徐涵秋
, 张好
福州大学环境与资源学院/福州大学遥感信息工程研究所/福建省水土流失遥感监测评价重点实验室,福建 福州350108
XU Han-qiu, ZHANG Hao
中图分类号: TP79
文献标识码: A
文章编号: 1000-0690(2015)07-0867-06
收稿日期: 2014-01-10
修回日期: 2014-04-26
网络出版日期: 2015-07-20
版权声明: 2015 《地理科学》编辑部 本文是开放获取期刊文献，在以下情况下可以自由使用：学术研究、学术交流、科研教学等，但不允许用于商业目的.
基金资助:
作者简介:
作者简介：徐涵秋（1955-）,男,江苏射阳人,博士,教授,博士生导师,主要从事环境资源遥感应用研究。E-mail:hxu@fzu.edu.cn
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摘要
利用Landsat卫星影像,采用新型遥感生态指数(RSEI)对海岛型城市厦门在不同时期的生态变化进行研究,以期揭示城市的快速发展对城市生态质量的影响。研究结果表明,1993~2009年,厦门岛的城市建筑用地虽经过大幅扩张,但并没有造成生态质量的大幅下滑,其RSEI生态指数值仅略为下降。分析表明,这主要是得益于厦门城市发展的科学规划。但是厦门岛西北部的生态较差,是今后进一步改善生态的重点地区。
关键词：
Abstract
Timely and precisely monitoring of ecological responses to urban′s fast expansion has become a very important issue for regional decision-makers. To meet this requirement, this article utilized a recently developed, remote sensing based ecological index (RSEI) to assess the urban ecological quality change in Xiamen, an island city located in Fujian Province, southeastern China, during the past two decades. The RSEI was constructed by integrating four important ecological indicators including greenness, wetness, dryness and heat.It can be represented respectively by four remote sensing indices or components, i.e., normalized difference vegetation index (NDVI), index-based built-up index (IBI), wetness component of the tasseled cap transformation (Wet), and land surface temperature (LST). The principal component analysis (PCA) was utilized to compress the four indicators into four PC components. Instead of using a simple, traditional weighted addition algorithm, the first component (PC1) was used to construct the RSEI because the PC1 is the best one among the PC components to represent the four indicators. Time-series Landsat Thematic Mapper (TM) images of 1993 and 2009 of Xiamen scenes, both acquired in summer season, were employed to compute RSEI and evaluate the ecological quality of the island. The successful application of the RSEI in Xiamen has reveled that, in spite of a fast urban expansion in the island during the study period from 1993 to 2009, the ecological quality of the island has not degraded substantially, because the RSEI value only declined slightly from 0.558 in 2003 to 0.534 in 2009. This is mainly owing to a scientific urban planning for the island city, which kept sufficient green spaces for the city and hence resulted in a much higher ratio of urban green spaces in 2009 than that in 2003. Nevertheless, low-grade ecological conditions have also been detected by the leveled RSEI map in the newly developed northwestern part of the island city, where the Xiamen Airport is located. The area, therefore, should be improved for the ecological quality in the near future. The RSEI-revealed results have also been compared with those using the index generated with a simple weighted addition algorithm. The result shows that the RSEI can explain the island city′s ecological status more reasonably than the latter.
Keywords：
当前,卫星遥感对地观测技术以其宏观、快速、实时等优点已被广泛地用于对城市、草地、森林、河流等各种生态系统进行监测和评价[1~5]。但是现有的遥感生态监测评价技术大多基于单一的指标,如在城市遥感监测和评价中往往只依靠城市植被、城市建筑用地、地表温度等单一指标来进行评测[4,6~9]。这种单指标的监测与评价往往只能解释某一方面的生态特征。而实际上在整个城市生态系统中,每个指标的互动综合影响着整个城市的生态系统。因此,需要用综合性的生态指标来对城市生态系统进行综合测评。
厦门是福建省东南部的海岛型城市。改革开放以来,厦门城市快速扩展,其原来局限于西部的城市建成区已扩展至整个岛屿。这种大规模的城市扩展是否给海岛的生态造成破坏也一直是人们关注的问题。本文采用新型遥感生态指数(Remote Sensing based Ecology Index, RSEI) [10]来对厦门岛的生态状况进行监测与评价,以期客观地评测厦门城市经快速扩展后的生态质量变化情况。
在反映生态质量的诸多自然因素中,绿度、湿度、热度、干度是与人类生存息息相关的4个重要指标,因此常被用于评价生态系统[4,8,11]。本文采用的遥感生态指数能够综合体现这4个指标, 它是这4个指标的函数,即：
RSEI=f(VI,Wet,LST,BI) （1）
式中,RSEI为遥感生态指数;VI为代表绿度的植被指数,由NDVI植被指数来代表;Wet为湿度,由缨帽变换的湿度分量Wet来代表;LST为代表热度的地表温度,BI为干度,由建筑指数IBI来代表,它们的公式分别为[12~15]：
Wet=0.0315ρ1+0.2021ρ2+0.3102ρ3+0.1594ρ4-
0.6806ρ5-0.6109ρ7 （2）
NDVI=(ρ4-ρ3)/(ρ4+ρ3) （3）
LST=γ[ε-1(ψ1Lsensor+ψ2)+ψ3]+δ （4）
IBI={2ρ5/(ρ5+ρ4)-[(ρ4/(ρ4+ρ3)+ρ2/(ρ2+ρ5)]}/
{2ρ5/(ρ5+ρ4)+[(ρ4/(ρ4+ρ3)+ρ2/(ρ2+ρ5)]} （5）
式中,ρi(i=1,...,5,7)分别为TM影像各对应波段的反射率,地表温度采用Jiménez-Muñoz & Sobrino的单通道算法[14]来计算,Lsensor为传感器处辐射值[W/(m2·sr·μm)],ε为地表比辐射率,γ和δ是基于Planck函数的2个参数,ψ1、ψ2、ψ3是与大气总水汽含量相关的大气参数,这些参数可以从文献[14]中计算获得。由于以上4个指标的量纲不尽相同,因此在计算RSEI之前,必须对它们进行正规化。
如何以单一变量来耦合以上4个指标变量,是综合指数构建的关键。常用的方法是简单地将各个指标加权求和[1,4,16],但RSEI则以主成份变换来集成以上4个指标。这一做法的最大优点就是各指标的权重不是人为确定,而是根据各个指标对第一主成分PC1的贡献度来自动、客观地确定。RSEI可由下式计算：
RSEI0=1-{PC1[f(NDVI,Wet,LST,IBI)]}
RSEI=(RSEI0-RSEI0_min)/(RSEI0_max-RSEI0_min) （6）
式中,RSEI0是经主成分变换后的初始生态指数,对其归一化后,就获得了RSEI遥感生态指数,其值介于[0, 1]之间,越接近1,生态越好,反之,越差。
为了与RSEI对比,同样用常规的加权求和法对以上4个指标进行综合,形成另一生态综合指数EIw：
EIw=∑(w1∙Wet + w2∙NDVI + w3∙IBI+ w4∙LST) （7）
式中,wi（i=1,2,3,4）为归一化后的指标权重,Wet、NDVI、IBI、LST的权重值分别为0.2、0.3、0.3、0.2。将NDVI和IBI的权重高赋0.1是考虑到绿地和建筑用地与人类的活动直接相关。
为了保证结果的可比性,严格选取季相、类型都相同的影像作为遥感数据源。所选的影像都为Landsat TM影像,时相同为6月,分别为1993-6-21和2009-6-6,它们的植被具有相近的生长状态,这对生态评价尤为重要。
首先对影像进行辐射校正,采用集成Chander[17]和Chavez[18]算法的IACM大气校正模型[19]将原始影像的灰度值转换为传感器处反射率,然后进行几何校正,最后利用公式(2)~(7)分别获得RSEI影像和EIw影像。
图1是厦门岛的原始影像及其RSEI和EIw影像。分析RSEI和EIw与4个分指标的相关度表明（表1）,EIw和4个分指标的平均相关度在1993和2009年都很低,不及RSEI的1/2,甚至低于任何一个分指标,可见,EIw无法综合代表4个分指标,而RSEI和各个分指标的平均相关度最大可达0.877。
表2是RSEI、EIw和4个分指标经正规化后的统计值。为了更好地分析厦门岛的生态质量,进一步将各年份的RSEI和EIw指数以0.2为间隔分成1~5级,分别代表差、较差、中等、良、优5个生态等级(图1e~h)。从表2可知,加权求和法计算的EIw均值要低于主成分分析法计算的RSEI,其标准差也明显小于RSEI。这说明EIw的数据分布范围很集中,都集中在均值附近,中等生态质量等级的分布面积偏大(图1g~h),其格局与厦门影像(图1a~b)比较,就显得很不协调;而RSEI的分级影像(图1e~f)与原影像的格局十分吻合。从图1还可以看出,EIw影像无法合理解释实际情况。厦门岛南部茂密森林覆盖的山地在2009年多被其归为生态质量中等的3级,而北部机场却由于跑道上的草而被定为生态质量较好的4级,明显与实际情况不符。而RSEI就不会出现这种现象,它将机场一带的生态主要定为较差的2级。显然,RSEI除了考虑草地的特征外,还顾及了机场的大片水泥跑道和相关的建筑设施,因此其所定的2级就显得更为合理。综上,采用主成分变化构建的综合指数RSEI显然要比加权求和法构建的EIw更为合理,已有研究表明,RSEI与国家环保部推出的生态指数EI有很好的可比性[10]。因此以下采用RSEI对厦门岛的生态进行分析。
从图1a~b可以看出,厦门城市建成区从1993~2009年有了大幅扩展。研究表明[21],厦门岛的城市建成区面积从1993年的45.45 km2,大幅上升到2009年的108.33 km2。在2003年,城市建成区只局限于厦门岛的西部,而到了2009年,城市建成区已扩展至全岛。RSEI生态指数的统计表明,厦门岛在这一期间的生态质量随着城市的扩展有所下降,表现为RSEI值由0.558下降到0.534 (表2);生态优良等级(4~5级)所占的面积比例从43.9%下降到29.5% (表3)。从图1a~b可知,由于城市的大规模扩展使得1993年厦门岛东北部的大片绿被到了2009年全部转为城市建设用地,从而导致了生态质量的下滑。这在定量指标上表现为,2009年的绿度指标NDVI的均值要小于2003年,而干度指标IBI和热度指标LST的均值都大于1993年(表2),致使由其计算的RSEI生态指数值的下降。从图1d、f来看,厦门岛生态质量较差的地区主要位于西北部。这些地区的RSEI等级多为差与较差,对应的LST可高达0.7,IBI一般都大于0.65,Wet小于0.5,NDVI小于0.45,表明这些地区建筑比例热度都很高,而植被覆盖和地面湿度却很低。
图1 厦门岛遥感影像(a, b)、RSEI影像(c, d)、RSEI等级(e, f)与EIw (g, h)分级影像
Fig. 1 Landsat images (a, b), RSEI images (c, d), RSEI grades (e, f) and EIw (g, h) images of Xiamen island
表1 RSEI、EIw和各指标的相关性统计
Table 1 Correlation analysis of RSEI, EIw and four indicators
| 1993年 | 2009年 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Wet | NDVI | IBI | LST | 平均相关度 | Wet | NDVI | IBI | LST | 平均相关度 | |
| Wet | 1 | 0.626 | -0.852 | -0.566 | 0.681 | 1 | 0.603 | -0.782 | -0.442 | 0.609 |
| NDVI | 0.626 | 1 | -0.913 | -0.639 | 0.726 | 0.603 | 1 | -0.864 | -0.517 | 0.661 |
| IBI | -0.852 | -0.913 | 1 | 0.657 | 0.807 | -0.782 | -0.864 | 1 | 0.498 | 0.715 |
| LST | -0.566 | -0.639 | 0.657 | 1 | 0.621 | -0.442 | -0.517 | 0.498 | 1 | 0.486 |
| RSEI | 0.79 | 0.931 | -0.95 | -0.836 | 0.877 | 0.697 | 0.875 | -0.844 | -0.85 | 0.817 |
| EIw | 0.401 | 0.619 | -0.537 | 0.163 | 0.43 | 0.299 | 0.469 | -0.301 | 0.456 | 0.381 |
表2 1993年和2009年4个指标和生态指数RSEI、EIw的统计值
Table 2 Statistics of RSEI, EIw and four indicators
| 1993 | 2009 | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Wet | NDVI | IBI | LST | RSEI | EIw | Wet | NDVI | IBI | LST | RSEI | EIw | |
| 均值 | 0.587 | 0.719 | 0.490 | 0.450 | 0.558 | 0.539 | 0.669 | 0.680 | 0.503 | 0.458 | 0.534 | 0.439 |
| 标准差 | 0.093 | 0.139 | 0.139 | 0.126 | 0.197 | 0.112 | 0.067 | 0.121 | 0.069 | 0.134 | 0.166 | 0.100 |
进一步研究发现,1993~2009年间厦门岛的东北部虽然有大片绿被消失,但2009年的RSEI生态指数值下降并不多。分析表明,这主要得益于这一期间城市建成区本身生态状况的改善和南部山地植被质量的提高。从图1a~b可以看出,1993年的建成区道路狭窄,建筑密度高,绿化程度低,生态多处于差或较差级别。而2009年的建成区随着道路的拓宽,建筑密度明显降低,大量绿化带分布在经纬分明的街道上,贯穿着整个岛屿,形成许多绿化条带,从而部分补偿了1993年来东部的大片绿被消失,没有造成RSEI指数的大幅度降低。定量统计表明(表3),这一期间虽然城市扩张使得生态优良等级所占的面积比例下降了14个百分点,但城市建成区本身生态质量的提升也使生态等级为差与较差(1~2级)的面积比例同时下降了近10个百分点,从而部分补偿了生态优良等级所占面积比例的下滑。
表3 厦门岛生态等级的面积和比例
Table 3 The area and percentage of the RSEI-based ecological grades
| RSEI 级 | 1993 | 2009 | ||
|---|---|---|---|---|
| 面积（km2） | 百分比（%） | 面积（km2） | 百分比（%） | |
| 1: 差 (0.0~0.2) | 4.53 | 3.66 | 0.39 | 0.27 |
| 2: 较差(0.2~0.4) | 31.50 | 25.49 | 27.14 | 18.69 |
| 3: 中等(0.4~0.6) | 33.28 | 26.93 | 74.80 | 51.53 |
| 4: 良(0.6~0.8) | 40.34 | 32.64 | 25.41 | 17.50 |
| 5: 优(0.8~1.0) | 13.96 | 11.29 | 17.42 | 12.00 |
| 合计 | 123.61 | 100.00 | 145.15 | 100.00 |
以上分析表明,厦门岛的城市建筑用地虽经大幅扩张,却没有造成生态质量的大幅下滑,这与其科学的城市规划有密切的关系。厦门作为一个海岛型城市,其城市的功能定位和规划明显有别于许多内陆城市。它以生态型港口风景旅游城市为发展定位,以优化建设厦门本岛为核心进行科学的城市规划。由于厦门岛的南部为大片山地(图1a),城市只能沿东北部扩展。针对当时东北部地区在开发建设中对生态用地重视不足,厦门对1993年东北部地区的规划进行重新调整,提出 以“一个绿心、四条楔形城市生态绿地”为框架的分区规划方案。在城市扩展的同时,适当控制居住用地,重视维护四条楔形城市生态绿地,保证厦门本岛生态保护用地。图1b的2009年厦门岛影像显然反映了这一科学规划的结果,拓宽的道路、降低的建筑密度、经纬分明的绿化带,有力地保证了厦门岛生态环境的舒适性。
对2009年的NDVI、Wet、LST、IBI和RSEI专题影像采集了25 000个样点,然后对其进行逐步回归分析,建立了基于RSEI的城市生态模型（模型通过了1%的显著性检验）：
RSEI=0.262Wet+0.615NDVI-0.334IBI-0.647LST+0.413 (R2=0.985)
分析模型中各指标变量的系数可以看出,对生态起正面影响的NDVI和Wet的综合影响力不及起负面影响的IBI和LST。因为IBI和LST系数的绝对值之和大于NDVI与Wet的系数之和。
从回归模型来看,代表热度的LST的负面影响在2009年已经突显出来,其系数的绝对值最大。因此,未来厦门如要提高生态质量,必须通过增加绿地,降低建筑覆盖率来降低热度。由于NDVI对RSEI的影响度要比IBI大,因此以增加绿被来提高生态质量的效果可能会更好。根据模型预测,如要将RSEI提升0.1,厦门的NDVI就须相应增加0.16,大约相当7.3 km2的绿地面积[20]。但是,如果能在增加绿地的同时减少等量的建筑用地,则可达到事半功倍的效果[22]。
绿度、湿度、热度和干度是反映城市生态状况的重要因素,在此基础上以主成分变化集成的综合生态指数RSEI可比以加权求和集成的EIw指数更好地定量刻画城市的生态质量及其变化。
基于RSEI指数分析的结果显示,1993~2009年,厦门岛的城市建筑用地虽经大幅扩张,但并没有造成生态指数的大幅下降,这与其科学的城市规划和定位是分不开的。厦门基于其海岛型城市的特点,将其发展定位为生态型港口风景旅游城市,并对其进行科学的规划,从而保证了厦门生态环境的舒适性。厦门岛的这一案例研究说明,在城市规模大幅扩张的背景下,在城市的科学定位和规划上做足文章,仍可以最大限度地保护城市的生态环境。
The authors have declared that no competing interests exist.
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Despite the ecological and economical importance of tropical forests they are currently affected by human activities, mainly through deforestation and selective extraction. With the aim of making an opportune diagnosis of the condition of forests, we developed an ecological index based on qualitative and semi-quantitative data, allowing a quick diagnosis in order to manage and conserve tropical forests. We evaluated 44 plots of tree vegetation, measuring canopy height, number of strata, tree cover, dominant trees, number of tree species, as well as the management of and damage to the forest. The data of each parameter was classified in categories of 3, 4 or 5, which were normalized between 0 and 1 for the worst and best characteristics, respectively. For the purpose of analysis, the average, a set of IF鈥揟HEN rules, and fuzzy logic were applied and as a result we obtained a model that measures the ecological condition of the tropical forests. The model has the advantages of having an ecological basis, allows data to be gathered quickly and is clear and easy to manage and interpret. When running the model, the value of each intermediate variable is displayed, thus allowing the detection of where necessary action is required to improve the ecological condition of the forest. We expect this index to contribute in evaluating the effectiveness of forest management and possibly offer advice for the short-term management and conservation of the remnants of tropical forests.
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| [2] |
The ecological status of grasslands on lowland farmlands in western Ireland and implications for grassland classification and nature value assessment [J].https://doi.org/10.1016/j.biocon.2010.03.035 URL 摘要
The identification and protection of High Nature Value (HNV) farmland is an objective of the European Rural Development policy which has yet to be met by Member States. Remote sensing and models based on farm statistics are commonly used to identify HNV farmland. Use of datasets such as Corine Landcover Classes is widespread but it has been acknowledged that such datasets can significantly overlook fine-scale biodiversity features. In countries where farmland is predominantly grass-based, there is an added difficulty in distinguishing between grassland types without undertaking field-scale survey work. This study was conducted to improve our knowledge of grassland biodiversity on lowland farms with a view to helping assess their potential conservation value in a High Nature Value farmland context. We analysed the grassland species composition of 603 fields on 32 lowland farms and investigated their relationship to management, ecological and spatial descriptors. Non-metric Multidimensional Scaling (NMS) and Multi-Response Permutation Procedure (MRPP) analyses of the grasslands and their ecology on these farms revealed a continuum between semi-natural and improved agricultural grasslands, including an intermediate Semi-Improved Grassland type. This gradation from improved to semi-natural grassland highlights the biodiversity variation that occurs on farms that are frequently considered to be of low nature value. Statistical analyses showed that management practices, and especially soil fertility, were most strongly associated with grassland type. The detailed description of the grasslands that occur on these lowland farms has the potential to provide a better assessment of the overall nature value of a farm, potentially aiding the identification of Type 2 High Nature Value farmland. Before this can be achieved, however, there is a need to amend the grassland classification system used in Ireland in order that intermediate semi-natural grassland assemblages can be iden
|
| [3] |
Urban expansion and heat island dynamics in the Quanzhou region, China [J].https://doi.org/10.1109/JSTARS.2009.2023088 URL 摘要
Urban spatial expansion in the Quanzhou region of southeastern China has been accelerated over the past 20 years. This has caused land cover changes and, thus, has significant impacts on the local ecosystem and climate. To study the urban expansion and heat island dynamics of the region over the past 20 years, multitemporal Landsat TM images of 1987, 1996, and 2006 were used. The estimation of the urban expansion was assisted by the index-based built-up index (IBI) through enhancing built-up land features in the images. The urban-heat-island (UHI) effect was assessed using the urban-heat-island ratio index (URI). Multitemporal analysis indicates that the great increase in urban area has resulted in the development of UHIs in the region. Regression statistics reveal that built-up land has a positive exponential relationship with land surface temperature (LST). Therefore, the increase in built-up land percentage can exponentially accelerate the rise of LST.
|
| [4] |
Urban Neighborhood Green Index-A measure of green spaces in urban areas [J].https://doi.org/10.1016/j.landurbplan.2012.01.003 URL [本文引用: 3] 摘要
Urban green spaces (UGS) form an integral part of any urban area and quantity and quality of UGS is of prime concern for planners and city administrators. Objective measure of greenness using remote sensing images is percentage area of green, i.e., Green Index (GI), which is insensitive to spatial arrangement within the areal units. Measuring UGS at neighborhood level is important as neighborhood is the working level for application of greening strategies. Neighborhood (NH) is synonymous of nearness and can be defined as an area of homogeneous characteristics. The Urban Neighborhood Green Index (UNGI) aims to assess the greenness and can help in identifying the critical areas, which in turn can be used to identify action areas for improving the quality of green. For the development of UNGI, four parameters, i.e., GI, proximity to green, built up density and height of structures were used and weighted using Saaty's pair wise comparison method. Four different types of NH were compared and it was found that mean GI (0.44) is equal for high-rise low density and low-rise low density NH, i.e., both areas have same quality of urban green based on GI. But mean UNGI is higher for low-rise low-density NH (0.62), as compared to high-rise low-density NH (0.54), hence, area of highrise NH requires more amounts of good quality properly distributed green as compared to low-rise NH. The input for UNGI is easily derivable from RS images, besides the developed method is simple, and easily comprehendible by city administrators and planners.
|
| [5] |
Estimating the ecological status and change of riparian zones in Andalusia assessed by multi-temporal AVHHR datasets [J].https://doi.org/10.1016/j.ecolind.2008.05.013 URL [本文引用: 1] 摘要
Following the European Commission's Water Framework Directive all surface waters in EU's Member States must reach a good status by 2015. The evaluation of this status will be partly based on ecological criteria, such as the hydro-morphological quality criteria which also evaluate the structure and condition of the riparian zone. Riparian zones with undisturbed or nearly undisturbed condition are given high-ecological status. The agri-environmental measures in the EU promote an extensive use of land to protect the farmed environment and its biodiversity. Recent studies in Andalusia and elsewhere suggest that extensification leads to riparian zones with higher ecological status compared to intensively used areas. We suggest that extensification and thus better ecological status of the riparian zone can be partly approximated by the amount of vegetation permanently present on the area. For this the so-called permanent vegetation fraction was derived from a multi-temporal advanced very high-resolution radiometer (AVHRR) dataset and was used (1) to classify the ecological status of the riparian zone into two classes, favourable and unfavourable, and (2) to assess the effect of agricultural practices on these areas. The classification was validated by field observations in the Guadalquivir river basin while detailed information on farming practices helped to assess the effect of agriculture on the riparian zone. The assessment was carried out in olive land cover because erosion control in olive cultivations is the most widely implemented measure in Andalusia. Results suggest that the remotely sensed permanent vegetation fraction is a good indicator of the favourable and unfavourable ecological status of the riparian zone. Furthermore, extensification of agricultural practices expressed in terms of increasing permanent vegetation cover was shown to have positive effect on the riparian zone as opposed to areas where no measures were implemented.
|
| [6] |
Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery [J].https://doi.org/10.1016/j.rse.2006.09.003 URL [本文引用: 1] 摘要
This paper compares the normalized difference vegetation index (NDVI) and percent impervious surface as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the land surface temperature (LST), percent impervious surface area (%ISA), and the NDVI. Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data were used to estimate the LST from four different seasons for the Twin Cities, Minnesota, metropolitan area. A map of percent impervious surface with a standard error of 7.95% was generated using a normalized spectral mixture analysis of July 2002 Landsat TM imagery. Our analysis indicates there is a strong linear relationship between LST and percent impervious surface for all seasons, whereas the relationship between LST and NDVI is much less strong and varies by season. This result suggests percent impervious surface provides a complementary metric to the traditionally applied NDVI for analyzing LST quantitatively over the seasons for surface urban heat island studies using thermal infrared remote sensing in an urbanized environment.
|
| [7] |
Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface Index (NDISI) [J].https://doi.org/10.14358/PERS.76.5.557 URL 摘要
The fast urban expansion has led to replacement of natural vegetation-dominated land surfaces by various impervious materials. This has a significant impact on the environment due to modification of heat energy balance. Timely understanding of spatiotemporal information of impervious surface has become more urgent as conventional methods for estimating impervious surface are very limited. In response to this need, this paper proposes a new index, normalized difference impervious surface index (NDISI), for estimating impervious surface. The application of the index to the Landsat ETM+ image of Fuzhou City and the ASTER image of Xiamen City in China has shown that the new index can efficiently enhance and extract impervious surfaces from satellite imagery, and the normalized NDISI can represent the real percentage of impervious surface. The index was further used as an indicator to investigate the impact of impervious surface on urban heat environment by examination of its quantitative relationship with land surface temperature (LST), vegetation, and water using multivariate statistical analysis. The result reveals that impervious surface has a positive exponential relationship with LST rather than a simple linear one. This suggests that the areas with high percent impervious surface will accelerate LST rise and urban heat island development. 漏 2010 American Society for Photogrammetry and Remote Sensing.
|
| [8] |
Remote sensing of urban heat islands by day and night [J].https://doi.org/10.14358/PERS.71.5.613 URL [本文引用: 1] 摘要
Urban heat island is a term used for the elevation of urban air temperatures over those in surrounding rural areas; the difference is generally greater at night than during the day. This article reports on a study in which a night-time thermal image from the ASTER satellite sensor, of the western New territories of Hong Kong is compared with a daytime Landsat Enhanced Thematic Mapper Plus (ETM+) thermal image obtained nineteen days earlier. Densely built high rise areas which appear cool on daytime images are conversely, relatively warm on nighttime images, though the temperature differences are not well developed at night. At night, proximity to extensive cool surfaces such as forested mountain slopes appears to be influential in maintaining cooler building temperatures. The relevance of satellite-derived surface temperatures for studies of urban microclimate is supported by field data of surface and air temperatures collected in the study area. The author concludes that thermal images from both the ETM+ and ASTER sensors are of adequate spatial and radiometric resolution for the study of urban microclimatic patterns. However, a major problem still occurs in that urban heat islands are essentially a nighttime phenomenon and satellite-based studies are less able to make nighttime images.
|
| [9] |
Remote sensing of the urban heat island effect across biomes in the continental USA [J].https://doi.org/10.1109/IGARSS.2010.5653907 URL [本文引用: 1] 摘要
We find that ecological context significantly influences the amplitude of summer daytime UHI (urban–rural temperature difference) the largest (802°C average) observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, ISA is the primary driver for increase in temperature explaining 70% of the total variance in LST. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.902°C, except for urban areas in biomes with arid and semiarid climates. The average amplitude of the UHI is remarkably asymmetric with a 4.302°C temperature difference in summer and only 1.302°C in winter. In desert environments, the LST's response to ISA presents an uncharacteristic “U-shaped” horizontal gradient decreasing from the urban core to the outskirts of the city and then increasing again in the suburban to the rural zones. UHI's calculated for these cities point to a possible heat sink effect. These observational results show that the urban heat island amplitude both increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.
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| [10] |
区域生态环境变化的遥感评价指数 [J].https://doi.org/10.3969/j.issn.1000-6923.2013.05.019 URL Magsci [本文引用: 2] 摘要
基于遥感信息技术提出一个新型的遥感生态指数(RSEI),以快 速监测与评价区域生态质量.该指数耦合了植被指数、湿度分量、地表温度和土壤指数等4个评价指标,分别代表了绿度、湿度、热度和干度等4大生态要素.与常 用的多指标加权集成法不同的是,本研究提出用主成分变换来集成各个指标,各指标对RSEI的影响是根据其数据本身的性质来决定,而不是由人为的加权来决 定.因此,指标的集成更为客观合理.将RSEI应用于福建长汀水土流失区,并与国家环境保护部《生态环境状况评价技术规范》中的生态指数EI的计算结果相 比较,发现二者的结果具有可比性.不同的是,RSEI不仅可以作为一个量化指标,而且还可以对区域生态环境变化进行可视化、时空分析、建模和预测.因此, 可弥补EI指数在这些方面的不足.
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| [11] |
脆弱生态环境定量评价方法的研究 [J].
<p>对常平镇1988～1996年期间景观组力转移情况和动态变化进程进行了研讨,重点分析非农用建设用地的主要来源,自然和农业组分向城镇转移的基本模式和景观动态变化的主要阶段。研究结果表明,常平镇城镇用地规模不断膨胀的主要来源依次是农田、果园和水体,林地受地形影响,转移量最少;自然和农业组分向城镇转移主要有直接和间接两种模式,直接模式是组分转移的主要方式;全镇过去8年的景观动态变化过程可分3个阶段,即1988～1991年传统农业景观阶段,1992～1994年城乡混合景观阶段和1996年城镇景观阶段。</p>
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| [12] |
A TM tasseled cap equivalent transformation for reflectance factor data [J].https://doi.org/10.1016/0034-4257(85)90102-6 URL [本文引用: 1] 摘要
A transformation of TM waveband reflectance factor data is presented which produces features analogous to TM Tasseled Cap brightness, greenness, and wetness. The approach to adjusting the transformation matrix to other types of reflectance factor data (different instrument or band response) is described in general terms.
|
| [13] |
Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements——An exploration with the simplified simple biosphere model [J]. |
| [14] |
Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data [J].https://doi.org/10.1109/TGRS.2008.2007125 URL [本文引用: 1] 摘要
at the sensor overpass time. The comparison with this ldquoground-truthrdquo map provided an rmse of 1.5 K.
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| [15] |
A new index for delineating built-up land features in satellite imagery [J].https://doi.org/10.1080/01431160802039957 URL [本文引用: 1] 摘要
A new index derived from existing indices – an index‐based built‐up index (IBI) – is proposed for the rapid extraction of built‐up land features in satellite imagery. The IBI is distinguished from conventional indices by its first‐time use of thematic index‐derived bands to construct an index rather than by using original image bands. The three thematic indices used in constructing the IBI are the soil adjusted vegetation index (SAVI), the modified normalized difference water index (MNDWI) and the normalized difference built‐up index (NDBI). Respectively, these represent the three major urban components of vegetation, water and built‐up land. The new index has been verified using the Landsat ETM+ image of Fuzhou City in southeastern China. The result shows that the IBI can significantly enhance the built‐up land feature while effectively suppressing background noise. A statistical analysis indicates that the IBI possesses a positive correlation with land surface temperature, but negative correlations with the NDVI and the MNDWI.
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| [16] |
Development and evaluation of a spatially-explicit index of Chesapeake Bay health [J].https://doi.org/10.1016/j.marpolbul.2008.11.018 URL PMID: 19117579 [本文引用: 1] 摘要
In an effort to better portray changing health conditions in Chesapeake Bay and support restoration efforts, a Bay Health Index (BHI) was developed to assess the ecological effects of nutrient and sediment loading on 15 regions of the estuary. Three water quality and three biological measures were combined to formulate the BHI. Water quality measures of chlorophyll-a, dissolved oxygen, and Secchi depth were averaged to create the Water Quality Index (WQI), and biological measures of the phytoplankton and benthic indices of biotic integrity (P-IBI and B-IBI, respectively) and the area of submerged aquatic vegetation (SAV) were averaged to create the Biotic Index (BI). The WQI and BI were subsequently averaged to give a BHI value representing ecological conditions over the growing season (i.e., March-October). Lower chlorophyll-a concentrations, higher dissolved oxygen concentrations, deeper Secchi depths, higher phytoplankton and benthic indices relative to ecological health-based thresholds, and more extensive SAV area relative to restoration goal areas, characterized the least-impaired regions. The WQI, P-IBI and BHI were significantly correlated with (1) regional river flow (r=-0.64, -0.57 and -0.49, respectively; p<0.01), (2) nitrogen (N), phosphorus (P) and sediment loads (all positively correlated with flow), and (3) the sum of developed and agricultural land use (highest annual r(2)=0.86, 0.71 and 0.68, respectively) in most reporting regions, indicating that the BHI is strongly regulated by nutrient and sediment loads from these land uses. The BHI uses ecological health-based thresholds that give an accurate representation of the health conditions in Chesapeake Bay and was the basis for an annual, publicly released environmental report card that debuted in 2007.
|
| [17] |
Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors [J]. |
| [18] |
Image-based atmospheric corrections—Revisited and revised [J].https://doi.org/10.1016/0031-0182(96)00019-3 URL [本文引用: 1] 摘要
ABSTRACT A major benefit of multitemporal, remotely sensed images is their applicability to change detection over time.(...) However, to maximize the usefulness of data from multitemporal point of view, an easy-to-use, cost-efective, and accurate radiometric calibration and correction procedure is needed.
|
| [19] |
基于影像的Landsat TM/ETM+数据正规化技术 [J].https://doi.org/10.3321/j.issn:1671-8860.2007.01.016 URL Magsci [本文引用: 1] 摘要
阐述了基于影像的Landsat TM/ETM+的数据正规化技术及其发展。该技术通过将Landsat影像的亮度值转换成传感器处的辐射值和反射率来对影像进行辐射校正。实例表明,使用正规化技术处理后的影像可以明显削弱日照和大气的影响,去除它们产生的噪声;其所求的传感器处的反射率与地面实测反射率的RMS值非常小。
|
| [20] |
厦门市植被时空演变的遥感动态监测[D] . |
| [21] |
厦门市城市空间动态变化的遥感研究[D] . |
| [22] |
基于城市地表参数变化的城市热岛效应分析 [J].
以不透水面、植被、水体为代表 的地表参数的变化决定了城市的热环境质量。针对福州从一个非"火炉"城市一跃成为中国新三大"火炉"之首,对福州市1976—2006年间的地表参数变化 及其对城市热环境的影响进行研究。通过Landsat卫星影像反演了福州市1976、1986、1996、2006年的不透水面、植被、水体、地面温度等 主要地表参数,并对其进行空间叠加分析和相关关系的定量分析。研究发现:不透水面对地面温度的影响可接近或超过植被和水体之和,查明了福州城市主要地表参 数在这30 a里发生的变化及其对城市热环境的影响。总的看来,城市地表不透水面斑块的增加和集聚、植被和水体面积的减少和破碎,以及通风不畅,是造成福州成为"火 炉"城市的主要因素。
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