Remote Sensing Letters ISSN: 2150-704X (Print) 2150-7058 (Online) Journal homepage: http://www.tandfonline.com/loi/trsl20 Improving large-scale moso bamboo mapping based on dense Landsat time series and auxiliary data: a case study in Fujian Province, China Chong Liu, Tianwei Xiong, Peng Gong & Shuhua Qi To cite this article: Chong Liu, Tianwei Xiong, Peng Gong & Shuhua Qi (2018) Improving large-scale moso bamboo mapping based on dense Landsat time series and auxiliary data: a case study in Fujian Province, China, Remote Sensing Letters, 9:1, 1-10, DOI: 10.1080/2150704X.2017.1378454 To link to this article: http://dx.doi.org/10.1080/2150704X.2017.1378454 Published online: 25 Oct 2017. Submit your article to this journal View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=trsl20 Download by: [UNSW Library] Date: 26 October 2017, At: 06:22 REMOTE SENSING LETTERS, 2018 VOL. 9, NO. 1, 1–10 https://doi.org/10.1080/2150704X.2017.1378454 Improving large-scale moso bamboo mapping based on dense Landsat time series and auxiliary data: a case study in Fujian Province, China Chong Liu a,b , Tianwei Xiongb, Peng Gongc and Shuhua Qia,b a Downloaded by [UNSW Library] at 06:22 26 October 2017 Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), Jiangxi Normal University, Nanchang, China; bSchool of Geography and Environment, Jiangxi Normal University, Nanchang, China; cMinistry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China ABSTRACT ARTICLE HISTORY Bamboo forest, especially moso bamboo forest, is very important to human society. However, our ability to detect large-scale moso bamboo with optical remote sensing is still limited due to the spectral similarity with other forest species and the inﬂuence of cloud occurrence. In this study, we examined the capability of dense Landsat time series for moso bamboo forest mapping by comparing three diﬀerent interpretation schemes . For each scheme, two experimental groups were further conducted to investigate the usefulness of gray-level co-occurrence matrix (GLCM) textures. Considering classiﬁcation accuracy, the full-season compositing strategy was regarded as the most eﬃcient. It was generally beneﬁcial to include GLCM textures as input features, although their usefulness would be partially oﬀset due to noise/correlation issues. We also investigated the roles of 15 types of auxiliary covariates in extracting moso bamboo and found some of them could enhance the classiﬁcation performance signiﬁcantly. With the full-season compositing scheme and crucial auxiliary covariates, an improved moso bamboo mapping performance (93.21% in overall accuracy and 73.97% in minimum accuracy) was observed within the study area. Our evaluation results are promising to provide robust guidelines for remote mapping of moso bamboo forest over large areas. Received 6 June 2017 Accepted 5 September 2017 1. Introduction Bamboos (Family Poaceae: Sub-family Bambusoideae) are woody grasses broadly distributed in the tropical, subtropical and mild temperate regions. Although these plants represent a relatively small fraction of the total forest area on earth, they play critical roles in the integrity of the global ecosystem. China is one of the richest countries in terms of bamboo coverage and diversity. Of the over 500 bamboo species found in China, moso bamboo (Phyllostachys pubescens) is the most abundant, occupying more than half of the nationwide bamboo area (Li et al. 2015). The advent of optical remote CONTACT Shuhua Qi email@example.com Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), Jiangxi Normal University, Nanchang, China © 2017 Informa UK Limited, trading as Taylor & Francis Group Downloaded by [UNSW Library] at 06:22 26 October 2017 2 C. LIU ET AL. sensing has revolutionized our ability to identify moso bamboo distribution in a spatially explicit manner. During past decades, extensive eﬀorts have been made on remote detection of moso bamboo in various ecosystems. However, reliable moso bamboo mapping to date still remains challenging because of two major issues. First, the spectral characteristic of moso bamboo makes it easily confused with other forest species. Second, moso bamboo is mostly distributed in subtropical East Asia where cloudy and rainy climates dominate, so it is diﬃcult to select satellite observations that are free of cloud impact for a speciﬁed time period. Several studies have demonstrated the advantages of using time series remote sensing data for terrestrial environment monitoring (Wilson and Sader 2002; Diao and Wang 2016). Compared to single-date remotely sensed data, multi-temporal image stacks are more likely to include essential phenological characteristics and overcome the cloud inﬂuence. With the opening of the Landsat archive from 2008, it is now possible to take advantage of dense time series satellite data to identify land cover distributions. Nevertheless, compared to other remote sensing analyses, the utilization of satellite time series for moso bamboo mapping is still in its infancy, and knowledge is particularly scarce on how the rich information carried by the dense satellite time series can be eﬃciently employed. In addition to the interpretation of time series images, auxiliary data is another key factor that impacts on moso bamboo mapping accuracy. Recent studies indicated that some geospatial covariates play an important role in the distribution of land cover classes, especially those characterized by climate and topography (Zhu et al. 2016; Dannenberg, Hakkenberg, and Song 2016). However, it is also noteworthy that the inclusion of irrelevant auxiliary data probably results in nothing beneﬁcial but increased computation burden (Li et al. 2016). Given that numerous auxiliary data are currently becoming available, there is an urgent need to evaluate their usefulness for moso bamboo identiﬁcation. The object of this study was to develop an improved framework to map large-scale moso bamboo with dense Landsat time series and auxiliary data. We examined the performances of diﬀerent image time series interpretation schemes, and then further investigated the importance of 15 types of auxiliary data which may facilitate remotely detecting moso bamboo. Based on acquired ﬁndings, a deﬁnitive moso bamboo mapping procedure was adopted and the result was compared to the reference data. 2. Material and methods 2.1. Study area and tools The study area covers the whole territory of Fujian Province, which is located on the southeast coast of mainland China with elevation gradients increasing from its southeast to northwest (Figure 1). Owing to the subtropical humid climate, Fujian has suitable temperature, rainfall and sunlight conditions for moso bamboo growth. As one of the national ‘hot spots’ of moso bamboo industry development, Fujian occupies approximately 1% of China’s land area, but contributes nearly one quarter of China’s moso bamboo yield (Li et al. 2015). We used the Google Earth Engine (GEE) to process remotely sensed data and generate ﬁnal mapping results. As a planetary-scale platform Downloaded by [UNSW Library] at 06:22 26 October 2017 REMOTE SENSING LETTERS 3 Figure 1. Geographic location of Fujian Province and the identiﬁcation of Landsat 8 image footprints included in this study. for environmental data and analysis, GEE provides trillions of scientiﬁc measurements and cloud computing resources. These advancements allow users to manipulate the archives of remote sensing data online and make large area research more feasible. In addition, in this study ArcGIS 10.3 was used to derive reference samples, and R software was used for classiﬁcation and statistical analysis. 2.2. Dataset and preprocessing Landsat 8 imagery was selected as our primary data source. Hosted on GEE, all available Landsat 8 surface reﬂectance data from 2014 to 2016 covering the study area were gathered. These images were calibrated and atmospherically corrected using the L8SR algorithm (Vermote et al. 2016). In all, we processed 781 Landsat 8 scenes with a range of seasonality and fractional cloud cover. To better discriminate moso bamboo from other forest plants, two spectral indices: the normalized diﬀerence vegetation index (NDVI, Tucker 1979) and the normalized diﬀerence moisture index (NDMI, Wilson and Sader 2002) were also calculated for each image scene. Downloaded by [UNSW Library] at 06:22 26 October 2017 4 C. LIU ET AL. Auxiliary data were collected and loosely grouped into four categories: topographic, vegetative, climatic and soil. Topographic covariates included elevation, slope and aspect, all of which were derived from the Shuttle Radar Topography Mission digital elevation model data (SRTM DEM). Vegetative covariates included the maximum, minimum, mean and standard deviation of NDVI and NDMI during the study period. Climatic covariates included precipitation, temperature and cloud frequency. To eliminate the inter-annual uncertainty, multi-year average values from 1980 to 2015 were used for precipitation and temperature data, while the cloud frequency was calculated based on CFmask results from all historical Landsat 4–8 imagery (Zhu and Woodcock 2012). Additionally, we included a single soil type layer as an auxiliary input. Among all auxiliary variables, topographic, vegetative and cloud frequency layers were directly obtained from GEE with 30 m resolution, while precipitation, temperature and soil data were downloaded from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (RESDC, http://www.resdc.cn) with 500 m resolution. Therefore, these three datasets were ﬁrst resampled to 30 m using a bilinear method before their further utilization. We adopted the third-time forest management inventory (3rd FMI) data as a basis to generate training and testing samples. Implemented by the provincial forestry department, the 3rd FMI results oﬀered comprehensive forest inventory metrics including moso bamboo distribution, area and culms quantity. The 3rd FMI data of Fujian was originally produced in 2007, and is updated annually. In this study, the latest 3rd FMI version from 2015 was used. Here we employed a stratiﬁed sampling procedure and collected a total of 8949 points. To avoid unnecessary computation and to limit our eﬀorts to forests, we created a non-forest mask following a similar procedure to Zhu, Woodcock, and Olofsson (2012). Based on the fact that forests tend to show high NDVI values but low reﬂectance in shortwave infrared (SWIR) bands, we masked an image pixel if it violated at least one of the criteria listed below: (1) NDVI mean value greater than 0.6; and (2) SWIR band 7 reﬂectance mean value less than 0.1. Here the mean value refers to the arithmetic average of all cloud-free observations acquired during the study period (2014–2016). 2.3. Initial classiﬁcation with diﬀerent interpretation schemes We tested three Landsat time series interpretation schemes in the study area (S1-S3 in Table 1). For S1, we simply selected the Landsat scene with the least cloud coverage from 2015. For S2, we adopted the same full-season strategy as Li et al. (2016), which implemented the S1 method for each season from 2015, and obtained four image scenes as classiﬁcation model inputs. For S3, we employed a pixel-level compositing strategy following previous research by White et al. (2014). For each season during 2014–2016, a quality score band based on two criteria (day of year score and distance to cloud score) were calculated, and the pixels with the largest quality scores were included in the compositing results. Here the purpose of using Landsat data from three years rather than one was to examine whether a suﬃciently large number of scenes is helpful to improve performance. For each scheme, we further designed two experimental groups to investigate the usefulness of gray-level co-occurrence matrix (GLCM) textures. To alleviate the potential correlation issue, a principal component analysis was conducted on Landsat bands, then REMOTE SENSING LETTERS 5 Table 1. Landsat time series interpretation scheme designed in this study. Scheme Group Image time series utilization strategy S1 I Only one image scene with the least cloud coverage during 2015 was used II Only one image scene with the least cloud coverage during 2015 was used, GLCMa textures were also added S2 I Four image scenes with the least cloud coverage from spring, summer, autumn and winter during 2015 were used II Four image scenes with the least cloud coverage from spring, summer, autumn and winter during 2015 were used, GLCM textures were also added S3 I Composited image based on four scenes with the greatest quality scoreb from spring, summer, autumn and winter during 2014–2016 were used II Composited image based on four scenes with the greatest quality score from spring, summer, autumn and winter during 2014–2016 were used, GLCM textures were also added Downloaded by [UNSW Library] at 06:22 26 October 2017 a. GLCM represents gray-level co-occurrence matrix. The derivation of GLCM textures in this study was based on Lu et al. (2014) and Li et al. (2016). b. The generation of a quality score was based on White et al. (2014). Sensor and opacity scores were not calculated because only Landsat 8 data were used. the ﬁrst component was used to generate four types of GLCM metrics (homogeneity, variance, contrast and entropy) with a moving square window size of 9 9, a direction of 45° and a grey-level of 64 (Lu et al. 2014; Li et al. 2016). The random forest classiﬁer (RFC) was chosen to create land cover maps and evaluate the inﬂuence of diﬀerent schemes on map accuracy. RFC is an ensemble learning method that produces a series of classiﬁcation trees using bootstrap samples with placement from training data (Breiman 2001). Due to reasonable prediction accuracy and ability of handling high dimensional feature spaces, this classiﬁer has been widely used in large-area mapping and proven to be particularly eﬀective when a large number of features is involved (Gong et al. 2013). Here we set RFC with 500 trees and the number of prediction variables equal to the square root of the number of total input variables. A 50-fold cross validation was performed with the reference sample set. For each classiﬁcation, 80% of the reference samples were randomly selected for model training, while the withheld 20% were used for accuracy assessment. This procedure was repeated 50 times and the statistical results of the accuracy measurements were adopted for the mapping result evaluation. The initial classiﬁcation performance was evaluated using two complementary metrics: overall accuracy (OA) and minimum accuracy (MA). OA refers to the proportion of correctly classiﬁed samples to the total sample set, while MA refers to the minimum of user’s accuracy and producer’s accuracy (Zhu et al. 2016). Our primary aim was to ﬁnd the best interpretation scheme that could mostly elevate MA without sacriﬁcing OA. 2.4. Auxiliary data selection and ﬁnal classiﬁcation On the basis of the optimal interpretation scheme (i.e., highest OA and MA), the importance of 15 auxiliary covariates were quantiﬁed by a mean increase in the classiﬁcation error using RFC. We assumed an auxiliary covariate to be important and necessary if its absence could elevate the error rate by over 1% for both classes (moso bamboo/other forest plants) together or could elevate the error rate by over 1% for the moso bamboo class alone. With the optimal Landsat time series interpretation scheme and auxiliary covariates labeled as important, a ﬁnal classiﬁcation procedure was then conducted with GEE platform to map moso bamboo in the study area. 6 C. LIU ET AL. 3. Results and discussion Downloaded by [UNSW Library] at 06:22 26 October 2017 3.1. Impact of image time series interpretation scheme We found substantial diﬀerences in the initial classiﬁcation results (Figure 2). This disagreement revealed that diﬀerent Landsat time series interpretation strategies can have a large impact on moso bamboo estimation. Relative to the single-date image selection (S1), the use of full-season images (S2) led considerable improvement in both OA and MA. These patterns are consistent with research of Diao and Wang (2016), and support the idea that including images from diﬀerent months is more capable of recovering from misclassiﬁcations and characterizing phenological trajectory (Li et al. 2016). Analysis results also showed further accuracy increases by S3 groups. We believe these enhancements were mainly results of alleviating the inﬂuence of cloud as previous studies suggested. Based on the same ‘best available pixel paradigm’ used in this study, Hermosilla et al. (2016) reported the superiority of composited Landsat images for broad-scale forest monitoring. Considering the severe cloud impact in our study area, it is almost impossible to ﬁnd clear Landsat scenes for all seasons, thus the compositing strategy turned out to be a more desirable option. According to our experiments, the inclusion of GLCM textures generally led to increases in accuracy. However, the degree of accuracy improvement varied across diﬀerent schemes. For instance, in areas severely inﬂuenced by cloud, adding GLCM textures as model inputs may cause greater uncertainty due to the missing information of original imagery. This possibly explains the relatively small variations displayed by groups of S2, as they were more prone to cloud inﬂuence. 3.2. Impact of auxiliary data We also provided a quantitative evaluation of auxiliary data importance measured by the mean increase in classiﬁcation error (Figure 3). The exclusion of a certain covariate caused a 0–2% error rate increase for both classes together, and a 0–7% error rate Figure 2. Initial classiﬁcation performance for minimum accuracy (upper row) and overall accuracy (lower row) with diﬀerent interpretation schemes. Downloaded by [UNSW Library] at 06:22 26 October 2017 REMOTE SENSING LETTERS 7 Figure 3. Auxiliary data importance measured by the mean increase in classiﬁcation error. The black lines suggest thresholds of 1% mean increase in error for the moso bamboo class alone (upper row) and for both classes together (lower row). G_p: maximum NDVI; G_m: mean NDVI; G_v: minimum NDVI; G_s: standard deviation of NDVI; W_p: maximum NDMI; W_m: mean NDMI; W_v: minimum NDMI; W_s: standard deviation of NDMI; asp: aspect; ele: elevation; slp: slope; pre: precipitation; soil: soil type; ta: temperature; cld_f: cloud frequency. increase for the moso bamboo class alone. This importance inconsistence highlights the complex suite of biotic and abiotic stresses that can aﬀect moso bamboo distribution. According to the evaluation result, ﬁve covariates (maximum NDVI, mean NDMI, minimum NDMI, elevation and temperature) met the importance criteria and were therefore included for the ﬁnal classiﬁcation. Among all vegetative covariates, the mean and minimum NDMI layers were the most helpful, which can be possibly explained by the lower leaf water content of moso bamboo than other forest plants (Li et al. 2016). Interestingly, it was the maximum NDVI rather than other NDVI metrics that passed the importance test, revealing the most useful time window should be within the growing seasons. Along with these biotic covariates, some abiotic covariates also bear special mention. For example, the elevation layer was regarded as a necessary input in identifying moso bamboos, probably because they tend to occur in distinctly diﬀerent elevation landscape positions compared to other forest types (Li et al. 2016). Zhu et al. (2016) reported a crucial role of cloud frequency in enhancing land cover classiﬁcation. However, in our study, this improvement was not signiﬁcant. On the contrary, temperature was labelled as an important variable, indicating its major role of explaining the spatial variation of moso bamboo compared to other climatic factors. 3.3. Final moso bamboo mapping with the optimal strategy A deﬁnitive map of moso bamboo was generated by adopting the optimal strategy (i.e., full-season compositing scheme and important auxiliary covariates). Pixel-level statistical results showed that the ﬁnal result yielded a higher prediction accuracy 8 C. LIU ET AL. Downloaded by [UNSW Library] at 06:22 26 October 2017 performance than any initial classiﬁcation trials (93.21% in OA and 73.97% in MA), implying the usefulness of combining dense Landsat time series and crucial auxiliary data for moso bamboo identiﬁcation. Based on the resultant map, we found moso bamboos are generally found in most regions of Fujian. Spatially, the highest moso bamboo densities exist in mountainous zones, and the moso bamboo density decreases signiﬁcantly from the northwest to the southeast coastline areas (Figure 4). Figure 4. Final moso bamboo mapping result of the study area. The scatter plot at the upper right corner displays the comparison between reference and estimated results of moso bamboo area at county-level (ha is an abbreviation for hectare). REMOTE SENSING LETTERS 9 At provincial-level, we noted a slight omission trend of our moso bamboo area prediction when comparing it with the 3rd FMI data (0.98 million hectares vs. 1.08 million hectares). This is maybe because the ‘threshold-based’ masking procedure in our study not only ﬁltered out non-forest regions, but also removed some isolated moso bamboo forests. To examine the omission phenomenon on mapping performance, we further selected 44 counties with moso bamboo areas from 300 to 90,000 hectares and conducted a scatter plot analysis. It can be found the linear regression relationship between our estimates and 3rd FMI data had a near 1:1 relationship, supported by its slope (0.97 close to 1) and a coeﬃcient of determination (R2) of 0.89. Therefore, we believe the ﬁnal mapping result is generally reasonable and can be used to identify moso bamboo distribution. Downloaded by [UNSW Library] at 06:22 26 October 2017 4. Conclusions This study comprehensively investigated diﬀerent interpretation schemes for mapping large-scale moso bamboo using dense Landsat time series, and provided a quantitative importance evaluation of auxiliary data. Compared to the commonly used single-date or sparse multi-date image selection approaches, it was found the adoption of full-season image compositing strategy signiﬁcantly enhanced the classiﬁcation accuracy by incorporating phenological information and alleviating the inﬂuence of cloud. Additionally, including GLCM textures as model inputs was generally beneﬁcial, yet the degree of precision increase varied with speciﬁc interpretation schemes. We also highlight the contribution variability of auxiliary data, indicating that care should be given to the selection of appropriate auxiliary covariates for moso bamboo identiﬁcation. With the full-season image compositing scheme and selected auxiliary covariates, the ﬁnal classiﬁcation exhibited a reasonable and improved performance (93.21% in OA and 73.97% in MA). Our conclusions contribute to a better understanding of the roles of dense satellite image time series and auxiliary data in identifying moso bamboo, and potentially serve as a reference for other large-scale plant distribution mapping applications. Acknowledgments We thank the International Network for Bamboo and Rattan (INBAR) for oﬀering valuable support. This work was supported by National Natural Science Foundation of China , Natural Science Foundation of Jiangxi Province of China [20161BAB213078] and Open Research Foundation of Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, State Bureau of Surveying and Mapping of China [WE2016007]. ORCID Chong Liu http://orcid.org/0000-0003-4662-1622 References Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324. Dannenberg, M. P., C. R. Hakkenberg, and C. 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