|
|
|
|
|
|
| Classifying Forest Dominant Trees Species Based on High Dimensional Time-Series NDVI Data and Differential Transform Methods |
| XU Kai-jian1, 2, TIAN Qing-jiu1, 2*, XU Nian-xu1, 2, YUE Ji-bo1, 2, TANG Shao-fei1, 2 |
1. International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China |
|
|
|
|
Abstract Ensuring the accuracy of forest trees species recognition based on remote sensing spectral detail information has strong practical significance and value in forestry resources monitoring and management, which is also an important scientific issue to be settled. The time-series remotely sensed data with high resolution can distinguish small canopy spectrum variation caused by different phenological growth characteristics of different forest tree species effectively, which is expected to solve the common problem of the isomorphism in multispectral recognition of tree species. To clarify this situation, we study the Wangyedian forest farm in Chifeng of Inner Mongolia, northeast China, by using a total of 36 scenes covering the whole year medium-high resolution satellite observations (at 16 m spatial resolution) which were supported with GF-1 WFV (wide field view) to extract various time series of NDVI reflectance data. The data contain all the seasonal phases and phenological growth stages of different tree species and are propitious for the fine recognition of forest types. Five dominant forest types of Pinus tabulaeformis, Larix gmelinii, Populus davidiana, Betula platyphylla, and Quercus mongolica forest were classified and recognized using Support Vector Machine (SVM) classifier at different time scales (single season, every quarter, month-to-month and every ten-days). We also explore the effects of different time scales of NDVI reflectance data and differential transformation methods on the recognition of regional forest dominant tree species, based on the original sequence spectrum and the first, second and third order differential transformation, respectively. The results showed that Autumn is the best single season to identify the dominant tree species in the study area (p<0.05), and the largely improved recognition accuracy of forest tree species can be obtained from different time series data than single season data across all different seasons (p<0.05). Compared with the single data of spring, summer and autumn, the overall accuracy (OA) based on the every quarter data improved, which increased by 7.67%, 6.64% and 3.6% respectively, indicating the importance of phenological information contained in time series data images for discriminating different forest types. Besides, the results of spectral recognition based on month-to-month and every quarter data were significantly lower than those based on every ten-days and every quarter (p<0.05), and the spectrum recognition results based on the whole seasonal phase were the lowest in these time series data (p<0.05), which showed that the denser time series spectral information is more beneficial to the improvement of the accuracy of regional tree species identification (p<0.05). In addition, combined appropriately with spectrum differential transformation increased classification accuracy using time series multispectral imagery (p<0.05), for the overall accuracy of tree species created with the combined data was higher than that from results of time series NDVI spectral alone in the study area. After combined with the optimal spectrum differential transformation method, the best overall accuracies occurred in every ten-days and month-to-month data were 82.1% and 78.74%, and the corresponding rates of increase reached 3.38% and 2.95%, respectively. The results indicated that adding spectral derivative analysis was more effective in improving the tree species recognition accuracy from every ten-days to month-to-month time series NDVI data (p<0.05), which provided an effective reference and foundation for related researches focusing on fine recognition of forest types with multispectral remote sensing.
|
|
Received: 2018-10-21
Accepted: 2019-02-19
|
|
|
|
Corresponding Authors:
TIAN Qing-jiu
E-mail: tianqj@nju.edu.cn
|
|
[1] Ballanti L, Blesius L, Hines E, et al. Remote Sensing，2016, 8(6): 445.
[2] Fassnacht F E, Latifi H, Stereńczak K, et al. Remote Sensing of Environment, 2016, 186(214): 64.
[3] ZHAO Xian-wen(赵宪文). Scientia Silvae Sinicae(林业科学), 2009, 45(8): 135.
[4] Roth K L, Roberts D A, Dennison P E, et al. Remote Sensing of Environment, 2015, 171: 45.
[5] Hill R A, Wilson A K, George M, et al. Applied Vegetation Science, 2010, 13: 86.
[6] JIA Li-ge, ZHANG Bo, WEI Huai-dong(加力戈, 张 勃, 魏怀东). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(9): 2881.
[7] JIA Ming-ming, REN Chun-ying, LIU Dian-wei, et al(贾明明, 任春颖, 刘殿伟，等). Acta Ecologica Sinica(生态学报), 2014, 34(24): 7167.
[8] Zhu X L, Liu D S. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 96: 1.
[9] CHANG Bu-hui，WANG Jun-tao，LUO Yu-li, et al(常布辉，王军涛，罗玉丽，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(23): 188.
[10] ZHANG Jing, ZHAN Yu-lin, LI Ru-ren(张 晶, 占玉林, 李如仁). Remote Sensing Information(遥感信息), 2017, 32(1): 50.
[11] FAN De-qin, ZHAO Xue-sheng, ZHU Wen-quan, et al(范德芹, 赵学胜, 朱文泉, 等). Progress in Geography(地理科学进展), 2016, 35(3): 304.
[12] Yu X F, Wang Q K, Yan H M, et al. Advances in Meteorology, 2014, 592106.
[13] ZHANG Dong, Tashpolat·Tiyip, ZHANG Fei, et al(张 东, 塔西甫拉提·特依拜, 张 飞, 等). Acta Optica Sinica(光学学报), 2016, 36(3): 0330002.
[14] XU Kai-jian, TIAN Qing-jiu, YANG Yan-jun, et al(徐凯健, 田庆久, 杨闫君, 等). Journal of Geo-Information Science(地球信息科学学报), 2018, 20(2): 246. |
| [1] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [2] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [3] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
| [4] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [5] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [6] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
| [7] |
LIU Fei1, TAN Jia-jin1*, XIE Gu-ai2, SU Jun3, YE Jian-ren1. Early Diagnosis of Pine Wilt Disease Based on Hyperspectral Data and Needle Resistivity[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3280-3285. |
| [8] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
| [9] |
LÜ Shi-lei1, 2, 3, WANG Hong-wei1, LI Zhen1, 2, 3*, ZHOU Xu1, ZHAO Jing1. Hyperspectral Identification Model of Cantonese Tangerine Peel Based on BWO-SVM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2894-2901. |
| [10] |
WANG Jun-jie1, YUAN Xi-ping2, 3, GAN Shu1, 2*, HU Lin1, ZHAO Hai-long1. Hyperspectral Identification Method of Typical Sedimentary Rocks in Lufeng Dinosaur Valley[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2855-2861. |
| [11] |
ZHANG Hai-liang1, XIE Chao-yong1, TIAN Peng1, ZHAN Bai-shao1, CHEN Zai-liang1, LUO Wei1*, LIU Xue-mei2*. Measurement of Soil Organic Matter and Total Nitrogen Based on Visible/Near Infrared Spectroscopy and Data-Driven Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2226-2231. |
| [12] |
LI Hao-dong1, 2, LI Ju-zi1*, CHEN Yan-lin1, HUANG Yu-jing1, Andy Hsitien Shen1*. Establishing Support Vector Machine SVM Recognition Model to Identify Jadeite Origin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2252-2257. |
| [13] |
LI Bin, HAN Zhao-yang, WANG Qiu, SUN Zhao-xiang, LIU Yan-de*. Research on Bruise Level Detection of Loquat Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1792-1799. |
| [14] |
PAN Zhao-jie1, SUN Gen-yun1, 2*, ZHANG Ai-zhu1, FU Hang1, WANG Xin-wei3, REN Guang-wei3. Tobacco Disease Detection Model Based on Band Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1023-1029. |
| [15] |
WANG Yu-ye1, 2, LI Hai-bin1, 2, JIANG Bo-zhou1, 2, GE Mei-lan1, 2, CHEN Tu-nan3, FENG Hua3, WU Bin4ZHU Jun-feng4, XU De-gang1, 2, YAO Jian-quan1, 2. Terahertz Spectroscopic Early Diagnosis of Cerebral Ischemia in Rats[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 788-794. |
|
|
|
|
