Discriminating trees species from the relationship between spectral reflectance and chlorophyll contents of mangrove forest in Malaysia

Ecological Indicators 111 (2020) 106024

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Discriminating trees species from the relationship between spectral reflectance and chlorophyll contents of mangrove forest in Malaysia

T

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A.W. Zulfaa, K. Norizaha,b, , O. Hamdanc, S. Zulkiflyd, I. Faridah-Hanuma, P.P. Rhymaa a

Faculty of Forestry, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia Institute of Tropical Forestry and Forest Product (INTROP), Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia c Forest Research Institute Malaysia (FRIM), 52109 Kepong, Selangor Darul Ehsan, Malaysia d Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia b

A R T I C LE I N FO

A B S T R A C T

Keywords: Chlorophyll content EMR Species mapping Remote sensing Matang Mangrove Forest

In situ spectral signatures of 19 mangrove species were measured to investigate whether mangrove species could be discriminated through spectral reflectance data. The study was conducted at Matang Mangrove Forest Reserve and the spectral signatures were recorded by using a handheld spectroradiometer. The reflectance data was analysed using one-way ANOVA to identify bands that exhibit significant difference (at 99.99% level) across the mangrove species. Potential important wave bands that can be used to discriminate mangrove species were identified by using linear discriminant analysis (LDA) (at 99.95% level). The study successfully discriminated 7 wave bands within visible region (400–700 nm), 9 wave bands within NIR region (701–1000 nm), 16 wave bands within SWIR-1 region (1001–1830 nm), and 19 wave bands within SWIR-2 region (1831–2500 nm). Previous studies indicated that the leaf spectral reflectance for mangrove species was reported to provide poor reflectance at visible region (400–700 nm) due to high chlorophyll concentration. Leaf surface reflectance appeared to be the most important factor in this variation. By conducting the laboratory measurement of leaf chlorophyll contents at three different absorbance viz. 1) A662, 2) A663, and 3) A645, the relationship with spectral reflectance of individual mangrove species was identified. Overall, spectral reflectance measurement pairing with leaf chlorophyll measurement provides a sound basis for classifying mangrove trees species (R2 > 80%). The study has shown that there are possibilities for discriminating mangrove trees species from chlorophyll content-to spectra linkages.

1. Introduction Various earth’s objects can be identified, delineated and classified from spectral reflectance signature of remote sensing. Sufficient spectral resolution provided by the sensing system and good interaction with the objects gives accurate estimation of what the objects are. Spectral response of an object can be acquired by measuring the energy that is reflected or emitted by objects on the earth’s surface over a variety of different wavelengths (Aggarwal, 2004). Understanding their spectral response(s) in different parts of the electromagnetic spectrum is the first step in observing our world remotely with rapid and meaningful estimates. However, various structural individual characteristics of the same object may provide different spectral responses, thus extensive calibration for each object is required. The complexity involved in separating the spectral reflectance from homogenous species of mangrove forest gives uncertain accuracy to

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map detailed individual mangrove at species level (Kamal et al., 2015; Wang et al., 2004; Liu et al., 2008; Heumann, 2011). According to Sims and Gamon (2002), uncertainty of spectral reflectance for plants could be due to the limited spectral signature and relatively sensitive reflectance to species and leaf structure variation. Ajithkumar et al. (2008) and Blasco and Aizpuru (2002) stated that the spectral reflectance of the mangrove leaf is affected by chlorophyll content; a high chlorophyll concentration gives lower reflectance value, and thus it is difficult to discriminate the mangrove species. Meanwhile, Poompozhil and Kumarasamy (2014) stressed that thick cuticle and water storage tissue of mangrove species further hinder the satellite sensor to retrieve the reflectance and capture images, and thus, the sensor cannot be simply used for species distribution mapping with multi spectral imageries (Peng et al., 2018; Ajithkumar et al., 2008). In order to overcome this complexity, discriminating mangrove species can be done by using advance technique and algorithm approach, as suggested by several

Corresponding author at: Faculty of Forestry, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. E-mail address: [email protected] (K. Norizah).

https://doi.org/10.1016/j.ecolind.2019.106024 Received 21 June 2019; Received in revised form 12 December 2019; Accepted 15 December 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.

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Fig. 1. In-situ spectral data collection in four regions of MMFR, Perak, Malaysia.

enhanced thousands of narrow, contiguous wave bands in forests (Peng et al., 2018; Asner et al., 2012; Blackburn 2007), crops and grasses (Feng et al., 2017; Aboelghar et al., 2013, Schmidt & Skidmore, 2003; Sims and Gamon, 2002) and wetlands (Klančnik and Gaberščik, 2016; Zhang et al., 2014; Chun et al., 2011; Wang and Sousa 2009). There were few hyperspectral measurement studies have been conducted on mangrove forest. For example, Wang and Sousa (2009) suggested that the accuracy of mangrove species classification can be increased by using a narrow band of hyperspectral data. Laboratory study conducted for mangrove leaves using a high-resolution spectrometer in the Carribean coast showed the most useful bands for mangrove species classification at 780, 790, 800, 1480, 1530 and 1550 nm with linear discriminant analysis (LDA). At the same time, the

researchers. For instance, quantifying pigments from laboratory spectroscopy data has recently proposed with the advances of narrow band extracted from hypersepctral remotely-sensed imagery of vegetation (Blackburn, 2007). Via this technique, spectra reflectance of selected species will be acquired depending on varied chlorophyll contents. This measurement of spectra reflectance in identifying plant species is called non-destructive technique also known as rapid and can be used at global scales (Sims and Gamon, 2002). Relationship between spectra reflectance and chlorophyll contents gained for each species may varied and this was due to different structures of leaves such as leaf thickness, density, number of air water interfaces, cuticle thickness and pubescence. The uses of hyperspectral data were clearly improved by integrating the laboratory measurement of plant chlorophyll by the 2

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connected to the laptop for spectral curve analysis; iv. The leaf samples were clipped using the handheld field portable spectroradiometer. Each measure consists of an average of three readings, five measures were taken for each canopy. v. The spectroradiometer was calibrated to ensure the measured spectral curve was consistently good enough. White Spectralon panel was used as the white reference for calibration, and vi. Wave bands extracted from handheld spectroradiometer would then be used and examined to discriminate the mangrove species and compared with the leaf chlorophylls contents at four regions of MMFR. The spectral reflectance used for the measurement ranged within 350–2500 nm with a total of 976 wave band used for the analysis. As the original measurement has no standardised interval (i.e., 350–980 nm at 0.9 nm to 1.6 nm interval; 981–2500 nm at 2.5 nm to 4.0 nm interval), the wave bands were proportionated at every 10 nm interval and an average reflectance was computed for each interval to reduce system noise and redundancy between the adjacent bands with a total of 216 wave band for the analysis. vii. Leaf samples then were stored in plastic bags and placed on ice immediately for chlorophyll analysis in the laboratory.

researchers diagnosed stress condition across mangrove species using four narrow band ratios (R695/R420, R605/R760, R695/R760 and R710/ R760) and revealed that at least one ratio index was proven useful from ANOVA. In a paper by Zhang et al. (2014), seven wave bands (520, 560, 650, 710, 760, 2100 and 2230 nm) measured using FieldSpec® 3JR spectrometer were selected based on the principle component analysis (PCA) and stepwise discriminant analyses to classify mangrove species in Mexico. An overall accuracy higher than 90% and a Khat coefficient higher than 0.9 confirmed that the wave band selected was able to identify the mangrove species and mangrove forest conditions. Meanwhile, an accuracy higher than 80% conducted in their study confirmed the stress of two mangrove forest conditions; poor and dwarf. Due to the high resolution remotely sensor used by the enhanced thousands of narrow, contiguous wave bands with the measurement of chlorophyll content, the aforementioned studies have provided great potential for discriminating mangrove canopies of differing species composition and for detecting mangrove forest condition. However, the generality of those result needs to be determined at our study site on Matang Mangrove Forest Reserve (MMFR) of Malaysia. MMFR was characterized with > 103 exclusive and non-exclusive mangrove species (Roslan et al., 2014). This number shows how important the advances in digital image processing algorithm in assisting the inventory of mangrove species resources as well as managing the area over time. Realising the importance of spectral information in species discrimination from recent availability of higher spatial and spectral resolution data of optical sensors, hyperspectral measurement was conducted to identify the most effective wave bands and spectral regions for discriminating mangrove tree species, and to find out if there are possibilities that contiguous wave bands of hyperspectral data; in this study, we used handheld optical satellite sensors of spectroradiometer could discriminate mangrove tree species that typically have poor reflectance due to high chlorophyll concentration.

The total number of samples collected for each species was not equal because some species were sometimes difficult to locate in extremely tidal event and muddy habitat. In areas where trees were not reachable due to being on tree tops, the leaves were taken from the same tree species growing in open areas, either at the water edge or along the roadside; landwards. According to Wang and Sousa (2009), these trees may experience similar levels of incident sunlight as the upper canopy of taller trees. In addition, some of the taller trees were felled within the production area with the help of some forestry staff to get the leaf samples. It is crucial to note that the area was undergoing timber harvesting during our sampling process. The total sample sizes are tabulated in Table 1 below.

2. Methods 2.1. Study site

2.3. Data analysis

The study was conducted at Matang Mangrove Forest Reserve (MMFR). The MMFR is located in the Northern region of Peninsular Malaysia, located at longitude 4°56′03.54″ N to 4°32′10.81″ N and latitude 100°28′33.26″ E 100°37′40.54″ E. The MMFR is the largest mangrove forest in Peninsular Malaysia with a total area of 40,288 ha (Roslan et al., 2014). In situ spectral data collection was taken at four different regions within MMFR, i.e. Kuala Gula, Kuala Sepetang, Kuala Trong and Sungai Kerang. These regions are separated by administrative boundaries, known as Range (Fig. 1).

A series of one-way ANOVA was conducted to compare the spectral reflectance of 976 wavebands of 19 mangrove species at four MMFR regions; Kuala Gula (n = 9 individual species), Kuala Sepetang (n = 9 individual species), Kuala Trong (n = 9 individual species) and Sungai Kerang (n = 6 individual species). Mangrove species were used as the independent variables. The species that proved to be significant at 99.99% of confidence level from the tested bands were included in Linear Discriminant Analysis (LDA) and further compared to leaf chlorophylls content. According to Blackard et al. (2013), Wang and Sousa (2009), Everingham et al. (2007), and Mathur et al. (2002), LDA can be a useful statistical method to discriminate vegetation characteristics.

2.2. Leaf collection and spectral measurements Leaves of dominant species (listed in MMFR work plan; Roslan et al., 2014) were sampled from 28 March to 8 April 2016, and in situ spectral measurement was conducted as described below following Prasad and Gnanappazham (2015) and Asner et al. (2012).

2.4. Chlorophyll analysis The leaves of all 19 mangrove species underwent chlorophyll analysis in the laboratory. The chlorophyll content was estimated based on the protocol by Asner et al. (2012), which was downloaded from Carnegie Spectranomics website (https://cao.carnegiescience.edu/ spectranomics-protocols). Three different absorbance were used for this analysis viz. 1) A662, 2) A663, and 3) A645. This absorbance was selected based on the maximum absorbance of chlorophyll, as suggested in the protocol and reports given in some previous studies (i.e., Johan et al., 2014; Şükran et al., 1998). Later, the absorption of total concentration of chlorophyll [Eq. (3)] was calculated based on chlorophyll A and chlorophyll B [Eqs. (1)–(2)], following the equation of Lichtenthaler and Buschmann (2001). A total of 70 samples was analysed for each species.

i. 19 species from 70 individual trees were collected across MMFR; the species chosen varied from seaward to landward region; ii. Five matured leaves from the upper parts of the canopy surface of each tree were collected during full sun (11 am–2 pm) to ensure that the leaf samples had enough energy to undertake photosynthesis process; plugged using long pole; iii. The leaf samples were immediately transported to a local site for electromagnetic reflectance measurement; 20 min’ maximum was allowed to maintain the original leaf quality and freshness. The measurement tool was Analytical Spectral Devices (ASD) FieldSpec 3 Spectroradiometer that was supported by a tripod stand and 3

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Table 1 Number of samples collected within MMFR. Species and common name

Region

Acrostichum aureum (Piai raya) Avicennia alba (Api-api putih) Avicennia offinalis (Api-api ludat) Avicennia sp. (Api-api) Barringtonia asiatica (Putat) Bruguiera cylindrica (Berus) Bruguiera gymnorrhiza (Tumu merah) Bruguiera parviflora (Lenggadai) Bruguiera sexangula (Tumu putih) Ceriop tagal (Tengar) Excoecaria agallocha (Bebuta) Instia bijuga (Merbau ipil) Pouteria obovata (Nyatoh laut/ Menasi) Rhizophora apiculata (Bakau minyak) Rhizophora mucronata (Bakau kurap) Sonneratia caseolaris (Berembang) Sonneratia ovata (Gedabu) Xylocarpus granatum (Nyireh bunga) Xylocarpus malaccensis (Nyireh batu)

Chl Aconcentration(μgml − 1) =

ChlBconcentration(μgml − 1) =

Kuala Gula

Kuala Sepetang

Kuala Trong

Sungai Kerang

– 4 – – – 3 2 2 – – 2 – – 4 2 2 1 – –

– – 1 – – 2 1 1 – 1 – – – 2 2 1 – 2 –

– – – – 2 – – – 2 – 2 2 2 4 2 2 – 1 1

1 – – 4 – – 2 2 – – – – – 4 2 – – – –

12.21(A663 ) − 2.81(A646 ) × 25ml 1000ml × W (g )

20.13(A646 ) − 5.03(A663 ) × 25ml 1000ml × W (g )

Concentrationofchlorophyll(mgg − 1freshweight) 20.2(A645 ) + 8.02(A663 ) × 25ml = 1000ml × W (g )

rapidly at red-edge leading to a plateau. At this stage, leaf chlorophyll no longer absorb radiation. Generally, all species shows similar reflectance pattern. Thus, we conducted one-way ANOVA to determine significant influence of wave bands for individual mangrove species at four different regions; Kuala Gula, Kuala Sepetang, Kuala Trong and Sungai Kerang. Fig. 3(a)–(e) present the wave bands which have the potential to discriminate mangrove species in MMFR by region after one-way ANOVA (p-values < 0.01). These bands were identified based on the different regions. Kuala Gula region had 59 significant wave bands within (1) 730–970 nm and (2) 990–1320 nm; Kuala Sepetang had 87 significant wave bands within (1) 1380–1880 nm and (2) 2030–2380 nm; Kuala Trong had 74 significant wave bands within (1) 1400–1550 nm, (2) 1780–1890 nm, and (3) 1950–2500 nm; Sungai Kerang had 63 significant wave bands within (1) 460–500 nm, (2) 660–670 nm, (3) 1360–1850 nm and (4) 2130–2180 nm. Here in MMFR, 182 wave bands were identified as significant within (1) 350–360 nm and (2) 710–2500 nm. The rest of the bands which did not show any variation to discriminate mangrove species were discarded from LDA. To best

(1)

(2)

(3)

where Ai is Absorbance (nm), and W is weight (gram). 3. Results 3.1. Leaf spectral reflectance The leaf reflectance curves for all 19 mangrove species was shown in Fig. 2. From the figure, all mangrove species have low reflectance within visible region; less than 700 nm. High reflectance was observed in Near Infrared (NIR) region where reflectance was observed to rise

Fig. 2. Mean relative reflectance spectra of the different mangrove species in MMFR. Spectra are means over 10 nm intervals. 4

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Fig. 3. (a)–(e): Distribution of significant wave band identifies from one-way ANOVA whose p-values are less than 0.01 (in blue) in the whole study area; four regions and whole MMFR.

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regions were due to the different number of samples collected and analysed.

characterise and classify the aforementioned significant bands that were found to be important to discriminate mangrove species, LDA was executed separately according to four regions of wave bands (Prasad and Gnanappazham, 2015). The regions are as follows: region 1: Visible (V) (wave bands range from 400 to 700 nm), region 2: Near Infrared (NIR) (wave bands range from 701 to 1000 nm), region 3: Short-wave infrared I (SWIR I) (wave bands range from 1001 to 1830 nm), and region 4: Short-wave infrared II (SWIR II) (wave bands range from 1831 to 2500 nm). There are several bands depicted as having significant influence to discriminate mangrove species at level 0.05%. These wave bands were ranked in the top 10 of the first two liner discriminants. Kuala Gula identified 14 wave bands as significant after LDA under region 2 and region 3, with 9 wave bands and 5 wave bands, respectively. Similarly, Kuala Sepetang and Kuala Trong also identified significant wave bands under 2 regions (namely, region 3 and region 4). Meanwhile, ten wave bands were found to be significant after LDA in Kuala Sepetang (3 wave bands within region 3, and 7 wave bands within region 4) and only 8 wave bands in Kuala Trong were analysed as significant (4 wave bands each within region 3 and region 4). Sungai Kerang exhibited the highest number of wave bands; 17 wave bands under 3 regions of region 1 (7 wave bands), region 3 (2 wave bands) and region 4 (8 wave bands) respectively. For the whole MMFR, only 7 wave bands were found to be significant, and all of them are under region 2 (with 2 wave bands), region 3 (with 3 wave bands) and region 4 (with 2 wave bands). Table 2 lists the final bands selected from LDA for four regions of MMFR and for the whole MMFR.

4. Discussion 4.1. Narrow band discrimination with LDA It is important to highlight that the vast amount of wave bands and relatively small numbers of training and test samples pose general challenges in narrow band discrimination analysis of hyperspectral data. By reducing the number of wave bands at the initial stage through one-way ANOVA, using species as the independent variables, this study has successfully identified significant wave bands to distinguish different mangrove species. LDA performed for this reduced number of significant wave bands has proven to be an effective procedure to best discriminate potential important wave bands within specific regions in which each mangrove species could be spectrally identified. Clark et al. (2005) suggested classification using LDA at leaf level to be more accurate than classification at crown level. At crown level, as reported by Ajithkumar et al. (2008), background reflection of water and sediment can be influential factors. Moreover, spatiotemporal changes in physico-chemical properties of water and changes in mineralogical composition, texture and moisture content of the soils and presence of heavy metals in the sediments may affect the classification accuracy (Peerzada and Rohoza, 1989). The LDA shows that discrimination of mangrove species within the visible and NIR region could only be done in the Sungai Kerang and Kuala Gula regions respectively. The relative high importance of the 7 wave bands in discriminating mangrove species within the visible range found in Sungai Kerang is in line with the previous studies conducted by Hauser et al. (2015) and Sobhan (2007). Studies by Prasad and Gnanappazham (2015) and Wang and Sousa (2009) also supported the potential important wave bands to discriminate mangrove species found within the NIR region in Kuala Gula region with 9 wave bands. Interestingly, different types of vegetation have also been reported to be successfully distinguished within these visible and NIR regions such as rice leaves (Feng et al., 2017), clover and maize (Aboelghar et al., 2013), degraded temperate vegetation (Peng et al., 2018) and saltmarsh vegetation (Schmidt & Skidmore, 2003). Although the LDA did not identify any wave bands as important to discriminate the mangrove species in the two regions of Kuala Sepetang and Kuala Trong within the visible and NIR regions, it is worth mentioning that for the overall mangrove species discrimination at MMFR, 2 wavebands were identified to be critical within NIR region; 790 nm and 810 nm. In addition, it is interesting to observe that our LDA found at SWIR-1 and SWIR-2 regions, 35 wave bands were identified as important spectra for mangrove species discrimination in all the four regions. Therefore, SWIR is more suitable for detecting more changes in water status, as suggested by Seelig et al. (2008) and Eitel et al. (2006). Since mangrove leaves are known to have high leaf hydraulic conductance (Reef & Lovelock, 2014), reflectance within this SWIR region is inevitable during in situ spectrometer measurement. In particular, the

3.2. Leaf Chlorophyll Variation All the leaf samples were tested for their chlorophyll contents under three different absorbance; A662, A663 and A645 (Johan et al., 2014; Şükran et al., 1998) Table 3 shows the relationship between spectra reflectance and total chlorophyll contents. Details graph can be found in Appendix 1. A total of 12 mangrove species found in the four regions showed high correlation between chlorophyll content and spectral reflectance with coefficient of determination (R2) is > 80%; highlighted with grey colour in Table 3. 8 species examined showed positive relationships, indicating that the higher the reflectance, there would be higher chlorophyll contents. Two species having positive relationship were found in Kuala Gula; Excoecaria agallocha and Rhizophora apiculata, 3 species; Bruguiera cylindrica, Bruguiera gymnorrhiza and Sonneratia caseolaris were found in Kuala Sepetang and 4 species in Kuala Trong; Xylocarpus malaccensis, Rhizophora apiculata, Sonneratia caseolaris and Pouteria obovata. Sungai Kerang observed as having 5 species to have positive relationship and they were Avicennia spp, Bruguiera gymnorrhiza, Rhizophora apiculata, Rhizophora mucronata and Acrostichum aureum. While, 4 species were inversely correlated with total chlorophyll content, indicating that the higher the reflectance was, the lower the total chlorophyll content was. These four species were Avicennia offinalis, Ceriop tagal and Rhizophora apiculata found in Kuala Sepetang and Rhizophora mucronata found in Kuala Trong, Different relationship variations were observed for the same species at different

Table 2 Potential important wave bands identified from LDA in discriminating mangrove species. Region

Spectral region (Visible) 400–700 nm

(NIR) 701–1000 nm

(SWIR-1) 1001–1830 nm

(SWIR-2) 1831–2500 nm

Kuala Gula

–

1100, 1140, 1200, 1270, 1280

–

Kuala Sepetang Kuala Trong Sungai Kerang

– – 460, 470, 480, 490, 500, 660, 670 –

740, 770, 810, 860, 870, 900, 970, 990, 1000 – – –

1430, 1440, 1450 1400, 1410, 1420, 1810 1420, 2430

790, 810

1220, 1600, 1660

1840, 2370, 1840, 2180 1900,

MMFR

6

1850, 1870, 1880, 2310, 2350, 2360 2420, 2450, 2490 1850, 2130, 2140, 2150, 2160, 2170, 2030

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Table 3 The regression equations indicate total leaf chlorophyll contents, based on selected absorbance. R2

Region

Species

Regression equation

Kuala Gula (22)

Avicennia alba (Api-api putih) Excoecaria agallocha (Bebuta) Bruguiera cylindrica (Berus) Bruguiera gymnorrhiza (Tumu merah) Bruguiera parviflora (Lenggadai) Rhizophora apiculata (Bakau minyak) Rhizophora mucronata (Bakau kurap) Sonneratia caseolaris (Berembang) Sonneratia ovata (Gedabu)

Chll Chll Chll Chll Chll Chll Chll Chll Chll

= = = = = = = = =

−0.3482x + 0.482 3.2818x + 0.0417 4.1707x + 0.0935 5.3938x − 0.1132 4.8555x + 0.2451 2.725x + 0.0679 −1.1513x + 0.7469 0.7643x + 0.4215 3.2824x + 0.1359

0.0137 0.9380 0.6355 0.5949 0.7660 0.9605 0.0927 0.0384 0.6542

Kuala Sepetang (13)

Avicennia offinalis (Api-api ludat) Xylocarpus granatum (Nyireh bunga) Ceriop tagal (Tengar) Bruguiera cylindrica (Berus) Bruguiera gymnorrhiza (Tumu merah) Bruguiera parviflora (Lenggadai) Sonneratia caseolaris (Berembang) Rhizophora apiculata (Bakau minyak) Rhizophora mucronata (Bakau kurap)

Chll Chll Chll Chll Chll Chll Chll Chll Chll

= = = = = = = = =

6.6187x 2.1808x 4.0887x 3.7738x 2.2743x 6.6672x 4.6972x 6.3044x 1.6183x

− + − + + + + − +

0.0301 0.4401 0.0196 0.0197 0.277 0.1784 0.0909 0.1957 0.2631

0.8898 0.0885 0.9383 0.8280 0.9015 0.796 0.8508 0.8641 0.4062

Kuala Trong (20)

Excoecaria agllocha (Bebuta) Barringtonia asiatica (Putat) Xylocarpus malaccensis (Nyireh batu) Bruguiera sexangula (Tumu putih) Rhizophora apiculata (Bakau minyak) Rhizophora mucronata (Bakau kurap) Sonneratia caseolaris (Berembang) Pouteria obovata (Nyatoh laut/ Menasi) Instia bijuga (Merbau ipil)

Chll Chll Chll Chll Chll Chll Chll Chll Chll

= = = = = = = = =

3.7113x + 0.1604 11.92x − 0.6572 5.8305x + 0.1581 3.1325x + 0.2154 3.2641x + 0.1246 4.6656x − 0.0867 4.6096x + 0.0243 3.4315x + 0.6062 9.8377x − 0.4104

0.1287 0.2684 0.8205 0.7282 0.9804 0.9284 0.9572 0.9127 0.7086

Sungai Kerang (15)

Avicennia spp. (Api-api) Bruguiera gymnorrhiza (Tumu merah) Bruguiera parviflora (Lenggadai) Rhizophora apiculata (Bakau minyak) Rhizophora mucronata (Bakau kurap) Acrostichum aureum (Piai raya)

Chll Chll Chll Chll Chll Chll

= = = = = =

4.4275x 3.7168x 18.196x 2.3612x 3.1838x 5.7919x

0.8728 0.9166 0.3032 0.9596 0.9321 0.9332

+ + − + + +

0.0312 0.0365 0.4092 0.1548 0.0387 0.1298

correlated with the chlorophyll content (R2 > 80%), indicating that the higher the reflectance, the higher the chlorophyll content. This occurred because the leaves with high chlorophyll content absorbed lesser amount of light, causing the reflectance to increase (Feng et al., 2017). However, there were 4 species examined as having inverse correlation between the reflectance and chlorophyll content. These 4 species were observed in Kuala Sepetang and Kuala Trong. Peng et al. (2018) suggested frequent used domain in spectra for estimating leaf chlorophyll content was wave band ranged from 400 to 1000 nm. Our study demonstrated there were no significant wave band within 1000 nm in region of Kuala Sepetang and Kuala Trong. Thus, 4 species observed as having negative relationship in our study absorbed more light, causing the reflectance to decrease. Also, this might happen due to solar radiation during in situ measurement was not enough to provide energy for leave undertook their photosynthesis process. According to Blasco and Aizpuru (2002), in this situation, species discrimination might be difficult to be conducted via spectra measurement technique. Peng et al. (2018) found several spectra beyond 760 nm (NIR region) to be sensitive to leaf chlorophyll content, the finding which was also identified in our study. At NIR region (701–1000 nm), only species in Kuala Gula region showed sensitive relationship with spectra reflectance comprising of only 2 mangrove species; Rhizophora apiculata and Excoecaria allagocha. According to Slaton et al. (2001), the anatomical structures such as leaf thickness, cell walls, and intracellular air space can affect the NIR reflectance which also shows the content of chlorophyll (Pastor-guzman et al., 2015). In addition, Merzlyak et al. (2003) stressed that the NIR reflectance is known to be affected under the condition of high chlorophyll concentrations. The results in this study are in line with that of Peng et al. (2018), Feng et al. (2017), Hauser et al. (2015), and Asner et al. (2012), whereby 6 mangrove species (out of the 19 mangrove species sampled for chlorophyll content

SWIR reflectance is known to be affected by water vapour (Prasad and Gnanappazham, 2015; Wang and Sousa, 2009), which also can indicate stress vegetation (Wang nad Sousa, 2009) and chemical traits (Asner et al., 2012). Although we did not assess whether localized variation in water content were correlated with spectra reflectance in this study, Seelig et al. (2008) did find associations between leaf water content and reflectance within visible, NIR and SWIR region for peace lily plant. We thus suspect that there are similar association at work for mangrove species as leaves have similar characteristics (i.e., leaf thickness, density, number of air water interfaces, cuticle thickness, and pubescence). Detailed explanation on the SWIR region that has been discriminated as having influence on the mangrove species discrimination will be further investigated.

4.2. Spectra reflectance and chlorophylls content Blackburn (2007) suggested that at spectra below 500 nm, leaf pigments have low reflectance. Pigments no longer absorb radiation when the spectra are over 700 nm. This is when chlorophyll contents become high and energy released for photosynthesis process is increased, showing a healthy plant. Beck et al. (2000) and Sonobe and Wang (2017) described that chlorophyll strongly absorbs light at the blue (400–500 nm) and red (600–700 nm) regions. Also, Jensen (2009) suggested that primary chlorophyll absorption band occurs between 400 and 500 and 650–660 nm. The present study matched those findings. More specifically, our study showed that only 5 mangrove species were active during in situ measurement with the spectrometer over the visible region (400–700 nm). All the mangrove species investigated are sensitive to this visible region found in Sungai Kerang region and they are Rhizophora apiculata, Rhizhophora mucronata, Bruguirea gymnorrhiza, Avicennia sp. and Acrostichum aureum. The reflectance was directly 7

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measuring spectral reflectance in the field. Also, we thank anonymous referees for their valuable comments on the manuscript.

analysis and in situ measurement of leaf reflectance with spectrometer of hyperspectral data) showed sensitive relationships between spectra reflectance and chlorophyll content (R2 > 80%). The current study clearly shows that chlorophyll content-to spectra linkages of hyperspectral data gained from in situ spectrometer measurement have the advantages in distinguishing mangrove at species level. Through detail spectra properties gained from this relationship of spectra reflectance and chlorophyll contents, classification of mangrove species composition from remotely acquired sensor reflectance data (i.e., Landsat imagery, Spot imagery, Quickbird imagery, etc.) can be pursued with least cost and less time especially for ground validation. Efforts remotely to map mangrove forests by using recent availability of higher spatial and spectral resolution data of satellite sensors has stimulated researchers to produce more accurate map of mangrove at landscape scale. Notably, spectra properties are providing benefits in validating the map although there were present and growing threat to mangrove ecosystems and characterised with flooded forest which were often inaccessible by road and difficult to traverse on foot. Consequently, up-to-date information on mangrove species composition, species distributions and canopy condition can help conservation efforts effectively and within shorter time. Although we did not classify mangrove species with high spatial and spectral imagery in this study, spectra discrimination via chlorophyll contents measurement conducted in this study can be the first step towards our ultimate goal in individual species of mangrove mapping and monitoring at the landscape scale.

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5. Conclusion In this study, in situ measurement of narrow bands spectrometer was used to discriminate potential wave bands in distinguishing mangrove species in MMFR. In addition, the contents of leaf chlorophylls at 3 different absorbance of (1) A662, (2) A663, and (3) A645 were also examined. By combining potential important wave bands through LDA at 99.95% level, 8 individual mangrove species could be discriminated within the visible region (400–700 nm) and NIR region (701–1000 nm). This can be observed from the relationship between A645 and total chlorophyll content, where R2 was examined to be > 80%. Future work will encompass the potential important wave bands within the SWIR region to detect any water vapour, stress or chemical traits for mangrove species. Coverage in several different spectral bands increases classification effectiveness for vegetation. It is also possible to specify with reasonable precision those spectra regions that are of particular use and those regions in which data may be less valuable or are actually counter productive for certain applications. Such research will help us to better understand the reflectance of each mangrove species towards narrow bands of hyperspectral remotely sensed imagery and to extend the monitoring and mapping works on mangrove forests. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper Acknowledgements This study was supported through the Fundamental Research Grant Scheme (FRGS) of Ministry of Higher Education Malaysia, grants FRGS/ 1/2015/ST04/UPM/02/4: 01-01-15-1658FR; Vote 5524763. Chlorophyll analysis was performed in the Laboratory of Department of Biology of the Faculty of Science, Universiti Putra Malaysia, Malaysia. We gratefully thank Jamhuri Jamaluddin for assisting this research in the field and providing computer technical support for this research and Dr. Affendi Suhaili and his System and Application Unit (SADU) team from Sarawak Forestry Department for assisting this research in 8

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