Unmanned aerial vehicle （UAV） based methodology for spatial distribution pattern analysis of desert vegetation

doi:10.7522/j.issn.1000-694X.2020.00027

Journal of Desert Research ›› 2020, Vol. 40 ›› Issue (6): 169-179.DOI: 10.7522/j.issn.1000-694X.2020.00027

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Unmanned aerial vehicle （UAV） based methodology for spatial distribution pattern analysis of desert vegetation

Mengyu Hao(), Longjun Qin, Peng Mao, Jiechunyi Luo, Wenli Zhao, Guoyu Qiu()

School of Environment and Energy，Shenzhen Graduate School，Peking University，Shenzhen 518055，Guangdong，China

Received:2020-01-13 Revised:2020-04-15 Online:2020-12-09 Published:2020-12-09
Contact: Guoyu Qiu

Abstract

Abstract:

Ground-based quadrat sampling is one of the commonly used vegetation survey methods， which is a time consuming and labor intensive process. The scale and amount of the quadrat are usually samll， and lots of vegetation information may not be fully revealed， too. To overcome these challenges， an UAV based methodology was proposed and was then applied to the spatial distribution pattern study in desert region： Taking the shrubs in the desert region in Bayannur as example， high spatial resolution RGB images were acquired by the UAV. After image preprocessing and object-oriented classification， the vegetation species were identified and then classified. The species distribution vector graphics were used for the multi-scale random quadrat layout and the vegetation location extracting. Then， the multi-scale quadrat analysis and the point pattern analysis based on complete statistics of vegetation information were carried out. The results showed that the classification processis of high accuracy. The quadrats data of vegetation information with enlarged scale and increased quantity could be processed automatically and then the multi-aspects ecological analysis could be conducted. Therefore， this method shows a great potential to be applied in a larger scale spatial distribution pattern analysis of plant population in desert reginon.

Key words: unmanned aerial vehicle, desert vegetation, spatial distribution, quadrat analysis, pattern

CLC Number:

Q948.15

Mengyu Hao, Longjun Qin, Peng Mao, Jiechunyi Luo, Wenli Zhao, Guoyu Qiu. Unmanned aerial vehicle （UAV） based methodology for spatial distribution pattern analysis of desert vegetation[J]. Journal of Desert Research, 2020, 40(6): 169-179.

Figures/Tables 12

Fig.1 Location and the 1×104 m2 UAV orthographic mosaic image of the study area

Table 1 UAV flight settings and image parameters

飞行设定内容	参数	获取图像内容	参数
飞行速度	2~3 m·s^-1	原始图像数量	1 650张
拍摄间隔	2 s	图像分辨率	72 dpi
飞行高度	约10 m	图像格式	JPEG
航线数量	15	拍摄日期	2018年7月11日
航点数量	30	拍摄时间	12:40—14:00

Fig.2 Methodology flow chart

Fig.3 Comparison of the segmentations with different scale parameter

Fig.4 Comparison of the segmentations with different color and compactness parameter

Table 2 Six vegetation indices in the visible light range

植被指数	名称	公式
CIVE	植被颜色指数	$C I V E = 0.441 r - 0.881 g + ???????????? 0.385 b + 18.787$
ExG	超绿指数	$E x G = 2 g - r - b$
ExGR	超绿超红差分指数	$E x G R = E x G - (1.4 r - g)$
NGRDI	归一化绿红差分指数	$N G R D I = G - R G + R$
VEG	植被因子	$V E G = g r 0.667 b 0.333$
WI	Woebbecke指数	$W I = g - b r - g$

Table 2 Six vegetation indices in the visible light range

植被指数	名称	公式
CIVE	植被颜色指数	$C I V E = 0.441 r - 0.881 g + ???????????? 0.385 b + 18.787$
ExG	超绿指数	$E x G = 2 g - r - b$
ExGR	超绿超红差分指数	$E x G R = E x G - (1.4 r - g)$
NGRDI	归一化绿红差分指数	$N G R D I = G - R G + R$
VEG	植被因子	$V E G = g r 0.667 b 0.333$
WI	Woebbecke指数	$W I = g - b r - g$

Fig.5 Comparison of the separation degrees under the condition of different spatial feature combinations

Fig.6 Spatial distribution pattern of A. mongolicus and Z. xanthoxylon populations in a typical desert plant community, where pink patch represents plant individual of A. mongolicus, green patch represents plant individual of Z. xanthoxylon, and the white area represents background soil

Fig.7 Accuracy verification of the plant number and the canopy area of plant

Fig.8 Layout of the random quadrats of A. mongolicus and Z. xanthoxylon populations

Fig.9 Canopy area and its width structure of Z. xanthoxylon population

Fig.10 Point pattern distributionof A. mongolicus (green filled circle) and Z. xanthoxylon (blue empty circle) populations

References 31

1	南笑宁，刘旻霞，朱恭，等.黄土高原中部红砂与荒漠锦鸡儿种群空间分布格局及关联性［J］.生态学杂志，2019，38（2）：48-54.
2	方精云，王襄平，沈泽昊，等.植物群落清查的主要内容、方法和技术规范［J］.生物多样性，2009，17（6）：533-548.
3	Velazquez E，Martinez I，Getzin S，et al.An evaluation of the state of spatial point pattern analysis in ecology［J］.Ecography，2016，39（11）：1042-1055.
4	Wiegand T，Moloney K A.Handbook of Spatial Point-Pattern Analysis in Ecology［M］.Boca Raton，USA：CRC Press，2014.
5	Toft C，Fraizer T.Spatial dispersion and density dependence in a perennial desert shrub （Chrysothamnus nauseosus：Asteraceae）［J］.Ecological Monographs，2003，73（4）：605-624.
6	Hesselbarth M H K，Wiegand K，Dreber N，et al.Density-dependent spatial patterning of woody plants differs between a semi-arid and a mesic savanna in South Africa［J］.Journal of Arid Environments，2018，157：103-112.
7	Perry J N，Liebhold A M，Rosenberg M S，et al.Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data［J］.Ecography，2002，25（5）：578-600.
8	Holman H F，Riche B A，Michalski A，et al.High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing［J］.Remote Sensing，2016，8（12）：1031.
9	Van Iersel W，Straatsma M，Addink E，et al.Monitoring height and greenness of non-woody floodplain vegetation with UAV time series［J］.ISPRS Journal of Photogrammetry and Remote Sensing，2018，141：112-123.
10	Christa L Z，Matthew A B，Percival H F，et al.Use of unmanned aircraft systems to delineate fine-scale wetland vegetation communities［J］.Wetlands，2015，35（2）：303-309.
11	Chen J，Yi S，Yu Q，et al.Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai-Tibetan Plateau［J］.International Journal of Remote Sensing，2016，37（8）：1922-1936.
12	万炜，肖生春，陈小红，等.无人机遥感在野外植被盖度调查中的应用：以阿拉善荒漠区灌木为例［J］.干旱区资源与环境，2018，32（9）：150-156.
13	Sandino J，Gonzalez F，Mengersen K，et al.UAVs and machine learning revolutionising invasive grass and vegetation surveys in remote arid lands［J］.Sensors2018，18（2）：200-216.
14	Ehlers B K，Holmstrup M，Schmidt I K，et al.Joint impact of competition，summer precipitation，and maternal effects on survival and reproduction in the perennial Hieracium umbellatum［J］.Evolutionary Ecology，2018，32（5）：529-545.
15	Li R，Yan C，Zhao Y，et al.Discriminating growth stages of an endangered Mediterranean relict plant （Ammopiptanthus mongolicus） in the arid Northwest China using hyperspectral measurements［J］.Science of the Total Environment，2019，657：270-278.
16	何恒斌，张惠娟，贾桂霞.磴口县沙冬青种群结构和空间分布格局的研究［J］.林业科学，2006，42（10）：13-18.
17	周向睿，周志宇，吴彩霞.霸王繁殖特性的研究［J］.草业科学，2006，23（6）：38-41.
18	Guan S，Fukami K，Matsunaka H，et al.Assessing correlation of high-resolution NDVI with Fertilizer application level and Yield of rice and wheat crops using small UAVs［J］.Remote Sensing，2019，11（2）：112.
19	井然，邓磊，赵文吉，等.基于可见光植被指数的面向对象湿地水生植被提取方法［J］.应用生态学报，2016，27（5）：1427-1436.
20	苗云鹏，张瑞红，赵保国，等.高分辨率遥感影像信息提取方法研究［J］.测绘与空间地理信息，2010，33（4）：150-152，156.
21	Drăguţ L，Csillik O，Eisank C，et al.Automated parameterisation for multi-scale image segmentation on multiple layers［J］.ISPRS Journal of Photogrammetry and Remote Sensing，2014，88：119-127.
22	周龙君，陈晓芬，杨利娟.面向对象标准最邻近分类法在地理国情监测中的应用［J］.测绘与空间地理信息，2016，39（5）：155-157.
23	Yang W，Wang S，Zhao X，et al.Greenness identification based on HSV decision tree［J］.Information Processing in Agriculture，2015，2（3）：149-160.
24	Woebbecke M D， Meyer E G，Von Bargen K，et al.Color indices for weed identification under various soil，residue，and lighting conditions［J］.Transactions of the ASAE，1995，38（1）：259-269.
25	Hunt E R，Cavigelli M，Daughtry C S T，et al.Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status［J］.Precision Agriculture，2005，6（4）：359-378.
26	Petrokofsky G，Kanamaru H，Achard F，et al.Comparison of methods for measuring and assessing carbon stocks and carbon stock changes in terrestrial carbon pools.How do the accuracy and precision of current methods compare？ A systematic review protocol［J］.Environmental Evidence，2012，1（1）：6.
27	曹宝，秦其明，马海建，等.面向对象方法在SPOT5遥感图像分类中的应用：以北京市海淀区为例［J］.地理与地理信息科学，2006（2）：46-59，54.
28	Greig-Smith P.Quantitative Plant Ecology［M］.California，USA：Berkeley University of California Press，1983.
29	张娜.生态学中的尺度问题：内涵与分析方法［J］.生态学报，2006（7）：2340-2355.
30	姜俊，张春雨，赵秀海.吉林蛟河42 hm²针阔混交林样地植物种-面积关系［J］.植物生态学报，2012，36（1）：30-38.
31	李月飞，陈林，李学斌，等.荒漠草原不同土壤条件下猪毛蒿幼苗种群的点格局分析［J］.生态学报，2019，39（17）：6273-6281.

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Unmanned aerial vehicle （UAV） based methodology for spatial distribution pattern analysis of desert vegetation

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