). In the following year, rubber trees were planted in Singapore and Malaya (Hong & Sim 1999). Although rubber was first an estate crop, local individual farmers soon adopted the crop and so they were drawn into the world commercial economy (Courtenay 1979). Since then, rubber industry had fully grown in Malaysia and has become one of the most important socio-economic sectors of the country, featuring strongly in both foreign exchange earnings and rural economic development for the country (Ratnasingam et al. 2012). Peninsular Malaysia has been among the world s most important rubber cultivation area, and the present wealth of this area was largely based on production of natural rubber (Collins et al. 1991). In the year 2005, Indonesia, Thailand and Malaysia produced 33, 23 and 13% of the world s natural rubber, respectively (FAO 2006). Lately, the rubber plantation area has been decreasing in Malaysia CONTACT Abdul Rashid bin M. Shariff 2017 Informa UK Limited, trading as Taylor & Francis Group rashidsnml@gmail.com

3 628 J. A. BINTI. ABD RAZAK ET AL. although this trend has been reversed in other rubber production leading countries such as Thailand whereby the rubber plantations have started to spread to new areas in the Eastern and North-east region of Thailand (Rantala 2006). The most significant decrease of rubber production in Malaysia was seen in 2006, caused by the dwindling demands of rubber during the global economic crisis (SME annual report 2007). In October 2012, the production of rubber decreased by 2.6% compared to the previous year (Department of Statistics 2012). Hence, accurate mapping of rubber plantation area and monitoring of rubber production in this country is necessary to observe this trend so that a proper plan for optimization of land for rubber plantation can be promoted. An accurate map of rubber plantation area is also necessary to access socio-economic and environmental impacts of rubber plantation which have been shifted from their original planting places or the opening of new plantation areas at the expense of natural forests and the balance of biodiversity. Previous studies; Yusoff and Muharam (2015), Fan et al. (2015), Senf et al. (2013), Dong et al. (2013), Kou et al. (2015) and Li & Fox (2011, 2012) focused specifically on distinguishing rubber trees against either deciduous forest or evergreen forest depending on the region where the studies were conducted. However, for a country such as Malaysia, instead of evergreen trees that are spectrally similar in properties with rubber trees, cropland such as oil palm trees also exhibit this property which can cause misclassification of those three types of vegetation. This can create a problem since rubber has quite similar growth requirements as oil palm, and both crops are therefore cultivated in the same geographical areas (Verheye 2010). To address these problems, in this study, a methodology was devised utilising the 10 years acquisition of Landsat time series data-sets; (1) extraction of vegetation indices (VIs); normalised difference vegetation index (NDVI) (Rouse et al. 1974), enhanced vegetation index (EVI) (Huete et al. 1997), leaf area index (LAI) (Watson 1947) and red green ratio index (RGRI) (Gamon & Surfus 1999; Yang et al. 2008), (2) analysis of the rubber trees vegetation dynamics information and (3) classification of the three vegetation of interests using SVM from recent Landsat images of the study area during the three phenological cycles of rubber trees. The results obtained from the designated methodology was used to form an effective strategy that can be used for accurate mapping of rubber trees in this region based on several criteria and considerations to distinguish between the three types of vegetation. 2. Methods 2.1. Study area Sungai Buloh is one of the Selangor state sub-districts, located at the south-west of Kuala Lumpur, the national capital of Malaysia, centred at the coordinates of 3 o 12 N latitude and 101 o 35 E longitude. The area elevation ranges from minimum 100 feet to a maximum 1754 feet above sea level at the average of 456 feet. This area is characterised by a tropical rainforest climate with annual rainfall accumulation of 2478 mm with the annual mean temperature of ~27.1 C (Malaysian Meteorological Department). Built-up area accounts for 56% of the total area of the land with the total population of 466, 163 focusing on Sungai Buloh sub-district only (Department of Statistics 2010), while the greenery made up of about 40% of the area of study. In this study, a subset image of the area centred on Sungai Buloh (Figure 1) was selected as a case study due to the nature of this area that had been historically and still at present became a rubber plantation area, housing Malaysia Rubber Board (MRB) Rubber Research Institute (RRIM) (Malaysian Rubber Board 2011). Choosing this area was also important as a basic foreknowledge in developing training sites for classification routines performed in this study. To be subjective to this study, the subset of Sungai Buloh was intentionally extended beyond the border of Sungai Buloh to include other vegetation of interests; natural evergreen forests and oil palm plantation areas that are vital to the designed methodology.

4 GEOCARTO INTERNATIONAL 629 Figure 1. The geographical extent of the study area which centred on Sungai Buloh sub-district in the state of Selangor. The extend of the study area was shown in the inset image of Landsat 8 OLI colour composite image (a). The image in (b) display the location of training (white) and ground truth ROIs (blue) developed for classification of the three types of vegetation while image in (c) is the topographic map from the Department of Survey and Mapping Malaysia (DSMM) used as a reference for selecting ground truth region of interests (ROIs) Landsat time series data-sets In order to capture the phenological characteristics of rubber trees and other land cover types, 50 level-one standard terrain-corrected products; L1T were used in this study. This product type provides systematic radiometric and geometric accuracy by incorporating ground control points while employing a digital elevation model (DEM) of three arc second (90-m) resolution in Geographic coordinates with a WGS-84 datum for topographic accuracy in which the geodetic accuracy of the product depends on the accuracy of the ground control points (from the GLS2000 data-set) and the resolution of the DEM used (USGS 2008, 2015). The time series Landsat images acquired from TM, ETM+, OLI/TIRS sensors at (path/row 127/58) from 2006 to 2015, all at 30-m spatial resolution were obtained from the USGS Earth Explorer. The bands used for this study comprises B1 B5 and B7 for Landsat 5 TM; B1 B7 for Landsat 7 ETM+; and B1 B7 and B9 for Landsat 8 OLI for the purpose of extracting rubber trees vegetation dynamics information and the three vegetation indices (NDVI, EVI, LAI and RGRI). The satellites (Landsat 5, Landsat 7 and Landsat 8) high revisit frequency of 16 days and USGS distribution policy in which all images are available almost at near real-time making this product extremely suitable for land use land cover mapping, monitoring and change detection. Images dating from 2006 to 2015 (Table 1) were acquired based on three criteria; (1) on specific dates from 2006 to 2015 based on rubber trees three phenological stages; defoliation, foliation and growth period (a period after the emergence of rubber new leaves and before the defoliation) for comparing VIs (NDVI, EVI, LAI and RGRI) of rubber trees, evergreen trees and oil palm trees; (2) covering all months in the year 2014 for extraction of rubber trees vegetation dynamics information and (3) recent images in 2015 (ranging from cloud free images to maximum 10% cloud cover images) for the purpose of image classification.

5 630 J. A. BINTI. ABD RAZAK ET AL. Table 1. Summary of the number of Landsat time series images used for each acquisition year from 2006 to Date acquired Year L5 L7 L8 Total Date acquired Year L5 L7 L8 Total 14-Feb Jan Mar 4-Feb 10-Sep 8-Mar24-Mar 5-Mar Apr 9-Apr 22-Apr 25-Apr 24-May 5-May12-Jun 20-Feb Jul 22-Jul 26-Mar Aug 11-Apr 23-Aug 27-Apr 24-Sep 17-Aug 18-Oct 9-Feb Nov 22-Apr 27-Nov 12-Aug 5-Dec 2-Feb Feb Apr 27-Mar 25-Apr 12-Apr 6-Jul 30-May 6-Jan Aug 23-Feb 7-Feb 26-Mar 27-Mar 27-Apr 12-Apr 1-Aug 9-Feb Mar 22-Apr 27-Jul 2.3. Landsat data pre-processing Landsat images were pre-processed based on sequences as such; (1) radiometric correction to top-of-atmosphere (TOA) reflectance, (2) spatial subsetting to the same geographic extent, (3) cloud raster masking, (4) Landsat ETM+ SLC failed gap fill and (5) atmospheric correction using QUick Atmospheric Correction (QUAC) module. Radiometric corrections were applied for all Landsat time series data to compensate for the changes in brightness value measured for any given object in the images. Radiometric corrections routines involved conversion of the measured brightness values to TOA reflectance units using Landsat image metadata file (*_MTL.txt). This normalisation procedure is essential when it comes to time series images as it mostly removes variations between these images due to sensor differences, earth-sun distance and solar zenith angle. In addition, the geometric correction was done for the images using the image-to-image method. In this method, a reference image was correction based on ground control points collected by differential global positioning system (DGPS) receiving real-time correction from Malaysian Land and Survey Department. Then, the remaining images were corrected based on the reference image to ensure that all the images are properly coincide. As the next process after pre-processing involved the computation of spectral vegetation indices, e.g. NDVI, this first step is crucial to prevent a relatively constant error due to the sensor that affects the NDVI results (Guyot & Gu 1994). In radiometric calibration framework, spatial subsetting of the study area extent was also conducted using a reference image with the Google Earth-based geographic extent of the study area has been properly determined with the aid of Google Earth. The third stage involved raster cloud masking. The raster pixels created from this process were set to a value of NoData in which they are excluded from subsequent image processing tasks; atmospheric correction, computation of vegetation indices and image classification. This was crucial as pixels from cloud cover can severely distort the image statistics and reduce overall accuracy of the tasks mentioned. Cloud masking for Landsat time series images were conducted in twofolds; (1) using the Quality Band

6 GEOCARTO INTERNATIONAL 631 for OLI/TIRS data following U.S. Geological Survey s Landsat 8 Quality Assessment Band procedures; and (2) using any of the band for TM and ETM+ data. First, a raster mask was created from a ROI of the image band. Using the ROI s threshold rule, the cloud pixels were identified by adjusting the threshold parameters minimum and maximum values. For OLI/TIRS data, the standard minimum and maximum threshold parameters for the Quality Band are 28,672 and 61,440, respectively. Since the Quality band pixel values with the range of 28,672 61,440 sometimes included too many pixels that were not required to be masked out (e.g. bare soil pixels, rooftop pixels) the range were altered to ensure that they included only the cloud pixels. Similar arrangements were done for TM and ETM+ data with the only difference is in the image band used (as TM and ETM+ do not have the Quality band, common multispectral bands, e.g. visible blue or NIR were used). In addition, due to ETM+ sensor had a failure of the Scan Line Corrector (SLC) on 31 May 2003, resulting in wedge-shaped gaps on both sides of each scene acquired by the sensor, all ETM images used were corrected. To compensate this data loss, using ENVI software, the installed plugin landsat_gapfill.sav was used to automate the gap and fill procedures for all ETM+ image. The final step in image pre-processing involved correction for atmospheric influences using ENVI QUAC module as radiometric calibration procedures does not account for atmospheric influence. Situated near the equator, it is a common for images captured over Malaysia to encounter atmospheric turbulences; e.g. haze and frequent cloud covers. Such atmosphere conditions distort imagery by both reducing the energy illuminating a ground object and by acting as a reflector itself, adding a scattered path radiance component to the signal detected by a sensor. QUAC module was preferred in this study compared to FLAASH module as the former works best with scenes from the study area that contain diverse materials such as water, soil, vegetation and man-made structures while the latter works better with scenes over oceans or large water bodies (Exelis 2015). All the subsequent processing in this study was performed based on QUAC products Computation of vegetation spectral indices Four vegetation indices were computed for all the 50 images acquired in this study; NDVI, EVI, LAI and RGRI. NDVI was computed in this study; (1) as an input data for rubber trees vegetation dynamics analysis; (2) statistical analysis using Pearson Correlation to study the relationship between rubber trees against evergreen trees and rubber trees against oil palm trees during rubber trees defoliation, foliation and growth stages; (3) to compare NDVI between rubber trees, evergreen trees and oil palm trees during the three main rubber trees phenological stages; and (4) to map NDVI land cover changes of the study area between the year 2006 to 2015 to study the capabilities of mapping rubber trees with different stand ages. In serving the purposes; (1), (2) and (3), only specific point of interests (POIs) from the three vegetation of interests were randomly extracted and properly plotted due to the limitations introduced by cloud cover with the aid of high-resolution Google Earth image altogether with its time slider to move between different acquisition dates. Despite NDVI limitations in this study due to the leaf-off period (defoliation) of rubber trees, the index altogether with other indices employed in this study is still reliable to be used as NDVI (like most other remotely sensed vegetation indices) is not an intrinsic physical quantity, although it is indeed correlated with certain physical properties of the vegetation canopy: leaf area index (LAI), fractional vegetation cover, vegetation condition and biomass (Carlson & Ripley 1997). To support NDVI analysis for purpose no. (3), other vegetation spectral indices; EVI, LAI and RGRI for the three types of vegetation were also computed based on their respective significance to this study. For example, LAI was computed to see the differences in foliage cover; EVI was used to improve NDVI results by reducing the atmospheric influences over the study area while RGRI were calculated to study the foliage development in canopies and leaf redness induced by anthocyanin (water-soluble pigments abundant in newly forming leaves and those undergoing senescence). These indices were all computed for the three types of vegetation during the three rubber trees phenological stages. The equations involved for each vegetation indices are;

7 632 J. A. BINTI. ABD RAZAK ET AL. EVI = 2.5 x NDVI = NIR RED NIR + RED (NIR RED) (NIR + 6 RED 7.5 BLUE + 1 LAI = (3.168 EVI 0.118) > 0 RGRI = 699 i=600 R i 599 i=500 R R RED : R GREEN j (1) (2) (3) (4) 2.5. Extraction of rubber trees vegetation dynamics information Vegetation dynamics information from rubber trees was extracted from PhenoSAT software to study their phenological cycles using NDVI time series acquired in a single year (2014) from Landsat datasets. As the aforementioned seasons for rubber trees defoliation, foliation and growing period stages from various researches with different geographical regions and climate varies, e.g. late February to March for defoliation and late March to April for foliation (Dong et al. 2013); defoliation foliation process between late December and mid-march (Fan et al. 2015) for the studies conducted in China, there is a need to verify rubber trees phenological information, especially in the equatorial region. For most location, leaf-shedding of rubber tree occurs in varying degree which coincides with annual seasonal dry season. However, due to climate change that affects the world s weather pattern, the starting and ending period of the annual dry season also slightly changes. To estimate rubber trees phenological cycle, a recent complete cycle of NDVI time series data-sets from the year 2014 was used using specific rubber trees point of interests (POIs) for 16 values (representing every month in 2014 with additional NDVI data extracted from certain month). PhenoSAT provided results in form of two separate analyses; the evaluated phenological stages and the analysis of the noise/smooth filter used to process the data. As the phenological analysis produced different results respective to each filter, all the results provided for each filter were properly plotted in Excel Spreadsheet and compared to accurately evaluate the phenological information of rubber trees Supervised classification routines using SVM Support vector machine (SVM) was used in this study as it is simple, user friendly and available in most remote sensing/image processing software. SVM was also used in consideration to the nature of the study area which is heterogeneous in nature (Sungai Buloh is a rapidly developed urban area). SVM also provides higher classification accuracies than other pattern recognition techniques and is advantageous, especially for remote sensing data-sets and image analysis in the presence of heterogeneous classes for which only few training samples are available (Melgani & Bruzzone 2004; Ge et al. 2008). In this study, supervised classification was performed to identify the differences in the distribution of the three vegetation types during the three main growing seasons of rubber trees. For classifying rubber trees, evergreen trees and oil palm trees, to ensure high classification accuracies and to prevent misclassification between the three vegetation, a combination of Google Earth, Google Street View and Topographic Map from DSMM (Figures 1 and 2) were used as guide for developing training and reference ROIs using four Landsat images according to the three phenological stages of rubber. For all the images used in the classification, the same ROIs were used to prevent biased in the classification process as well as in comparing the accuracy results from the classified images. In addition, for classification the three types of vegetation, to ensure the spectral separability between the training ROIs between each class, a spectral separability analysis using Jeffries-Matusita (J-M) (Jeffreys 1946; Richards 1999) distance was computed.

8 GEOCARTO INTERNATIONAL 633 Figure 2. Sample images used to develop reference ROIs from Google Earth and Google Street View; rubber trees (A), oil palm trees (B) and evergreen trees (C) Classifying rubber trees with different stand ages As the foliage cover of rubber trees varies with their stand ages, vegetation indices such as LAI was experimented to map the distribution of rubber trees with two different stand ages in the study area. As the actual ages of rubber trees in this area were unknown due to the non-existence of ancillary data and the long history of the study area being a rubber cultivation area (rubber trees were continuously replanted), based on the time period of data acquisition of Landsat images used in this study, their stand ages were only divided into two broad sub-categories: (1) mature rubber trees (> 6 years old) and; (2) young rubber trees (< 6 years old). Image used in this classification was LAI image differencing result during the growing period of rubber trees dated on 18 August For mapping the rubber trees stand ages, LAI image during their growing period is easier to be applied as there would be significant difference between the younger and older trees outer canopy (surface of canopy in direct contact with the atmosphere) and emergent layer (an irregular zone of extremely tall trees, rising above the mean canopy) (Parker et al. 1989). As the LAI image generates similar values for mature rubber trees and evergreen trees for image used in their growing period, the previous rubber trees classification result with the highest user and

9 634 J. A. BINTI. ABD RAZAK ET AL. producer accuracy was used (12 April 2015) for ROIs intersection. This process eliminated other types of vegetation that falls between the specified ROI threshold rule. The classification process of rubber trees in ENVI were conducted as such; (1) creating ROI threshold rule for all vegetation types; e.g. min LAI = 0, max LAI = 3; (2) importing previous rubber trees classification result as a vector in LAI s ROI layer; (3) determining the approximate LAI for young rubber trees based on LAI values in ROI threshold rule histogram, e.g. min LAI = , max LAI = ; (4) determining the approximate LAI for mature rubber trees based on LAI values in ROI threshold rule histogram, e.g. min LAI = , max LAI = ; (5) intersect the ROI threshold rule results for young and mature rubber trees with rubber trees classification result, respectively (two different classes); (6) classify rubber trees with different stand ages from the two ROIs intersection results. The LAI threshold values used to classify rubber trees stand ages can be determined by examining the LAI histogram plot for any image used (peaks and valleys of the histogram). In assessing the classification accuracy, due to the limited ability to identify the age of rubber trees from high-resolution Google Earth image from single date, e.g. image acquired in 2015 Landsat and Google Earth images from two difference dates were used as samples for extracting reference ROIs. The reference ROIs for error matrix were selected from the difference in between the Landsat images in 2006 and 2015 to detect the missing rubber trees (younger rubber trees were not yet planted in 2006) with the aid of Google Earth high-resolution image (swiping the various acquisition dates in Google Earth for verifying the reference ROIs. 3. Results 3.1. Computed vegetation indices of rubber, evergreen and oil palm trees (1) Normalized Difference Vegetation Index Using NDVI metrics, the pattern in the differences between the three vegetation types showed significant differences during the three phenological periods which can be used to distinguished between the three vegetation types specifically during each season. During defoliation (Figure 3(a)), the differences in between rubber trees and evergreen trees were clearly delineated except for certain date; 9 February 2013 which might be due to the changes in the starting period for defoliation of rubber trees. Compared to evergreen trees, rubber trees showed lower NDVI values during their defoliation phase as rubber trees have a canopy with little or no green leaves, while evergreen forests have only slight change in canopy coverage with high NDVI values (Dong et al. 2013; Fan et al. 2015). The pattern of NDVI values for oil palm trees and rubber trees were generally similar during the defoliation period; low NDVI values due to the properties of the leaves characterized by oil palm trees which are produced in spiral succession with only opened leaves throughout their various stage of development (crown) (Verheye 2010). The low number of leaves counted characterized by oil palm trees were in conjunction with the leaf off properties examined in rubber trees during their defoliation period which caused their NDVI values to display similar pattern. In their foliation stage (Figure 3(b)), NDVI values from rubber trees rapidly recovered and exhibited higher values than those of oil palm trees. According to Dong et al. (2013), during this stage, rubber trees underwent rapid foliation and canopy recovery which suggests that rubber plantations can be used separated from natural evergreen forests. By hypothesis, during the rapid new leaf emergence of rubber trees, their NDVI values started to surpass those of oil palm trees while with evergreen trees, although their NDVI values were lower, there were still a significant level of differences in between their NDVI values during this period with the greatest differences in the NDVI between rubber trees and evergreen trees were found in the rubber trees defoliation period. During their growth period (Figure 3(c)), the pattern of NDVI values between rubber trees and evergreen trees were similar and sometimes, their values were overlay on top of one another. In

10 GEOCARTO INTERNATIONAL 635 Figure 3. NDVI plot of rubber trees, evergreen trees and oil palm trees in defoliation phase of rubber trees (a); foliation phase of rubber trees (b) and growing phase of rubber trees (c). contrary, the plot pattern of NDVI values in between rubber trees and oil palm trees showed a constant high of NDVI values for rubber trees which pinnacle over oil palm trees. When, their foliage covers were at maximum and dense crown crowding the upper envelope of the trees branches, the actual

11 636 J. A. BINTI. ABD RAZAK ET AL. Figure 4. EVI plot of rubber trees, evergreen trees and oil palm trees in defoliation phase of rubber trees (a); foliation phase of rubber trees (b) and growing phase of rubber trees (c). leaves properties characterized by rubber trees made their growing period to be more significant in distinguishing them from oil palm trees. (2) Enhanced Vegetation Index

12 GEOCARTO INTERNATIONAL 637 Figure 5. LAI plot of rubber trees, evergreen trees and oil palm trees in defoliation phase of rubber trees (a); foliation phase of rubber trees (b) and growing phase of rubber trees (c). EVI was formed to study MODIS imagery that theoretically improved upon the quality of the NDVI ratio. Whereas the NDVI is chlorophyll sensitive, the EVI is more responsive to canopy structural variations, including LAI, canopy type, plant physiognomy and canopy architecture (Baunman

13 638 J. A. BINTI. ABD RAZAK ET AL. 2009). During the defoliation of rubber trees (Figure 4(a)), the EVI values showed a variable pattern between the three types of vegetation as compared to NDVI. The low EVI values of rubber trees against evergreen trees occurred only from 2006 to Other years showed that the EVI values of rubber trees higher or coincided with that of evergreen trees. Against oil palm trees, the plot of EVI values between rubber and oil palm were not significantly different with the EVI values of oil palm trees. During foliation (Figure 4(b)), the plot of EVI values for the three vegetation types were nearly similar in pattern (no significant differences in their values), although the high and low EVI values between the three vegetation were interchangeable, except for certain dates; 22 April 2013; 25 April 2011 and 9 April 2011 where their EVI values were significantly different. In the growing period of rubber trees (Figure 4(c)), the EVI values between the three types of vegetation display a constant similar pattern in their plot and their values were almost all coincides with one another. However, there were irregular high and low EVI values between the three vegetation. The variability in the EVI values of the three vegetation (high and low values), especially during defoliation stage of rubber trees suggests that as compared to EVI, NDVI is more sensitive to low-density canopy and EVI is more sensitive to high-density canopy (Ji & Peters 2007). The performance of EVI metrics used in this study was slightly increased with the increased in the amount of foliage cover starting from rubber trees foliation period to their growing period. Although not apparent, as the correction for aerosol impact on the final index makes use of reflectance measurements within visible blue, the EVI specifically developed for MODIS data may produce a slightly different result for data acquired by another sensor (EUMeTrain 2010). (3) Leaf Area Index According to Asner et al. (2003), mean (± SD) LAI, distributed between 15 biome classes were ranged from 1.3 ± 0.9 for deserts to 8.7 ± 4.3 for tree plantations, with temperate evergreen forests (needleleaf and broadleaf) displaying the highest average LAI ( ) among the natural terrestrial vegetation classes. During growing period of rubber trees, their total vegetation surfaces are mainly composed of leaf area, lesser part of twigs, branches and stem surface. During the defoliation stage (Figure 5(a)), their woody parts determine vegetation surface area. In the 10 years of data acquisition, the lowest and highest LAI were both recorded by rubber trees; (7 February 2010) and (26 March 2012), respectively, which occurred on their estimated defoliation stage. In their estimated defoliation stage, mostly, the LAI values of rubber trees were lower than that of evergreen trees and oil palm trees. Only on certain dates; 23 March 2013 and 23 February 2012, their LAI values overtook the two vegetations. With the bounce in their foliage cover, the LAI for rubber trees showed similar pattern in their LAI values with evergreen trees beginning from their foliation to their growing stage (Figure 5(b) and (c)). The changes in reflectance during the foliation period were caused by changes in the number of young leaves, as these have different reflectance properties (Caldararu et al. 2012). The only significant variable in their LAI values was shown in the estimated foliation period; 25 April With the absence of ground data, this anomaly could only be hypothesized due to the conditions of the rubber trees themselves as based on correlative and biogeographical analyses, LAI is strongly tied to site water balance and nutrient status (Scheffer et al. 2005; Woodward & Williams 1987). Additional sources of variations in LAI values include age, disturbance history, soil texture and genetic material (Baldocchi & Meyers 1998; Eamus & Prior 2001). In the growing period of rubber trees, the LAI values among the three vegetation exhibit almost similar LAI values with oil palm trees mostly had the highest values. This may be due to the needleleaf characteristic of oil palm tree; 1.3 m long, 6 cm broad (Corley & Tinker 2015) and broadleaf characteristic of both evergreen and rubber trees (15 20 cm) that cause the differences in their LAI values. The variations in LAI values may also due to the high-biomass conditions observed in the study area (various land cover types). Previous studies had shown LAI retrievals of vegetation often have saturation problems in areas where LAI becomes insensitive to changes in reflectance (Caldararu et al. 2012).

14 GEOCARTO INTERNATIONAL 639 Figure 6. RGRI plot of rubber trees, evergreen trees and oil palm trees in defoliation phase of rubber trees (a); foliation phase of rubber trees (b) and growing phase of rubber trees (c). (4) Red Green Ratio Index RGRI was employed in this study to observe the change in anthocyanin pigmentation for the three types of vegetation. Increased amounts of anthocyanin pigments occur during early leaf development (Gamon & Surfus 1999). Anthocyanins are non-photosynthetic water-soluble pigments associated

15 640 J. A. BINTI. ABD RAZAK ET AL. with the resistance of plants to environmental stresses such as drought, low soil nutrients, high radiation, herbivores and pathogens. The highest value of RGRI observed was recorded by oil palm trees during the foliation period of rubber trees (Figure 6(b)) at (12 April 2015), while the lowest value recorded was by evergreen trees during the growing period (Figure 6(c)) of rubber (12 August 2010). For rubber trees, low values of RGRI (high anthocyanin) were mostly observed during their defoliation (Figure 6(a)) and foliation period which correspond to the physiological conditions of the trees during the start of new foliage development. A pronounced high of anthocyanin pigment content in newly expanding leaves suggest that anthocyanins and xanthophyll cycle pigments serve complementary photoprotective roles during early leaf development. During the three phenological cycles, oil palm trees recorded high RGRI values in most of the acquired dates. This suggest that the cropland consist mature trees with low anthocyanin content as senescing older leaves tended to have a higher NIR reflectance (Gamon & Surfus 1999). Although rubber trees had lower in values than oil palm trees, on most of the acquired dates, their RGRI values showed similar high values pattern, a significant indicator of their stand ages (mature rubber trees). This condition was not applied to natural evergreen tress as although their ages may already outlive the two croplands, they have longer life cycle than the two types of vegetation and would take years before they undergone their senescence stage Statistical test: correlation analysis The strength of relationships between the three vegetation was tested using the Pearson productmoment correlation coefficient. The scatter plots and correlation coefficients were computed between; rubber trees against evergreen trees and rubber trees against oil palm trees. Guide from Evans (1996) was used as a metric to denote the strength of each variable relationships ( = very weak; = weak; = moderate; = strong; = very strong). During defoliation of rubber trees (Figure 7(a) and (b)), the relationship between rubber and evergreen trees; r 2 = showed a weak correlation. For rubber and oil palm trees, the computed r 2 = suggested that the pair had a moderately positive correlation. During foliation, the relationship between rubber and evergreen trees (Figure 7(c) and (d)) continued to show a weak relationship; r 2 = On the contrary, the relationship between rubber and oil palm trees showed a strong correlation; r 2 = The weak and strong relationship pattern between rubber against evergreen trees and rubber against oil palm trees interchanged in the growing period of rubber trees (Figure 7(e) and (f)). With; r 2 = , the variables pair of rubber and evergreen trees showed strong positive relationship while the scatter plot of rubber against oil palm trees showed a weak correlation at r 2 = The statistical test results showed that at each phenological cycle of rubber trees, there are strong possibilities of distinguishing between either evergreen trees or oil palm trees against rubber trees which can be manipulated to improve the overall accuracy in mapping the distribution of rubber trees Vegetation dynamics of rubber trees from different fitting methods using PhenoSAT software PhenoSAT software was used to extract rubber trees vegetation dynamics from the NDVI time series data in the year 2014 (Figure 8(a)). The result displayed a fitted time series data that were smoothed using several noise filtering techniques. As different noise filtering methods computed different dates for the seven phenological cycles; Start of Season (SOS), Left Inflexion Point, Right Inflexion Point, Maturity, Maximum Vegetation Development, Senescence and End of Season in the main growing season of rubber trees, another plot was made to determine the dates representing the seven phenological cycles which were frequently coincides with most of the noise filtering techniques (Figure 8(b)). As been revealed by the previous analysis, there were clear distinction between the NDVI values of rubber trees and evergreen trees during the months where defoliation and foliation stages of rubber trees are commonly occurred (January until April) and the differences in NDVI values between rubber trees also occurred during the growing period of rubber trees (June

16 GEOCARTO INTERNATIONAL 641 Figure 7. NDVI scatter plot; rubber trees against evergreen trees during defoliation (a); rubber trees against oil palm trees during defoliation (b); rubber trees against evergreen trees during foliation (c); rubber trees against oil palm trees during foliation (d); rubber trees against evergreen trees in the growing period of rubber (e) and rubber trees against oil palm trees in the growing period of rubber (f).

17 642 J. A. BINTI. ABD RAZAK ET AL. Figure 8. The NDVI values extracted from the three vegetation of interests for the whole months in 2014 (a) and the plot of the seven phenological cycles using six different fitting methods; cubic smoothing splines (CSS), polynomial curve fitting (PCF), Gaussian models (GMs), Fourier series (FS), piecewise logistic (PL), Savitzky-Golay (SG) against day of the year (DOY) which had been computed by the six available fitting techniques from PhenoSAT software (b). until November). The phenological cycles of interests here were the Start of Season (SOS) and End of Season (EOS) which correspond to the foliation and defoliation stages of rubber trees. From the plot, most of the fitting techniques used predicted months between December until April for the EOS

18 GEOCARTO INTERNATIONAL 643 and SOS of the rubber trees; SOS was predicted for NDVI extracted on the 27, 35 and 67 day of the year and EOS was predicted on 267, 291, 307 and 339 day of the year. However, the predicted EOS as September (267), October (291) and early November (307) could be a biased prediction from the fitting techniques applied as the estimation of vegetation dynamics could result in a false vegetation regrowth by PhenoSAT which could be influenced by adverse weather conditions such as extreme heat or irregular precipitation (Rodrigues et al. 2013) which is common for a country that geographically situated near the equator such as Malaysia Comparing spectral reflectance of rubber trees, evergreen trees and oil palm trees from endmember extraction Prior to supervised classification routines, several training ROIs for the three vegetation of interests; rubber trees, evergreen trees and oil palm trees were selected from recent Landsat time series images in These images represented the three main phenological stages of rubber; defoliation (27 March), foliation (12 April) and their growing period (30 May and 18 August). The training ROIs were used to develop end-member spectra to be plotted against each other in the spectral reflectance curve. Visual analysis from the plotted spectral reflectance curves of the three vegetation spectra collected showed that on 27 March (defoliation), the spectra of rubber trees and evergreen trees were significantly distinguished starting from red region (~0.65 μm) and ongoing consistently until the NIR (~ μm) and SWIR regions (~ μm). The spectra of rubber trees against oil palm trees were nearly similar (their spectral reflectance curves coincide onto each other) with the differences in their spectra were only observed on the green band at ~0.55 μm and NIR at ~ μm. Similar pattern was also observed during the foliation period on 12 April, whereby the spectra between evergreen trees and rubber trees were significantly distinguished while the differences in spectral reflectance curve of rubber trees against oil palm trees were starting to show. It is to be noted however, the differences in their spectra is very small as compared to the differences observed during the growing period of rubber. In rubber trees growing season, e.g. on 30 May, the differences in between oil palm and rubber trees spectra were more highlighted especially in the SWIR region (~ μm) in which the reflectance values for oil palm trees were lower than rubber trees. The observed pattern continued during most of rubber trees growing seasons as the spectral reflectance curves of both vegetation extracted from the ROIs end-member spectra on 18 August reveal similar results. For spectra of evergreen trees against rubber trees, although the reflectance values for rubber trees were higher than the reflectance values for evergreen trees, their spectra were not significantly differed as compared to the differences observed during their defoliation and foliation stages. The most significant difference in their spectra was observed only on one particular point in the SWIR region; ~1.65 μm. Through all the phenological stages of rubber, the spectra between evergreen trees and oil palm trees showed similar pattern in their spectral reflectance curves with no significant distinction for a particular wavelength. The differences observed was only the reflectance values of oil palm which were higher than the values for evergreen trees. This analysis was proven by the ROI separability assessment made during the classification between the three vegetation which affect the overall producer and user accuracy results between these two vegetations Classification results for different rubber trees phenological cycles Recent four Landsat OLI images in the year 2015 were used to experiment the identification of rubber trees during three phenological stages; defoliation, foliation and the growing period. As there were no cloud free images available in the defoliation stage, an image with ~10% cloud cover on 27 March was used. The rest of the images; 12 April (foliation), 30 May and 18 August (growing period) were cloud free images. ROI separability scores used to compute the spectral separability between selected ROI pairs for images used in the SVM classification range from 0 to 2.0, providing an indicator how

19 644 J. A. BINTI. ABD RAZAK ET AL. Table 2. Error matrix computed for the classification on 27 March. Ground truth (pixels) Class Rubber trees Evergreen trees Oil palm trees Total classified pixels Rubber trees Evergreen trees Oil palm trees Total ground truth pixels Producer accuracy (per cent) User accuracy (per cent) Overall accuracy = (2574/2776) % Kappa coefficient = Table 3. Error matrix computed for the classification on April 12. Ground truth (pixels) Class Rubber trees Evergreen trees Oil palm trees Rubber trees Rubber trees Evergreen trees Oil palm trees Total ground truth pixels Producer accuracy (per cent) User accuracy (per cent) Overall accuracy = (2574/2776) % Kappa coefficient = Table 4. Error matrix computed for the classification on May 30. Ground truth (pixels) Class Rubber trees Evergreen trees Oil palm trees Total classified pixels Rubber trees Evergreen trees Oil palm trees Total ground truth pixels Producer accuracy (per cent) User accuracy (per cent) Overall accuracy = (2574/2776) % kappa coefficient = Table 5. Error matrix computed for the classification on Aug 18. Ground truth (pixels) Class Rubber trees Evergreen trees Oil palm trees Total classified pixels Rubber trees Evergreen trees Oil palm trees Total ground truth pixels Producer accuracy (per cent) User accuracy (per cent) Overall accuracy = (2574/2776) % Kappa coefficient = well the selected ROI pairs are statistically separate. Values greater than 1.9 indicate that the ROI pairs have good separability (Jeffreys 1946; Richards 1999). Most pairs have good separability with scores more than 1.9 except for Evergreen Trees/ Oil Palm Trees with the highest score of 1.83 on 27 March and the lowest score of 1.73 on 30 May due to the similarly spectral pattern. The classification results using SVM classifier produced different accuracies. The overall accuracy for classification on 27 March (Table 2), April 12 (Table 3), May 30 (Table 4) and 18 August (Table 5) were 91.92, 90.10, and 97.08% with the Kappa coefficient of 0.88, 0.85, 0.87 and 0.96, respectively.

20 GEOCARTO INTERNATIONAL 645 Figure 9. Classification results of the distribution of rubber, evergreen and oil palm trees at different phenological cycles. For rubber trees, their highest combined producer s and user s accuracies were on 12 April (foliation) with 96.58% (producer) and 99.73% (user). For evergreen trees, the highest accuracies were on the defoliation period (27 March) at 98.49% (producer) and 87.02% (user). Instead of 18 August which provided better user s and producer s accuracies at and 97.23%, 27 March was chosen for better delineation of evergreen trees as although the image was influenced by cloud cover and shadows (mostly removed) the accuracies were still high and could provide more accurate classification result if not influenced by these variables as proved by previous studies (Senf et al. 2013; Kou et al. 2015). For oil palm trees, the highest user s and producer s accuracies were observed on 18 August (growing stage) at and 93.81%, respectively. From the classification results (Figure 9), it was found that for rubber trees against the natural evergreen trees, the two vegetations were better distinguished during either the defoliation or foliation stages of rubber. For rubber trees against oil palm trees, image acquired during the growing period of rubber (between May to October) provide better accuracy in delineating the two vegetations. For most of the classification results, their accuracies were affected by the misclassification between oil palm and evergreen trees. Based on J-M separability scores and the accuracy assessment results using error matrix, the best period to classify the two vegetations was during the defoliation stage of rubber as low misclassified pixels were observed between the two vegetations Classification of rubber trees with different stand ages According to the confusion matrix created from the ground truth ROIs, the overall accuracy for rubber trees stand ages classification is 79.36% with the Kappa coefficient of (Table 6). The user s and producer s accuracies for young rubber trees (< 6 years old) are and 96.10%, respectively, while the user s and producer s accuracies for mature rubber trees (> 6 years old) are and 71.22%,

21 646 J. A. BINTI. ABD RAZAK ET AL. Table 6. Error matrix computed for the rubber trees stand ages classification. Ground truth (pixels) Class Mature (> 6 years) Young (< 6 years) Total classified pixel Mature rubber (> 6 years) Young rubber (< 6 years) Total ground truth pixels Producer accuracy (per cent) User accuracy (per cent) Overall accuracy= (2574/2776) % Kappa coefficient = Figure 10. Distribution of rubber trees with different stand ages (young rubber trees; < 6 years old and mature rubber trees; > 6 years old), evergreen trees and oil palm trees. respectively. These low accuracies were caused by the difficulties in developing ground truth ROIs from Google Earth image (J-M separability scores of 1.33) and the inexistence of ground truth data. Majority of the rubber trees in the study area consist of mature rubber trees and thus, even with the aid of Google Earth time slider and Landsat images of different time period (2006/2015), it was highly difficult to develop ROIs on particular young rubber trees. In rubber trees distribution map (Figure 10), the distribution of oil palm trees and evergreen trees were extracted from the classification results with high accuracies (27 March for evergreen trees and 18 August for oil palm trees). The LAI image was also overlaid onto rubber trees classification image on 12 April (highest accuracy of rubber trees distribution), thus making this map a high accuracy map that displays the distribution of the three vegetation Suitable strategy for accurate mapping of rubber plantations Based on the analysis of the vegetation indices, statistical analysis from the NDVIs and the classification results of the three vegetation types, a suitable strategy was suggested for more accurate mapping of rubber plantations. The strategy includes; (1) classical supervised classification techniques with their routines can be employed to accurately classify rubber trees based on the following reasons; (a) during defoliation stage of rubber, it is the best period to discriminate rubber trees and evergreen trees. However, the area extend should not consists of oil palm trees; (b) during growing period of rubber,

22 GEOCARTO INTERNATIONAL 647 Figure 11. The result of threshold-based classification using NDVI values. Table 7. Error matrix from image segmentation results using NDVI threshold values. User class Rubber trees Evergreen trees Oil palm trees Sum Error matrix Rubber trees Evergreen trees Oil palm trees Unclassified Sum Accuracy Producer User Hellden Short Kia per class Totals Overall accuracy KIA it is the best period to discriminate rubber trees and oil palm trees. However, the area extend should not consists of evergreen trees. The first suggested strategy can be best adopted for homogenous area, meaning, a geographical area extent which majorly constitutes two different types of vegetation only e.g. area made up of either natural evergreen forests and rubber plantations or area fairly distributed with rubber plantations and oil palm plantations. (2) For area fairly distributed with rubber, evergreen, oil palm trees and other vegetation types (heterogeneous or mixed area), the following rules can be adopted; (a) although complex and time consuming, classification methods such as decision tree and rule-based Object Based Image Classification (OBIA) are better to be employed particularly during the defoliation stage of rubber trees that best distinguish between rubber trees and evergreen trees. For spectrally similar oil palm trees (during the growing period of rubber trees), a texture and spatial based-rule can be developed to differentiate them. However, it is not a best practice to employ OBIA for low to medium resolution image with