Study of crown information extraction of <i>Picea schrenkiana var. tianschanica</i>based on high-resolution satellite remote sensing data

doi:10.6046/gtzyyg.2019.04.15

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(3521 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

For the reasons that images of Picea schrenkiana var. tianschanica in the western tianshan forest were round or suborbicular, crown information extraction was conducted with the space geometric features. According to the workflow of “investigating the features of Picea schrenkiana var. tianschanica in satellite images - extracting tree crown information-estimating tree crown width with remote sensing images”, a method for estimating tree crown width in Central Asia mountain forests based on remote sensing images was proposed and evaluated, with the purpose of challenging the problems that it is difficult to set the marks for marking watershed transform target ground objects and the active contour model evolution results are limited by the original positions of contour lines. Multi-scale blob detection, marking watershed transform and GVF Snake active contour model were orderly combined for tree crown information extraction. This technical process integrated and optimized the process of tree crown information extraction, and gained the tree crown contour distribution map of Picea schrenkiana var. tianschanica from images. A comparison with the measured tree crown width of each tree in the investigated sample ground shows that this method well estimates the tree crown width of Picea schrenkiana var. Tianschanica with high, medium or low canopy density, with the mean error being 10.8%, 4.5% and 6.4%, respectively. The research results provide a better solution for the key technical problem of tree crown interpretation for high-resolution remote sensing data in forest resource monitoring.

Keywords high resolution remote sensing Picea schrenkiana var. tianschanica’s crown multi-scale blob detection marking watershed transform GVF Snake model

TP79

Issue Date: 03 December 2019

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Yufeng LIU
	Ying PAN
	Hu LI

Cite this article:

Yufeng LIU,Ying PAN,Hu LI. Study of crown information extraction of Picea schrenkiana var. tianschanicabased on high-resolution satellite remote sensing data[J]. Remote Sensing for Land & Resources, 2019, 31(4): 112-119.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.04.15 OR https://www.gtzyyg.com/EN/Y2019/V31/I4/112

Fig.1 Schematic diagram of research area

Fig.2 Schematic of extreme points detection in scale space

Fig.3 Extreme points’ difference between discrete space and continuous space

Fig.4 Crown outline distribution of sparse Picea schrenkiana var. tianschanica

Tab.1 Table of tree numbers of auto identification and field work from different stands

Fig.5 Accuracy analysis in crown width estimation of dense Picea schrenkiana var. Tianschanica

[1]	Song C, Band L E . MVP:A model to simulate the spatial patterns of photosynthetically ac[J]. Revue Canadienne De Recherche Forestière, 2004,34(34):1192-1203.
[2]	Pouliot D A, King D J, Bell F W , et al. Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration[J]. Remote Sensing of Environment, 2002,82(2-3):322-334.
[3]	Hay G J, Blaschke T, Marceau D J . A comparison of three image-object methods for the multiscale analysis of landscape structure[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2003,57(5-6):327-345.
[4]	Johansen K, Coops N C, Gergel S E , et al. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification[J]. Remote Sensing of Environment, 2007,110(1):29-44.
[5]	Leckie D G, Gougeon F A, Tinis S , et al. Automated tree recognition in old growth conifer stands with high resolution digital imagery[J]. Remote Sensing of Environment, 2005,94(3):311-326.
[6]	Erikson M, Olofsson K . Comparison of Three Individual Tree Crown Detection Methods[M]. New York:Springer-Verlag, 2005.
[7]	Kalapala M, Rao V S, Srinivas K. Robust tree crown delineation using novel marker controlled watershed segmentation algorithm [C]//International Journal of Engineering Research and Technology.ESRSA Publications, 2012.
[8]	熊轶群, 吴健平 . 基于高分辨率遥感图像的树冠面积提取方法[J]. 地理与地理信息科学, 2007,23(6):30-33.
[8]	Xiong Y Q, Wu J P . Tree-crown area detection algorithm for high spatial resolution remote-sensing image[J]. Geography and Geo-Information Science, 2007,23(6):30-33.
[9]	李德仁, 童庆禧, 李荣兴 , 等. 高分辨率对地观测的若干前沿科学问题[J]. 中国科学(地球科学), 2012,42(6):805-813.
[9]	Li D R, Tong Q X, Li R X , et al. Current issues in high-resolution earth observation technology[J]. Science China(Earth Science), 2012,42(6):805-813.
[10]	Zhao J, Huang L, Yang H, et al. Fusion and assessment of high-resolution WorldView-3 satellite imagery using NNDiffuse and Brovey algotirhms [C]//Geoscience and Remote Sensing Symposium.IEEE, 2016: 2606-2609.
[11]	Moon W K, Shen Y W, Min S B , et al. Computer-aided tumor detection based on multi-scale blob detection algorithm in automated breast ultrasound images[J]. IEEE Transactions on Medical Imaging, 2013,32(7):1191-1200.
[12]	Quine B M, Tarasyuk V, Mebrahtu H , et al. Determining star-image location:A new sub-pixel interpolation technique to process image centroids[J]. Computer Physics Communications, 2007,177(9):700-706.
[13]	陈杰, 邓敏, 肖鹏峰 , 等. 结合支持向量机与粒度计算的高分辨率遥感影像面向对象分类[J]. 测绘学报, 2011,40(2):135-141.
[13]	Chen J, Deng M, Xiao P F , et al. Object-oriented classification of high resolution imagery combining support vector machine with granular computing[J]. Acta Geodaetica et Cartographica Sinica, 2011,40(2):135-141.
[14]	Vincent L, Soille P . Watersheds in digital spaces:An efficient algorithm based on immersion simulations[J]. Transactions on Pattern Analysis and Machine Intelligence, 1991,13(6):583-598.
[15]	Song X, Tang G, Li F , et al. Extraction of loess shoulder-line based on the parallel GVF Snake model in the loess hilly area of China[J]. Computers and Geosciences, 2013,52(1):11-20.

[1]	HU Suliyang, LI Hui, GU Yansheng, HUANG Xianyu, ZHANG Zhiqi, WANG Yingchun. An analysis of land use changes and driving forces of Dajiuhu wetland in Shennongjia based on high resolution remote sensing images: Constraints from the multi-source and long-term remote sensing information[J]. Remote Sensing for Land & Resources, 2021, 33(1): 221-230.
[2]	WU Tong, PENG Ling, HU Yuan. Informal garbage dumps detection in high resolution remote sensing images based on SU-RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(3): 90-97.
[3]	Chunsen ZHANG, Rongrong WU, Guojun LI, Weihong CUI, Chenyi FENG. High resolution remote sensing image object change detection based on box-plot method[J]. Remote Sensing for Land & Resources, 2020, 32(2): 19-25.
[4]	Lihua FU, Ce ZHANG. Study of ore control information in Rongle area of Tibet based on high resolution remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(1): 98-105.
[5]	Hui HUANG, Xiongwei ZHENG, Genyun SUN, Yanling HAO, Aizhu ZHANG, Jun RONG, Hongzhang MA. Seismic image classification based on gravitational self-organizing map[J]. Remote Sensing for Land & Resources, 2019, 31(3): 95-103.
[6]	Qifang XIE, Guoqing YAO, Meng ZHANG. Research on high resolution image object detection technology based on Faster R-CNN[J]. Remote Sensing for Land & Resources, 2019, 31(2): 38-43.
[7]	Ling CHEN, Jia JIA, Haiqing WANG. An overview of applying high resolution remote sensing to natural resources survey[J]. Remote Sensing for Land & Resources, 2019, 31(1): 1-7.
[8]	SU Tengfei, ZHANG Shengwei, LI Hongyu. Heuristics optimized segmentation of agricultural area for high resolution remote sensing image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 106-113.
[9]	DAI Jingjing, WANG Denghong, WU Yanan. Investigation of rare metal mine using high resolution remote sensing data: A case study of No. 414 rare metal mine in Yichun, Jiangxi Province[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 104-110.
[10]	CAI Hongyue, YAO Guoqing. Auto-extraction of road intersection from high resolution remote sensing image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 63-71.
[11]	GUO Qiqian, LI Shengle, LIU Zhumei. Platform of online interpretation and attitude measurement for faults using high resolution remote sensing image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 190-196.
[12]	YANG Xianhua, HUANG Jie, TIAN Li, LIU Zhi, HAN Lei. A discussion on comprehensive governance of mine environment based on high resolution remote sensing data: A case of Maoniuping REE deposit, Mianning County[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 115-121.
[13]	LI Xianghui, CHEN Yixiang, WANG Haibin, ZHANG Enbing, QIN Kun. Urban area detection based on Gabor filtering and density of local feature points[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 59-64.
[14]	GAO Yongzhi, CHU Yu, LIANG Wei. Remote sensing monitoring and analysis of tailings ponds in the ore concentration area of Heilongjiang Province[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 160-163.
[15]	YANG Yun, XU Li, YAN Peili. Urban land use/cover classification of remote sensing using random forests under the framework of conditional random fields[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 51-55.

Viewed

Full text

Abstract

Cited

Shared

Discussed