|
|
|
|
|
|
| Extraction Method of Oasis Shelterbelt Systems Based on Remote-Sensing Images ——A Case Study of Dengkou County |
| GAO Feng1, 2, 3, JIANG Qun-ou1, 2, 3*, XIN Zhi-ming4, XIAO Hui-jie1, 2, LÜ Ke-xin1, QIAO Zhi1 |
1. School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
2. Key Laboratory of Soil and Water Conservation & Desertification Combating of Ministry of Education, Beijing Forestry University, Beijing 100083, China
3. Jinyun National Positioning Observation and Research Station of Forest Ecosystem in Three Gorges Reservoir Area, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
4. Experimental Center of Desert Forestry, Chinese Academy of Forestry, Dengkou 015200, China
|
|
|
|
|
Abstract Shelterbelt systems are the main type of vegetation in the desert oasis regions, which provide a strong guarantee for wind-break and sand fixation, salt-water regulation and water-heat balance. It is important to investigate the spatial distribution information of shelterbelts. However, precisely mapping shelterbelts systems on a large scale are difficult due to narrow strips, small patches and wide & scattered distribution. This study aims to accurately map shelterbelts using object-oriented extraction based on GF-2 satellite imagery in Dengkou oasis. Firstly, the optimal scale parameter of SF segmentation was determined by local variance (LV) and rate of change (ROC) curve, and then the features space and classifier’s parameters were optimized by Out of bag error (OOB error) and Gini index through Random Forest (RF) algorithm prior to classification. Finally, Random Forest, CART decision tree, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) were compared and validated for shelterbelt systems extraction. The results showed that: (1) the ROC-LV curve method can obtain the possible value of optimal scale parameter more objective and more efficiently than iterating all scale parameter values. (2) OOB error and Gini index through RF algorithm can effectively eliminate the redundant features among spectral, shape and texture. The processing time was sharply reduced and ensuring the accuracy of the classification. (3) The classification results were verified based on the measured data sets, and the results showed that the feature optimization based on the RF algorithm combined with the SVM classifier was the best method for extracting the desert oasis shelterbelt systems, with the highest producer accuracy of 97.14%. Meanwhile, the extracted area of shelterbelt systems was 208.99 km2, which was close to reality (210 km2). The SVM classifier performs better than the other three classifiers while zooming in a small areas; (4) Due to the high resolution of GF-2 images and the near-infrared band, sub-meter information can be obtained through appropriate band fusion. Based on the object-oriented method, a single shelterbelt can be used as the basic unit to explore the attributes and characteristics of the shelterbelts net. For example, the broken shelterbelts information could be extracted. All these conclusions will provideimportant technical support for the shelterbeltextraction in the desert oasis areas.
|
|
Received: 2022-04-23
Accepted: 2022-07-12
|
|
|
|
Corresponding Authors:
JIANG Qun-ou
E-mail: jiangqo.dls@163.com
|
|
[1] LEI Si-jun, SUN Hua, LIU Hua, et al(雷思君, 孙 华, 刘 华, 等). Journal of Central South University of Forestry & Technology(中南林业科技大学学报), 2020, 40(4): 57.
[2] YANG Yi-tian, ZHENG Du, ZHANG Xue-qin, et al(杨依天, 郑 度, 张雪芹, 等). Acta Geographica Sinica(地理学报), 2013, 68(6): 813.
[3] HE Bao-zhong, DING Jian-li, ZHANG Zhe, et al(何宝忠, 丁建丽, 张 喆, 等). Acta Geographica Sinica(地理学报), 2016, 71(11): 1948.
[4] WU Jin-zhou, ZHENG Xiao, GAO Tian, et al(武金洲, 郑 晓, 高 添, 等). Chinese Journal of Ecology(生态学杂志), 2020, 39(11): 3567.
[5] Wiseman G, Kort J, Walker D. Agriculture, Ecosystems and Environment, 2009, 131(1): 111.
[6] XING Ze-feng, LI Ying, DENG Rong-xin, et al(幸泽峰, 李 颖, 邓荣鑫, 等). Scientia Silvae Sinicae(林业科学), 2016, 52(4): 11.
[7] GAO Meng-jie, JIANG Qun-ou, ZHAO Yi-yang, et al(高梦婕, 姜群鸥, 赵一阳, 等). Journal of China Agricultural University(中国农业大学学报), 2018, 23(8): 125.
[8] SUN Pan, DONG Yu-sen, CHEN Wei-tao, et al(孙 攀, 董玉森, 陈伟涛, 等). Remote Sensing for Natural Resources(国土资源遥感), 2016, 28(4): 108.
[9] TANG Huai-zhi, TANG Min, GUAN Ming-wen, et al(汤怀志, 汤 敏, 关明文, 等). Journal of China Agricultural University(中国农业大学学报), 2021, 26(4): 157.
[10] WU Ya-juan, LIU Ting-xi, TONG Xin, et al(邬亚娟, 刘廷玺, 童 新, 等). Arid Zone Research(干旱区研究), 2020, 37(4): 1026.
[11] Wulder Michael A, White Joanne C, Hay Geoffrey J, et al. The Forestry Chronicle, 2008, 84(02): 221.
[12] Drǎguţ L, Csillik O, Eisank C, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 88: 119.
[13] Habermann Mateus, Fremont Vincent, Shiguemori Elcio Hideiti. International Journal of Remote Sensing, 2019, 40(10): 3900.
[14] Breiman L. Machine Learning, 2001, 45(1): 5.
[15] ZHAN Guo-qi, YANG Guo-dong, WANG Feng-yan, et al(詹国旗, 杨国东, 王凤艳, 等). Journal of Geo-Information Science(地球信息科学学报), 2018, 20(10): 1520.
[16] ZENG Wen, LIN Hui, LI Xin-yu, et al(曾 文, 林 辉, 李新宇, 等). Journal of Central South University of Forestry & Technology(中南林业科技大学学报), 2020, 40(7): 32.
[17] Pal M, Mather P M. International Journal of Remote Sensing, 2005, 26(5): 1007.
[18] HUANG Shuang-yan, YANG Liao, CHEN Xi, et al(黄双燕, 杨 辽, 陈 曦, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(10): 3169.
[19] PENG Jia-yi, WANG Xin-jun, ZHU Lei, et al(彭佳忆, 王新军, 朱 磊, 等). Arid Zone Research(干旱区研究), 2019, 36(3): 771.
|
| [1] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [2] |
YAN Xing-guang, LI Jing*, YAN Xiao-xiao, MA Tian-yue, SU Yi-ting, SHAO Jia-hao, ZHANG Rui. A Rapid Method for Stripe Chromatic Aberration Correction in
Landsat Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3483-3491. |
| [3] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
| [4] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [5] |
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. |
| [6] |
LI Quan-lun1, CHEN Zheng-guang1*, JIAO Feng2. Prediction of Oil Content in Oil Shale by Near-Infrared Spectroscopy Based on Stacking Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1030-1036. |
| [7] |
LIU Xin-yu1, SHAO Wen-wu2*, ZHOU Shi-rui3. Spectral Pattern Recognition of Cardiac Tissue in Electric Shock Death and Post-Mortem Electric Shock[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1126-1133. |
| [8] |
YANG Cheng-en1, SU Ling2, FENG Wei-zhi1, ZHOU Jian-yu1, WU Hai-wei1*, YUAN Yue-ming1, WANG Qi2*. Identification of Pleurotus Ostreatus From Different Producing Areas Based on Mid-Infrared Spectroscopy and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 577-582. |
| [9] |
YAN Wen-hao1, YANG Xiao-ying1, GENG Xin1, WANG Le-shan1, LÜ Liang1, TIAN Ye1*, LI Ying1, LIN Hong2. Rapid Identification of Fish Products Using Handheld Laser Induced Breakdown Spectroscopy Combined With Random Forest[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3714-3718. |
| [10] |
GONG Sheng1, ZHU Ya-ning2, ZENG Chen-juan3, MA Xiu-ying3, PENG Cheng1, GUO Li1*. Near-Infrared Spectroscopy Combined With Random Forest Algorithm: A Fast and Effective Strategy for Origin Traceability of Fuzi[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3823-3829. |
| [11] |
GAO Rong-hua1, 2, FENG Lu1, 2*, ZHANG Yue3, YUAN Ji-dong3, WU Hua-rui1, 2, GU Jing-qiu1, 2. Early Detection of Tomato Gray Mold Disease With Multi-Dimensional Random Forest Based on Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3226-3234. |
| [12] |
LI Zi-zhao, BI Shou-dong, CUI Yu-huan, HAO Shuang*. Comparison of Forest Stock Volume Inversion Methods Coupled With Multiple Features——A Case Study of Forest in Yarlung Zangbo River Basin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3263-3268. |
| [13] |
YANG Cheng-en1, WU Hai-wei1*, YANG Yu2, SU Ling2, YUAN Yue-ming1, LIU Hao1, ZHANG Ai-wu3, SONG Zi-yang3. A Model for the Identification of Counterfeited and Adulterated Sika Deer Antler Cap Powder Based on Mid-Infrared Spectroscopy and Support
Vector Machines[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2359-2365. |
| [14] |
WANG Cai-ling1, WANG Bo2, JI Tong3, XU Jun4, JU Feng5, WANG Hong-wei6*. Simulated Estimation of Nitrite Content in Water Based on
Transmission Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2181-2186. |
| [15] |
JIANG Ping1, LU Hao-xiang2, LIU Zhen-bing2*. Drugs Identification Using Near-Infrared Spectroscopy Based on Random Forest and CatBoost[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2148-2155. |
|
|
|
|
