国土资源遥感, 2021, 33(1): 54-62 doi: 10.6046/gtzyyg.2020069

技术方法

城市绿地资源多尺度监测与评价方法探讨

熊育久,1,2, 赵少华3, 鄢春华4, 邱国玉4, 孙华5,6,7, 王艳林8, 秦龙君,4

1.中山大学土木工程学院,广州 510275

2.广东省华南地区水安全调控工程技术研究中心,广州 510275

3.生态环境保护部卫星环境应用中心/国家环境保护卫星遥感重点实验室,北京 100094

4.北京大学深圳研究生院环境与能源学院,深圳 518055

5.中南林业科技大学林业遥感信息工程研究中心,长沙 410004

6.林业遥感大数据与生态安全湖南省重点实验室,长沙 410004

7.南方森林资源经营与监测国家林业与草原局重点实验室,长沙 410004

8.广州市规划和自然资源局,广州 510000

A comparative study of methods for monitoring and assessing urban green space resources at multiple scales

XIONG Yujiu,1,2, ZHAO Shaohua3, YAN Chunhua4, QIU Gouyu4, SUN Hua5,6,7, WANG Yanlin8, QIN Longjun,4

1. School of Civil Engineering, Sun Yat-Sen University, Guangzhou 510275, China

2. Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Guangzhou 510275, China

3. Satellite Environment Center, Ministry of Ecology and Environment/State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing 100094, China

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

5. Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China

6. Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China

7. Key Laboratory of State Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China

8. Guangzhou Municipal Planning and Natural Resources Bureau, Guangzhou 510000, China

通讯作者: 秦龙君(1988-),男,博士研究生,主要从事生态环境方面的研究。Email: qinlongjun@sz,pku.edu.cn。

责任编辑: 张仙

收稿日期: 2020-03-20   修回日期: 2020-08-22   网络出版日期: 2021-03-15

基金资助: 深圳市技术攻关项目“飞行智能环境监测机器人研究”.  JCYJ20180504165440088
国家自然科学基金项目“干旱区绿洲与荒漠植被蒸散发及其组分定量遥感反演研究”共同资助.  41671416

Received: 2020-03-20   Revised: 2020-08-22   Online: 2021-03-15

作者简介 About authors

熊育久(1982-),男,博士,副教授,主要从事资源与环境遥感方面的研究。Email: xiongyuj@mail.sysu.edu.cn。

摘要

绿地是城市生态资源的重要组成部分,定量评估其时空分布、构建多尺度监测方法体系,是自然资源管理、生态文明城市建设等领域的迫切需求。以城市植被和城市水体为例,从资源的数量、质量与生态价值3个层次,梳理对比主流的监测评价方法,探讨这些方法的优势与存在的问题,为我国新时期城市自然资源评估与监测提供方法参考。结果表明: ①尽管基于样方的传统抽样方法可获得城市绿地的数量信息,但城市绿地高度斑块化特征限制了样方结果尺度推绎; ②卫星遥感是监测城市绿地的有效手段,可准确获取绿地空间分布、面积、种类、质量变化等信息,但生物量(或蓄积量)、体积等信息需要米级(< 5 m)遥感数据和其他新技术支持精细化研究; ③无人机可获得亚米级(如< 5 cm)数据,满足精细化监测需求,但受飞行管制、电池续航能力等限制,数据覆盖范围有限,且数据拼接等后处理复杂、传统的数据处理或反演算法可能不适用于亚米级空间分辨率数据; ④城市绿地对城市热环境调节功能研究较多,但当前100 m(及更粗空间分辨率)的热红外地表温度数据难以支持绿地蒸腾降温机理等精细化研究。可见,城市绿地的精细化资源评估与监测仍面临诸多挑战。

关键词： 自然资源 ; 城市绿地 ; 评估与监测 ; 遥感 ; 无人机

Abstract

Urban green spaces are important ecological resources in cities; therefore, quantitative assessment of these green space resources as well as establishment of monitoring system at multiple scales is urgently required for assisting natural resources management and eco-city construction. The objectives of this study are to summarize major methods used to assess and monitor two typical urban green space resources, i.e., vegetation and water bodies, in terms of quantity, quality, and ecosystem service value, and to discuss advantage and disadvantage of these methods. Some results have been obtained: ① Although traditional sampling methods can obtain quantitative information for urban vegetation, fragmentation and patch of urban vegetation has limited scaling such information to larger scales; ② Satellite remote sensing (RS), which can provide information such as spatial distribution, area, vegetation classification, and water quality, is an effective method to assess and monitor urban green spaces; nonetheless, detailed information, such as biomass and water volume, requires high spatial resolution (e.g., < 5 m) RS data as well as corresponding methods to process the data; ③ Unmanned aerial vehicle (UAV) can provide land surface information at high spatial resolution (e.g., < 5 cm); however, UAV has limitations, such as limited data coverage and challenged data processing; ④ Lots of studies focus on the relationship between urban green spaces and urban heat islands, but the mechanism, i.e., how much energy is consumed by evapotranspiration and its impact on cooling effect, is less focused, which is likely due to a relatively low spatial resolution of available thermal infrared RS data. In summary, there are still lots of challenges in assessing and monitoring nature resources, including urban green spaces.

Keywords： natural resources ; urban green space ; assessment and monitoring ; remote sensing ; unmanned aerial vehicle (UAV)

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本文引用格式

熊育久, 赵少华, 鄢春华, 邱国玉, 孙华, 王艳林, 秦龙君. 城市绿地资源多尺度监测与评价方法探讨. 国土资源遥感[J], 2021, 33(1): 54-62 doi:10.6046/gtzyyg.2020069

XIONG Yujiu, ZHAO Shaohua, YAN Chunhua, QIU Gouyu, SUN Hua, WANG Yanlin, QIN Longjun. A comparative study of methods for monitoring and assessing urban green space resources at multiple scales. REMOTE SENSING FOR LAND & RESOURCES[J], 2021, 33(1): 54-62 doi:10.6046/gtzyyg.2020069

0 引言

城市是人类聚居的重要场所,亦是人类社会发展水平的重要标志。截至2018年,全球55%的人口居住在城市,该比例在2050年预计将达到68%[1],尤其是发展中国家,正经历不同程度的城市化进程。在中国,城市化是目前和未来发展的基础与中心任务[2]。但城市化进程在推动社会进步与发展的同时,高强度的人类活动改变了下垫面属性,不透水建筑等灰色景观大量增加、自然植被等绿色景观极大减少,引发了诸如城市热岛等严重的生态环境问题,降低了城市环境的人居质量[3,4,5]。

绿地是城市生态系统的重要组成部分,亦是构成城市绿色基础设施的生态资源,在提供城市生物栖息地、调节城市热环境、丰富城市居民文化等方面发挥了积极作用[6,7,8,9],是改善城市景观、提升城市居民健康水平与生活品质的重要抓手之一。然而城市用地寸土寸金,城市建设中通常优先考虑经济效益明显的商业用地等,挤占绿地空间,减少城市生态系统的自然组成部分、导致自然生态功能退化、承载力下降等[5, 9]。党的十八大以来,我国政府高度重视生态文明建设、推进绿色发展,提出生态宜居城市、低碳城市、海绵城市等建设理念,以期构建科学合理的城市化格局和提升城市品质、建设美丽中国,有效推动了城市绿地的建设与发展。

我国城市化发展快速、地域分布广等特点,决定了该过程必然面临诸多新问题[10,11]。就资源领域而言,同种资源因管理部门和调查方法差异,会出现资源评估数据不一致等情况,严重影响管理决策。为此,国家调整组建了自然资源部,以解决山水林田湖草等自然资源的评估与监测[12],面临的首要问题是如何开展顶层设计、统一评价与监测标准、形成对应的技术体系,准确获取各种资源空间布局等基础资料,服务于资源保护与优化利用。相比自然生态系统,城市生态系统人为干扰程度高,高楼林立、下垫面异质性高[13],绿地呈斑块化、破碎化分布,增加了评估与监测的难度,基于自然生态系统发展的传统抽样调查方法与结果可能与实际不符[14,15]。为满足新时期城市建设与生态环境保护需求,迫切需要开展精细化的城市绿地资源监测、建立可行的评估方法与监测系统,准确、及时掌握绿地资源的状况与动态变化等基础数据,服务城市建设与管理决策。

为此,本文针对国家对城市绿地等自然资源评估与监测业务需求的紧迫性,以城市绿地中常见的植被和水体为例,从资源的数量、质量与生态价值3个层次梳理对比主流的监测评价方法并分析其优势; 在此基础上,结合本文作者近期研究成果,讨论城市绿地多尺度监测与评价方法,为我国新时期城市自然资源管理等工作提供方法支持。

1 城市植被评估与监测方法

本文以城市绿地中常见的植被和水体为例,从资源的数量、质量与生态价值3个层次(图1),梳理对比主流的监测评价方法。查阅文献发现,尽管有少量研究关注城市植被的健康状态(质量)[16,17],研究更多关注城市植被的类型组成(数量特征)与生态功能(生态价值)。

图1

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图1   城市绿地资源不同层次评价与监测方法

Fig.1   Different methods used to assess and monitor urban green space resources


1.1 数量方面

植被数量评估的传统方法以样方调查为主。理论上,样方越多、调查结果越好(如全面调查),但受经济条件等制约,通常采用抽样调查[14]。抽样调查的核心问题包括样方数量和样方面积确定[18,19]。最近研究发现,样方数量的多寡明显影响城市植被调查结果[15],但难点在于确定最佳样方数量。类似难点还有确定最佳样方面积,因为植被群落中的植物种类随生境面积而变化,只有达到某个临界值时(最小面积),植物种类才趋于饱和[20]。因此,对于乔木、灌木、草本等不同植被类型,采用的抽样面积变化范围大(如1~2 500 m2)[14]。此外,城市绿地植被种类构成多是人工配置的结果,抽样调查结果的代表性也值得商榷。

随着20世纪下半叶遥感技术的发展,从空中监测地表成为可能[21,22],发展了针对自然生态系统中森林资源种类判别、面积区划的方法等[23,24,25,26,27,28]。我国从第6次全国森林资源清查开始增加遥感抽样调查,以快速、高效地获取相关基础数据,评估森林资源[19]。但这些评估方法主要基于中低空间分辨率(<30 m)的卫星遥感数据,难以应用于斑块破碎、面积较小的城市绿地。近20 a来高空间分辨率商业卫星数据的发展,可提供地表0.5~5 m精细尺度的可见光遥感数据[21,29],城市绿地资源精细化评估与监测成为可能,包括植被种类识别、冠幅大小与数量等评估[30,31,32,33]。尤其是最近的三维激光雷达,可提取乔木的高度和垂直结构信息,如冠幅、胸径和树高、蓄积量/生物量等[34,35,36,37]。图2为本文作者利用三维激光雷达监测评估乔木资源的案例,图2(a)和(b)是在高植被覆盖度的森林区域,采用定点式激光扫描,以观测点为圆心360°探测,经数据处理后,提取各株乔木的三维信息,据此可获取树高、胸径、蓄积量/生物量等信息,与传统标准木调查方法获取结果精度相当(如胸径R2=0.99),且能获得林分任意树木的信息及空间位置关系等,提供更多的精准数据[35]; 图2(c)和(d)是利用最新研发的背包式激光雷达,在行走中探测道路周围植被的三维信息,根据点云提取植被相关信息,无需考虑样方数量、样方面积等问题,有助于调查斑块破碎的城市植被,若将该设备置于车载平台,可形成车载移动式观测,具有较好的应用前景[38]。

图2

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图2   三维激光雷达调查评估城市植被方法示例

Fig.2   Examples showing assessment and monitoring of urban trees based on three-dimensional laser scanning


1.2 生态功能

作为城市生态系统的重要组成部分,城市植被具有滞尘、净化空气、降温等生态功能[39,40,41]。蒸腾作用贯穿于植被的生活周期,该生理过程需要吸收能量,使植被具有天然的降温功能。观测表明,城市植被区域的温度比水泥等不透水设施的温度低5~20 ℃[42,43,44,45,46]。考虑到城市独特的热岛效应[47]及其引发的高温热浪等次生灾害[48,49,50,51]严重威胁公众健康和城市环境的生态宜居性,且植被蒸腾及蒸散发在城市生态系统中难以定量、研究关注较少[42-43, 52-53],本文重点探讨城市植被蒸腾作用及其降温功能的评价方法。

蒸腾作用受植被生理过程(如气孔开闭)与外界环境(如土壤水分含量、大气温度与气流运动等)影响,自然生态系统中植被蒸腾量的精确测算一直是地球系统和全球变化等研究领域的薄弱环节与难点 [21,54-57]。由于组成城市绿地的植被与自然植被差异大(如斑块破碎程度高、受园林管理措施影响频繁),加之城市下垫面异质性高、形成独特的城市小气候[13],适用于自然生态系统植被蒸腾的测算方法可能失效,增加了城市植被蒸腾量测算的难度,制约了城市绿地降温功能的准确量化[44, 53]。

目前,城市蒸散发典型的测算方法包括: ①针对乔木的树干液流计,用于测算单株树木的蒸腾量[58,59]; ②针对草本或低矮灌木的称重式蒸渗仪,即根据水量平衡方程在控制条件下测量蒸散发之外的变量,推算蒸散发[60,61]; ③针对不同植被类型,借助世界粮农组织参考作物蒸散发的概念,根据彭曼-蒙蒂斯公式与气象观测数据获得参考蒸散发,利用植被系数(landscape or garden coefficient)推算实际蒸散发[62,63]。尽管可将单株树干液流测算的蒸腾量推绎到林分或景观尺度,但结果的代表性与精确性值得商榷[64]。水量平衡方法测算结果代表某区域平均值,难以反映区域内蒸散发的空间差异,且仅在观测条件较好的固定试验场所,才可能准确测算水分平衡的各分量。城市区域高楼林立、对流强烈、微气象环境差异大,彭曼-蒙蒂斯公式仅适用于平坦均质地表; 加之植被系数受多种因素影响,缺乏不同水热条件下的观测值,亦影响推算的蒸散发,且同种植被赋予相同的植被系数,结果同样不能反映空间差异。

热红外卫星遥感的发展(表1)提供了100 m左右(如Landsat系列数据、ASTER数据)至1 km(如MODIS数据)的地表温度[65],如图3所示,促进了城市景观尺度地表温度空间差异及其与植被关系的研究 [66,67]。但基于卫星遥感地表温度和植被蒸腾机理探讨降温的研究相对较少[68,69],一方面城市中蒸散发及其植被蒸腾组分定量困难,二是目前较低空间分辨率的卫星遥感数据(约100 m)制约了对城市蒸散发深入、系统的研究,因为城市植被斑块破碎、通常在10 m范围内都会因植被类型差异而使蒸散发具有高异质性[70],用100 m的热红外遥感数据研究城市全局尺度(104~106 m)基本可行,但限制了街区尺度(<104 m)的精细研究。近年无人机的发展使亚米级遥感数据获取成为可能,无人机光学数据获取相对成熟[71]。受传感器与无人机搭载平台耦合等制约,高空间分辨率热红外数据的无人机获取仍然面临较大挑战,如确定图像地理空间位置、拼接不同航带图像等。本文作者经过前期探索,研发了基于热红外温度的三温模型,可测算城市草坪、灌木、乔木等植被的蒸散发,与传统的波文比能量平衡法、树干液流法等测算结果接近,但可提供蒸散发空间分布信息、实现了城市复杂下垫面蒸散发精准估算[42-43, 53]; 提出移动式运动样带法,可定量研究条带状城市绿地植被对温度的影响(图4)[41]; 近期解决了热红外传感器与无人机平台兼容搭载、地理坐标定位、数据拼接处理等,实现了亚米级地表温度及蒸散发时空分布数据获取(图5),有望为系统评估城市绿地蒸散发及其降温效果提供新方法。

表1   典型的卫星热红外遥感数据

Tab.1  Typical thermal infrared remote sensing data

传感器卫星平台幅宽/
km		空间分
辨率/m		时间分
辨率
TMLandsat(美国)18512016 d
ETM+60
OLI100
ASTERTerra(美国)60909~16 d
IRMSSCBERS-01/02(中国)12015626 d
IRSHJ-1B(中国)7203004 d
VIIRSNPP(美国)3 0004004 h
MODISTerra/Aqua(美国)2 3301 0001 d
AVHRRNOAA(美国)2 8001 1000.5 d
SEVIRIMSG(欧盟)全球3 00015 min
SVISSRFY-2(中国)全球5 0001 h

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图3

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图3   基于ASTER AG100v003产品的广州市海珠区2000—2008年平均地表温度空间分布

Fig.3   Distribution of average land surface temperature based on ASTER product (AG100v003) for Haizhu District, Guangzhou City during 2000—2008


图4

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图4   基于车载(电动摩托车)移动式的运动样带城市绿地类型与温度关系研究方法

(运动样带位于广东省深圳市南山区大学城)

Fig.4   A mobile traverse observation method to study the relationship between air temperature and different types of urban green spaces


图5

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图5   基于无人机与热红外遥感的城市绿地蒸腾作用与地表温度关系研究

(研究区位于图4(b)东北局部区域,观测时间为2019年11月7日12时)

Fig.5   An unmanned aerial vehicle and thermal infrared based method to study the mechanical impact of ET on LST


2 城市水体评估与监测方法

2.1 水量评估

传统的水资源数量评估,以降水、径流为主要要素,多在流域尺度根据水量平衡原理开展。若能将城市生态系统的各水文分量监测清楚,可以测算水总量。在仅考虑城市河流、湖泊水库情况下,具体水量的评估可考虑使用体积描述,即水面面积与水深之积。遥感数据测算面积精度非常高、相对容易。此时,仅需获得水深信息即可。目前数字高程模型可提供高程信息,如30 m空间分辨率的ASTER GDEM,高程精度平均为(7.4±12.7) m。我国最新的高分七号卫星可获取亚米级立体影像,高程精度可望高达1.5 m。为利用高精度遥感信息开展水量评估提供了丰富的数据源。基于遥感的水量评估方法,可获取空间任意位置水量信息,弥补了传统水量平衡测算法只能获取区域平均值的不足。

2.2 水质评估与监测

随着生态宜居等城市建设需求,城市水体水质成为政府、研究人员与城市居民关注的焦点。传统的水质评估依赖监测断面定点观测数据,但受到监测断面数量、采样频次等限制,评价结果通常难以反映水质的时空分布信息。遥感数据覆盖范围广、快速成像与周期成像的特征,为水质时空监测提供了强有力的工具。目前典型的监测评估要素包括水温、叶绿素浓度、总悬浮物、可溶性有机污染物等[72]。根据《全国城市市政基础设施建设“十三五”规划》,将整治城市黑臭水体2 000多个、总长度约5 800 km,建立城市水质监测与评估方法必然是治理的重要内容之一。图6是本文作者利用30 m空间分辨率的Landsat系列数据评估广东省珠海市大镜山水库(约1 km2)叶绿素浓度时空分布的案例,尽管采用线性回归算法,却弥补了传统水质评估中有限采样点难以反映水质空间分布的不足[73]。考虑到我国中小型水库(库容小于106 m3)数量高达93 308座[74],是未来水质与水量监测与管理的重点,传统人工调查方法难以快速获取水质空间信息、周期更新更难,基于遥感影像的水质评估方法可快速、周期性地获取水质时空分布信息,必将发挥重要作用。城市水质遥感监测与评估的难点在于,由于水体浑浊度高,无机悬浮颗粒、可溶性有机污染物的光谱特征与叶绿素重叠,水体光学特性复杂,增加了水质参数的反演难度[73]。且卫星遥感数据空间分辨率与光谱分辨率的博弈,导致多(高)光谱数据的低空间分辨率制约城市河流、湖泊、水库的识别,或高空间分辨率影像的低光谱分辨率制约水质参数的精细化遥感反演。因此,基于无人机高光谱、高时空分辨率的数据获取与水质反演是未来城市水质监测与评估的重要手段。

图6

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图6   基于Landsat TM/ETM+遥感数据的水质遥感多时间序列监测评估示 例: 广东省珠海市大镜山水库叶绿素a浓度时空分布图

Fig.6   Examples showing chlorophyll-a concentration (mg/L) monitoring at Dajingshan Reservoir, Zhuhai City, Guangdong, based on Landsat TM/ETM+ series datasets


3 结论与建议

1)尽管基于样方的传统抽样方法可获得城市绿地的数量信息,但城市绿地高度斑块化特征限制了样方结果尺度推绎。

2)卫星遥感是监测城市绿地的有效手段,可准确获取绿地空间分布、面积、种类、质量变化等信息,但生物量(或蓄积量)、体积等信息需要米级(< 5 m)遥感数据和其他新技术支持精细化研究。

3)无人机可获得亚米级(如< 5 cm)数据,满足精细化监测需求,但受飞行管制、电池续航能力等限制,数据覆盖范围有限,且数据拼接等后处理复杂、传统的数据处理或反演算法可能不适用于亚米级空间分辨率数据。

4)城市绿地类型、面积与城市温度关系研究较多,但当前100 m(及更粗空间分辨率)的热红外地表温度数据难以支持绿地蒸腾降温机理等精细化研究。可见,城市绿地的精细化资源评估与监测仍面临诸多挑战,建议未来加强米级、亚米级遥感数据在城市绿地评估与监测中的应用研究,加强研究多传感器与无人机搭载平台耦合及数据处理业务化流程、研发适用于城市绿地精细化管理的反演算法。

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