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Journal of Geo-information Science >

2017 , Vol. 19 >Issue 7: 950 - 961

DOI: https://doi.org/10.3724/SP.J.1047.2017.00950

Orginal Article

Spatial Distribution of Land Surface Vegetation-Energy Relationship in Sanya Tropical Rain Forest Regions

  • YAO Wutao , 1, 2 ,
  • GUAN Yanning 1 ,
  • GUO Shan , 1, * ,
  • CAI Danlu 1 ,
  • XIAO Han 1, 2 ,
  • ZHANG Chunyan 1
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  • 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
*Corresponding author: GUO Shan, E-mail:

Received date: 2017-04-18

  Request revised date: 2017-05-25

  Online published: 2017-07-10

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Abstract

Land surface energy information of remote sensing describes the ecological process of regional ecosystem elements. The distribution and variation trends of land surface energy reflect structure and quality of regional ecosystem element. This study is based on the theory of ecology and aims to provide a scientific basis of preservation and restoration of forests in decision-making, prediction, implementation, verification and other aspects. In this study, we extracted the information about the comprehensive responses and interactive relationship between tropical rain forest and land surface energy in Sanya, using classes of vegetation greenness, land surface energy and the vegetation-energy relationship index to evaluate the quality of forest ecosystem. Vertical and horizontal distributions of tropical rain forest of 30 years (1987-2016) were used to discuss a change of spatial-temporal zonality. The following results are noted: (1) With around 90% of vegetation coverage in the past 30 years, classes of vegetation greenness are mainly composed of high and medium values, and has an increasing trend. (2) The low vegetation greenness and high land surface energy shifts to high vegetation greenness and low land surface energy from coastal area to mountain area. (3) The fluctuation of land surface energy distribution at all levels was less than 10%. Regions with medium energy expanded to low energy areas. (4) Tropical rain forest of high vegetation greenness increases with elevation increasing associated with land surface energy decreasing. (5) The ecological quality of the planted vegetation regions below 200 meters height, declined faster than that of planted vegetation regions above 400 meters height. Compared with planted vegetation regions, tropical rain forest regions have high spatial-temporally stability in both surface energy and vegetation greenness. In general, comprehensive response characteristics of remote sensing and their interactive relationship provide quantitative basis for evaluating the tropical rain forest ecosystems.

Key words： tropical rain forest; remote sensing; NDVI; land surface energy; ecosystems

Cite this article

YAO Wutao , GUAN Yanning , GUO Shan , CAI Danlu , XIAO Han , ZHANG Chunyan . Spatial Distribution of Land Surface Vegetation-Energy Relationship in Sanya Tropical Rain Forest Regions[J]. Journal of Geo-information Science, 2017 , 19(7) : 950 -961 . DOI: 10.3724/SP.J.1047.2017.00950

1 引言

热带雨林是地球陆地上物种最丰富、结构最复杂的森林生态系统,在维持全球碳氧平衡、调节气候、保护生物多样性等方面具有重要作用[1-2]。自20世纪开始,世界人口快速增长,城市数目与面积不断扩大,工、农业的发展及污染加剧等,导致雨林面积不断减少,现存雨林生态系统也越来越多地受到人为扰动的影响[3-6]。海南岛是中国热带雨林的主要分布地区之一,据统计,海南岛在西汉前森林覆盖率高达90%,明清以来开始急剧下降,至1956年,岛内天然林覆盖率已降至25.5%,1999年仅为4%,人口增长、森林采伐、橡胶种植、热带农业开发等,均成为海南岛近百年来森林变迁的重要原因[7]。三亚市位于海南岛最南端,是中国著名热带滨海旅游城市,同时是中国生态最为优良、环境品质最具国际竞争力的城市之一[8]。三亚地区地带性植被为热带山地雨林和热带季雨林,在历经发展橡胶、多次体制变更以及刀耕火种、烧山打猎等人为持续扰动之后,三亚天然植被环境遭受严重破坏。虽然从20世纪80年代开始,三亚已经对林业进行恢复和发展,但是现状仍不容乐观[9]。因此,针对热带雨林等天然植被区域生态系统作用与影响关系的研究,已经成为众多学者们关注的焦点。
学者们分别从不同角度对生态系统质量和变化进行分析讨论。① 从区域生态系统角度, Zhang等[10]利用不透水面、地表温度等5个遥感信息参数,建立了针对城市及周边生态质量和环境变化的监测模型,分析得到珠三角地区1998年到2008年生态质量高等级面积缩小、生态质量低等级面积增加的结论;Zhang等[11]以土地利用类型、城市空间格局、环境特征等因素作为影响因子,建立城市生态环境质量综合评价指标体系,分析认为北京市城市综合生态质量处于中等水平,水资源短缺、城市绿地萎缩是导致生态质量下降的负性因素。② 从城市生态系统角度,关燕宁等[12]利用遥感热红外数据,建立了定量化的城市相对地表能量分级与评价指标,分析认为城市实体空间的组团方式、空间配置、实体与开放空间的组合方式及比例等是影响城市地表能量分布的重要原因;王蕾等[13]提取重庆市地表要素的地表能量特征,认为地表要素中实体空间和开敞空间空间布局的合理性与稳定性是影响城市热平衡的重要因素。③ 从森林生态系统角度,学者们多利用生物学、景观生态学的方法评价森林的质量和变化情况[14-15]。Keddy等[16]选取动植物群落构成、森林面积等10个参数构建评价体系,分析得到北美东部阔叶林生态破坏程度逐渐升高的结论;吴志丰等[17]应用空间直观景观模型(LANDIS),从景观水平和年龄类型水平上,对大兴安岭呼中区森林进行模拟分析,得到该地区景观斑块和破碎化程度均偏离历史变域的结论。
现有研究鲜有从植被指数、地表能量、植被-能量关系多个方面对雨林-人工植被-城市复合生态系统进行综合分析。为此,本文提取三亚研究区的植被指数特征、地表能量特征以及植被-能量关系特征,结合雨林垂直分带和植被分布信息,探讨近30年（1987-2016年）不同时期热带雨林环境的水平地带性、垂直地带性及其不同时间序列的时空变化特征。

2 研究区概况和数据源

2.1 研究区概况

三亚市（18°9′34″~18°37′27″N, 108°56′30″~109°48′28″E）辖区地貌以北部山地、东南部丘陵和西南部海积平原为主[18],境内最高山峰为尖岭（1099 m）,属热带海洋性季风气候,年平均气温25.8 °C,气温最高月为6月,平均气温为28.8 °C;气温最低月为1月,平均气温为21.6°C,年平均降水量1392.2 mm。三亚因气候温暖湿润,素有“天然温室”之称[19]。近年来,由于城市扩展,三亚热带雨林等天然植被生境受到较为严重的破坏,主要体现为橡胶林、果树林大面积增加,天然林面积不断减少等。

2.2 数据源

本文使用的基础遥感数据为美国地质调查局（USGS）陆地资源卫星Landsat 5 TM /Landsat 8 OLI采集的系列遥感数据,共选取1987、1994、2001、2008、2016年5期干季数据用于动态变化分析;采用的数字高程数据（DEM）来源于美国国家航空航天局提供的ASTER GDEM数据,空间分辨率为30 m;高分辨率遥感数据为Quick Bird采集的亚米级数据,用于验证研究区内地物的类型及其在不同时间段的变化情况;森林类型数据为中国城市规划设计研究院提供的三亚市森林资源分布图（2008年12月）。

2.3 技术路线

图1为研究方案的实施流程,主要分为3个部分：①遥感数据、数字高程模型（DEM）和森林植被信息的预处理,包括遥感数据的传感器定标、基于直方图匹配的相对辐射校正、几何校正;利用DEM高程数据计算坡度数据;对三亚市森林资源分布图进行数字化处理,并与TM数据进行配准。②遥感信息提取、数据分级处理与分析,包括植被指数、地表能量信息的提取与分级;高程、坡度分带及植被类型分类;植被-能量的三维密度分析和相关性分析。③基于三亚植被环境的分析和讨论。
Fig. 1 Research framework and technical processes

图1 研究框架与技术流程

3 评价指标与研究方法

3.1 植被指数分级

植被指数采用归一化差分植被指数(NDVI)。NDVI是反映植被状况的一个重要遥感参数,能够体现植物生长状态和植被类型空间分布状态,是植被冠层有机体和多样性水平的度量[12]。Cai等[20-21]提出的NDVI分级方法,在区分草地与灌木、过渡林,以及温、热带雨林时具有较高精度。本文以0.1为数值间隔对NDVI的-0.3至0.7区间进行分级,低于-0.3、高于0.7部分各自分为一级,各级别可归为4类：0.1以下为非植被;0.1-0.4为低植被指数; 0.4-0.6为中植被指数;0.6以上为高植被指数。

3.2 相对地表能量分级

本文地表能量为传感器处的温度值,即亮度温度,通过反演Landsat TM/OLI数据的热红外波段获得。热红外波段求亮度温度需经过2个步骤：①根据定标系数（增益、偏移）把DN值转化为相应的热辐射强度值;②再由热辐射强度值推算得到对应的亮度温度值。热红外信息经过上述处理后得到的绝对地表能量,虽然是地物辐射温度的表征,但直接使用却存在一定的局限性[12]。为了获取体现城市及其周边生态系统各要素特征及相互关系的相对地表能量信息,要对绝对地表能量进行分级处理。根据实验的研究区域面积大小及地形地貌和气候特征,综合考虑地表要素的典型性和传感器能够区分的最低辐射分辨率[22],得到最终的分级方法：对原始数据做去云处理,并舍弃各占像元总数量1%的极高能量像元和极低能量像元,得到的新的能量峰值和谷值;以新的峰、谷值为阈值上下限,对三亚研究区地表能量进行等阈“4区16级”分级,1-4级为低能量区,5-8级为中能量区,9-12级为中高能量区,13-16级为高能量区。

3.3 DEM数据分级

DEM数据分级包括高程带分级和坡度带分级。海南岛热带雨林分布广泛,类型众多,地形地貌、气温、降水、日照[23]等多种因素决定了雨林的空间分布特征,因此在不同高程区间主要分布的雨林类型有所差异[24]。本文根据三亚植被在不同高程区间的分布差异,把研究区高程划分为5个区域。其中,20 m山基线以下是城市和农田植被的主要分布区,20~200 m高程区间主要以人工林覆盖为主,200~400 m是季雨林、人工林的混合分布区域,400 m以上以雨林等天然林类型分布为主,用800 m高程间隔将其划分为两部分,以对比讨论雨林类型的植被指数和地表能量在垂直空间上的差异（表1、 图2（a））。
Tab. 1 Description of elevation zoning

表1 高程分带说明

高程/m 类型 描述 面积/km2
<20 山前平原 农田植被、人工林等 337.6
20~200 人工林 人工林、果园等 848.3
200~400 季雨林 热带常绿季雨林、其他类型天然林、人工林等 458.1
400~800 低山雨林 热带低山雨林、其他类型天然林等 289.4
>800 低山与中山雨林 热带低山和中山雨林、其他类型天然林等 14.1
Fig. 2 Geographical setting of elevation, slope and vegetation types in Sanya

图2 三亚高程、坡度、和植被类型划分示意图

不同坡度下光、热、水、土等自然因素的差异,形成了不同的植被空间分布格局[25-26]。依据国际地理学联合会地貌调查与地貌制图委员会关于地貌详图应用的坡地分类情况,并结合三亚研究区的地形地貌和植被覆盖特征,把三亚坡度划分为5级[27]（表2、图2（b））：即平坡（0°~5°）、缓坡（5°~10°）,缓斜坡（10°~15°）、斜坡（15°~25°）、陡坡（25°以上）。
Tab. 2 Description of slope ranges

表2 坡度分级说明

坡度/° 命名
0~5 平坡
5~10 缓坡
10~15 缓斜坡
15~20 斜坡
>25 陡坡

3.4 植被类型分类

以三亚市森林资源分布图的植被原生性为依据,实施植被类型划分。将三亚市植被划分为天然林和人工林两个大类,不同类型的热带雨林包含在天然林中,人工林包括果树林、橡胶林、桉树林3个小类（图2(c)）。

3.5 植被-能量关系建立

（1）利用R语言的kde2dplot函数,建立基于像元分布的植被指数-地表能量关系的等密度分析 图[20]。以基础像元为计算单元,地表能量值为X轴,NDVI值为Y轴,根据各期数据的地表能量和NDVI阈值范围,定义X轴地表能量区间为[280 K,305 K],Y轴NDVI区间为[0.1,0.8],图中灰色圈线为根据三维核密度函数计算的等值线,数值越高,代表分布密度越大。
（2）计算不同高程带和坡度带的像元地表能量与NDVI的线性回归方程 y=ax+b和二者的线性相关系数r。回归方程中a、b分别表示回归系数和截距,回归系数越大表示 x 对 y 影响越大,a>0, x 、 y 有正相关关系,a<0, x 、 y 有负相关关系。相关系数r计算公式如下：
r = ∑ i = 1 n ( x i - x ̅ ) ( y i - y ̅ ) ∑ i = 1 n ( x i - x ̅ ) 2 ∙ ∑ i = 1 n ( y i - y ̅ ) 2 （1）
式中： x i 和 y i 分别是变量 x 和 y 的样本观测值; x ̅ 和 y ̅ 分别是变量 x 和 y 样本值的平均值; n 为样本数量;r代表相关性,∣r∣越高,相关性越强。

4 结果与分析

4.1 植被指数特征

（1）分级构成
研究区植被指数保持以中、高植被指数规模分布为主的构成（0.4以上）,各期植被指数0.4以上的植被分布比例均在55%以上,非植被比例稳定在10%。2016年植被指数整体最高,植被指数分级数量由6个扩展至7个,出现超过15%比例的0.7以上极高植被指数分布,中植被指数以上的植被分布比例超过70%（图3-4）。
Fig. 3 Statistics of NDVI classes in Sanya

图3 三亚植被指数构成分布

Fig. 4 NDVI changes in Sanya

图4 三亚植被指数变化

（2）分布状态
研究区自滨海向山地植被指数呈现数值增高型分布（图5）。非植被区（0.1以下）集中在山前平原的人居环境;西部山区、东北地区及城市边缘植被指数相对较低;高植被指数主要分布在北部、中部、东部和西南沿海的山地;其余地区以中植被指数分布为主。1994年研究区整体植被指数在各期数据中最低,热带雨林环境的植被指数处于0.4-0.6之间,人工植被环境的植被指数大部分低于0.3;2016年研究区整体植被指数最高,热带雨林环境的植被指数达到0.6以上,人工植被环境的植被指数在0.2-0.6之间波动。
Fig. 5 Geographical setting of NDVI in Sanya

图5 三亚植被指数空间分布特征

（3）变化趋势
研究区植被指数整体数值增高趋势明显。低植被指数分布在1994年后呈现幅度逐渐减小的降低趋势;中植被指数在2008年以前所占比例超过50%,在4个级别中占据比例最高,波动幅度小于5%,2016年中植被指数比例下降30%,是因为更高级别植被指数分布的出现和增长;高植被指数呈现先降后升的变化,波动幅度和频率改变明显,2016年的增长比例最高,增幅超过30%,其中0.7以上的极高植被占17%（图3-5）。

4.2 地表能量特征

（1）分级构成
研究区的地表能量构成较为稳定,整体保持以中、中高能量为主的构成格局（5-12级）,相邻两期数据之间各级别的波动幅度低于10%（图6）。
Fig. 6 Land surface energy distribution in Sanya

图6 三亚地表能量构成分布

（2）分布状态
研究区从滨海向山地呈现地表能量逐级降低趋势,在海拔升至400 m时呈现阶跃性改变（图8）。山前平原的人居环境和部分低海拔山地人工植被为高能量级别集中分布区,西北、中部的平缓山地人工植被覆盖区为中能量主要分布区,北部、东部等高海拔山地是低能量唯一分布区。1994年,研究区整体能量相对较低,中能量人工植被环境地表能量等级分布在6-10级区间,低能量热带雨林环境地表能量等级分布在1-6级;2016年研究区地表能量整体趋高,人工植被环境地表能量等级分布区间提升至8-13级,热带雨林植被环境地表能量仍保持在6级以下。
Fig. 8 Geographical setting of land surface energy in Sanya

图8 三亚地表能量空间分布特征

（3）变化趋势
研究区地表能量呈现平缓和小幅度整体增高趋势（图7）。其中,低地表能量和高地表能量的分布比例均维持在10%以下,低地表能量的分布比例略高于高地表能量;中地表能量在各级中比例最高,除1987年外,呈现降低趋势;中高地表能量比例呈增高趋势,与中地表能量级别差距逐渐缩小。山前平原地区高地表能量级别斑块数目增多,破碎程度提高;西部山地地区、东北部丘陵地区和西北部水库两侧等多处地区,地表能量级别由中地表能量提高为中高地表能量,是导致研究区中高地表能量级别构成比例提升的主要原因;东部、中部山地的低能量斑块边缘萎缩,面积减小,整体地表能量级别提升,仅北部山区地表能量级别稳定在5级以下。
Fig. 7 Land surface energy change in Sanya

图7 三亚地表能量变化

4.3 植被-能量关系特征

4.3.1 高程变化特征
研究区植被指数和地表能量存在明显的垂直地带性特征。同期数据中（图9）,随高程带海拔升高,植被指数分布区间变窄,整体向植被指数高值方向移动至季雨林带,变化态势趋于平缓;像元地表能量分布区间随海拔升高,整体向低能量方向偏移。2016年相对于1987年,各高程带植被指数分布区间上限均提升0.1左右,地表能量分布区间变窄,相邻高程带地表能量峰值的差异减小。
Fig. 9 Density analysis in (NDVI,T)-diagram associated with different elevation zones in Sanya

图9 三亚高程带植被-能量三维密度分析

（1）山前平原（0~20 m）。2016年较1987年,高植被指数-高地表能量区域像元密度明显增加,高、低植被指数区域的地表能量值达到同一水平（图9（a）、图9（e））。表明该高程带中、高植被指数分布区受人为扰动影响加剧,地表能量级别不断提升。
（2）人工林（20~200 m）。2016年植被指数0.5附近出现低峰（图9（f））,是部分地区植被质量下降的表现,高、低植被指数区域的能量差异减小,但变化程度低于20 m以下区域。
（3）季雨林（200~400 m）。相对于海拔200 m以下范围,此高程带植被指数分布区间下限提升幅度最高,峰值走向亦发生改变,形成从植被指数、地表能量较高区域至二者较低区域的长条形状（图9（c）、（g））。综合三亚植被类型分布（图2（c））和高程带划分（图2（a））,海拔200 m以上区域雨林覆盖面积比例逐渐提升,是造成植被指数变化、峰值走向差异的主要原因。峰值区向较低植被指数、较高地表能量方向有小范围延伸,但分布比例较低,2016年较1987年变化不大,说明此高程区间存在少面积的人工植被,且在研究时间范围内面积增长幅度较小。
（4）低山、中山雨林（400~800 m、800 m以上）。两个高程带植被指数分布区间基本保持一致,未出现向高能量、低植被指数方向的延伸趋势（图9（d）、（h）、（i）、（j））,整体分布态势相对稳定。说明这两个高程带植被原生性保持较好。
各高程带中,20~200 m范围的人工林主要分布区∣r∣稳定在0.4以上,a小于0（表3）,说明人工林的植被指数和地表能量值存在弱负相关关系,其余高程带∣r∣多低于0.3,波动幅度大,并非典型的线性相关关系。
Tab. 3 Correlation analysis of the NDVI-T relationship associated with different elevation zones in Sanya

表3 三亚高程带植被-能量相关性分析

高程/m 1987年 1994年 2001年 2008年 2016年
∣r∣ a b ∣r∣ a b ∣r∣ a b ∣r∣ a b ∣r∣ a b
<20 0.30 -4.26 293.77 0.32 -4.74 297.90 0.32 -2.97 296.04 0.27 -2.55 296.80 0.12 -1.44 296.16
20~200 0.58 -8.72 294.42 0.52 -6.58 297.81 0.54 -5.97 296.72 0.41 -5.03 296.53 0.40 -3.54 297.05
200~400 0.17 -2.84 290.03 0.03 -0.35 294.17 0.22 -2.74 294.04 0.07 -1.00 292.86 0.26 -2.13 295.30
400~800 0.11 1.90 285.50 0.06 0.88 292.66 0.12 1.88 290.00 0.15 2.53 289.17 0.37 2.93 290.80
>800 0.18 3.14 282.51 0.16 2.20 290.60 0.36 5.32 286.32 0.28 4.65 286.49 0.75 4.85 288.40
4.3.2 坡度变化特征
同期数据中（图10）,随坡度增大,植被指数分布区间上限稳定,下限有上升趋势;地表能量分布区间随坡度增大,整体向低地表能量方向移动,但相邻坡度区之间,地表能量降低幅度小于相邻高程带。与高程带相似,2016年相对于1987年,各坡度区植被指数上限提高0.1左右,地表能量分布区间变窄。1987年坡度10°以上区域植被指数分布区间下限基本一致,而2016年随坡度区增大,植被指数分布区间下限始终保持升高趋势。
Fig. 10 Density analysis in (NDVI,T)-diagram associated with different slope ranges in Sanya

图10 三亚坡度带植被-能量三维密度分析

（1）平坡（0~5°）。高、低植被指数区域地表能量差异减小（图10（a）、（e））,中、高植被指数的植被受人为扰动影响程度提高。
（2）缓坡（5~10°）。植被指数0.5附近形成第二峰值（图10（f））,表明该坡度区内部分地区植被质量降低,植被指数下降,地表能量升高。
（3）缓斜坡、斜坡（10~15°、15~25°）。1987年,植被指数分布区间下限提升至0.3左右,与陡坡的植被指数分布区间基本一致（图10（i））,峰值重心稳定处于高植被指数、低地表能量位置（图10（c）、（d）;而2016年,缓斜坡、斜坡植被指数分布区间下限低于陡坡0.2左右（图10（g）、（h）、（j））。说明坡度10~25°范围内的植被在1987年至2016年受破坏程度较高,部分地区植被指数有下降趋势。
（4）陡坡（25°以上）。2016年较1987年分布形态基本不变,峰值走向与前四个坡度区不同（图10（i）、（j））,与400 m以上高程区域的分布形状相似,说明此坡度区以雨林等天然植被覆盖为主,受人为扰动影响程度最低,植被原生性保持较好。
15°以下的平坡区、缓坡区和缓斜坡区∣r∣基本在0.4以上（表4）,植被指数与地表能量表现为较弱的线性负相关关系,这与20~200 m高程带特征相似,说明研究区坡度15°以下以人工林分布范围较广。
Tab. 4 Correlation analysis of the NDVI-T relationship associated with different slope ranges in Sanya

表4 三亚坡度带植被-能量相关性分析

坡度/° 1987年 1994年 2001年 2008年 2016年
∣r∣ a b ∣r∣ a b ∣r∣ a b ∣r∣ a b ∣r∣ a b
0~5 0.64 -9.35 294.85 0.65 -8.60 298.55 0.56 -5.77 296.69 0.53 -6.60 297.47 0.27 -2.64 296.65
5~10 0.65 -10.80 294.99 0.64 -8.73 298.37 0.61 -7.56 297.13 0.53 -7.83 297.37 0.41 -3.90 297.01
10~15 0.44 -9.14 293.69 0.42 -6.71 297.31 0.50 -7.73 296.90 0.38 -7.12 296.58 0.34 -3.59 296.45
15~20 0.17 -4.01 290.47 0.13 -2.31 295.08 0.26 -4.64 294.93 0.16 -3.43 294.21 0.12 -1.32 294.66
>25 0.16 3.32 285.94 0.21 3.48 292.06 0.18 3.21 290.19 0.21 4.58 289.23 0.29 3.14 291.36

5 结论

（1）近30年三亚市域植被覆盖比例维持在90%左右,植被指数分级构成以高、中数值分布为主,并呈现数值整体趋高态势。热带雨林植被区的高植被指数分布比例高于人工植被区,部分人工林植被指数呈现波动或降低趋势,农田植被的植被指数波动幅度较大。在水平地带性上,靠近人居环境的植被,其植被指数整体数值较低,增长幅度小,波动程度较高;在垂直地带性上,高海拔、大坡度地带植被指数整体增长幅度高于低海拔、小坡度地带。
（2）各级地表能量分布比例的波动幅度在10%之内,中等地表能量级别范围呈现向低地表能量区域扩展趋势。中、高能量过渡区附近,城市扩展对于植被环境影响明显,具体体现在城市自身高地表能量斑块分布更为集中,且边缘逐渐向周边地区扩展。人工-天然植被交界线向高海拔、大坡度方向移动,中地表能量过渡区范围向低地表能量区延伸。山地人工植被集中分布区地表能量整体提升,形成的较高能量斑块与低能量区之间表现为明显的阶跃性变化。
（3）随着海拔高度的提升,植被指数高数值的热带雨林分布比例增加,且地表能量值降低。三亚热带雨林主要分布在海拔200 m、坡度15°以上,植被指数和地表能量随高程增高而变化的幅度低于高程200 m、坡度15°以下地区。研究时间范围内,植被破坏最严重的区域集中在高程20~200 m、坡度5~10°地区,其次为高程200~400 m和坡度10~25°地区。
（4）热带雨林的地表能量和植被指数的时空分布稳定性均高于人工植被。在各期数据中,热带雨林覆盖区均属于研究区植被指数最高、地表能量最低的区域,其高植被指数-低地表能量特征保持不变;人工植被环境的植被指数与地表能量之间的关系特征存在较大的随机性,二者在同期数据中的分布区间宽度、在不同期数据中的波动幅度,均高于热带雨林。
物种多样性架构的热带雨林植被环境与相对单一的人工植被环境对比分析表明,因分布格局、结构及其物候期等方面的不同,导致二者在植被指数和地表能量数值及其时效性上存在明显差异,即热带雨林植被稳定的低地表能量-高植被指数特征优于人工植被波动的中、高地表能量-中、低植被指数特征。
植被指数、地表能量和植被-能量关系指标分析,为研究快速城市化和人为扰动对三亚区域雨林等植被生态系统的破坏程度,以及更具目标性的植被保护和恢复提供科学依据。

The authors have declared that no competing interests exist.

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王蕾,关燕宁,郭杉,等.城市地表要素的地表能量响应特征及其关系研究[J].地球信息科学学报,2016,18(12):1684-1697.遥感地表能量信息可揭示城市地表要素的地表能量综合响应特征和作用关系特征,客观地反映了城市实体空间、开敞空间及其开放空间网络的格局和变化。本文从重庆城市地表能量响应的基本网格单元和研究区尺度,分析城市地表要素对城市热环境的贡献,并结合与＂国际宜居城市＂——西雅图的对比,探讨城市化过程中地表要素类型改变对城市热环境的影响及其变化和规律。结果表明：1在基本网格单元和区域尺度层面,城市地表能量的平衡取决于地表要素中实体空间和开敞空间布局的合理性与稳定性;2在城市大规模的硬质化区域,建筑实体的垂直体量相对于其水平体量及其组团格局,对于地表能量的聚集、改变具有较高的敏感性;3地表要素对城市热环境的影响若达到同等贡献指数（绝对值）程度,基本网格单元的开敞空间比例需要高于实体空间;4开敞空间基本网格单元的林地和水体类型所占比例达到20%时,地表能量的减幅明显;5实体空间基本网格单元的在建/工业用地要素类型所占比例超过5%,以及高密度建设用地所占比例达到30%时,地表能量的增幅明显。本研究旨在从城市地物实体地表能量的体量与空间关系角度,为建设基于城市更新的城市规划和城市设计提供科学依据。

DOI

[ Wang L, Guan Y N, Guo S, et al.Urban surface energy’s responses to land surface element types and interactive relationship[J]. Journal of Geo-information Science, 2016,18(12):1684-1697. ]

[14]
Ochoa-Gaona S, Kampichler C, de Jong B H J, et al. A multi-criterion index for the evaluation of local tropical forest conditions in mexico[J]. Forest Ecology and Management, 2010,260(5): 618-627.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 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.

DOI

[15]
徐丽. 森林类自然保护区生态质量评价研究[D].武汉:华中农业大学,2014.

[ Xu L.Study on ecological quality assessment of forest nature reserve:a case of Dinghushan nature reserve[D]. Wuhan: Huazhong Agricultural University, 2014. ]

[16]
Keddy P A, Drummond C G.Ecological properties for the evaluation, management, and restoration of temperate deciduous forest ecosystems[J]. Ecological Applications, 1996,6(3):748-762.Given that many of the original deciduous forests of North America have disappeared over the last few centuries, our challenge is to preserve remnant forests, restore altered forests, and harvest managed forests in a sustainable manner. To do so, we need to identify macroscale properties that can easily monitor the condition of the eastern deciduous forest as a whole. We offer 10 possible properties: (1) tree size; (2) canopy composition; (3) quantity and quality of coarse woody debris; (4) number of spring ephemeral species in the herbaceous layer; (5) number of typical corticulous bryophyte species; (6) density of wildlife trees; (7) fungi; (8) avian community; (9) number of large carnivores; and (10) forest area. We have assigned to each property a control (or normal) value, an intermediate value, and a heavily altered value. These values are based on the existing literature. These 10 properties would: (1) allow us to recognize, rank, and protect high-priority forest sites for conservation; (2) tell us whether changes in a forest are in the direction of restoration or toward further alteration; and (3) enable us to evaluate different harvesting methods so we can select those that cause the least alteration to forests.

DOI

[17]
吴志丰,李月辉,布仁仓,等.呼中林区森林景观的历史变域模拟及评价[J].生态学报,2013,(15):4799-4807.After randomizing the input information of age and distribution of forest species we simulated the long-term (2500 years) dynamic of forest landscape pattern of huzhong area in Great Xing'an Mountains using spatially explicit landscape model (LANDIS), Then we took the period when simulation results reached stable as the time range to estimate the historical range of variability (HRV). Furthermore, we analyzed the relationship between HRV and the forest landscape pattern of 1990 and 2000 using principal component analysis and kernel density estimation methods. The simulation results showed that the succession of almost all forest species arrived to their stable status at around 900th year of the simulation, and the period afterwards was utilized to estimate the HRV; as to the characteristics of forest, landscape pattern in 1990 fell out of the HRV in area of patches and the degree of landscape fragmentation, especially mature and over-mature forest deviated significantly from HRV in patch area and fragmentation; though forest harvest management was better planned after 1990, the forest landscape pattern in 2000 still deviated from the HRV.

DOI

[ Wu Z F, Li Y H, Bu R C, et al.Evaluation and simulation of historical range of variability of forest landscape pattern in Huzhong area[J]. Acta Ecologica Sinica, 2013,(15): 4799-4807.]

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中国科学院《中国自然地理》委员会.中国自然地理:地貌[M].北京:科学出版社,1980.

[ Chinese Academy of Sciences, Chinese natural geography Committee. Physical geography of China: Landforms[M]. Beijing: Science Press, 1980. ]

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黄海智,黄萍.三亚市旅游气候舒适度评价[J].气象研究与应用,2010,31(4):70-73.利用三亚30年气候资料对三亚气候舒适程度进行评价,得出三亚的最适宜旅游期和较不适宜期,为游客在三亚安排旅游活动时间及当地旅游部门做出合理的旅游方案提供参考依据。

DOI

[ Huang H Z, Huang P.Evaluation of tourism climate comfort in Sanya city[J]. Journal of Meteorological Research and Application, 2010,31(4):70-73. ]

[20]
Cai D, Fraedrich K, Sielmann F, et al.Climate and vegetation: An era-interim and GIMMS NDVI analysis[J]. Journal of Climate, 2014,27(13):5111-5118.Abstract To complement geographical presentation of remote sensing vegetation information, the authors apply Budyko's physical state space diagram to analyze functional climate relations. As an example, the authors use Interim ECMWF Re-Analysis (ERA-Interim) global weather data to provide the statistics (1982-2006) of climate states in a two-dimensional state space spanned by water demand (net radiation N) versus water/energy limitation (dryness ratio D of net radiation over precipitation). Embedding remote sensing-based Global Inventory Modeling and Mapping Studies (GIMMS) data [normalized difference vegetation index (NDVI) > 0.1] shows the following results: (i) A bimodal frequency distribution of unit areas (pixels) is aligned near D similar to 1 but separated meridionally, associated with higher and lower net radiation. (ii) Vegetation states are represented as (N, D, NDVI) triplets that reveal temperate and tropical forests crossing the border (D similar to 1) separating energy- and water-limited climates but unexpectedly show that they also exist in marginal regions (few pixels) of large dryness. (iii) Interannual variability of dryness is lowest where the largest climate mean NDVI values of greenness (forests) occur. The authors conclude that the combined (N, D, NDVI) analysis based on climate means has shown that tropical and temperate forests (NDVI > 0.6) are (i) not restricted to the energy-limited domain D < 1 (extending into the water-limited surface climate regime) and (ii) associated with low interannual variability of dryness. Thus, measures of interannual variability may be included in Budyko's classical framework of geobotanic analysis of surface climates.

DOI

[21]
Cai D, Fraedrich K, Sielmann F, et al.Vegetation dynamics on the tibetan plateau (1982-2006): An attribution by ecohydrological diagnostics[J]. Journal of Climate, 2015,28(11):4576-4584.Vegetation greenness distributions [based on remote sensing normalized difference vegetation index (NDVI)] and their change are analyzed as functional vegetation-climate relations in a two-dimensional ecohydrological state space spanned by surface flux ratios of energy excess (U; loss by sensible heat H over supply by net radiation N) versus water excess (W; loss by discharge Ro over gain by precipitation P). An ecohydrological ansatz attributes state change trajectories in (U, W) space to external (or climate) and internal (or anthropogenic) causes jointly with vegetation greenness interpreted as an active tracer. Selecting the Tibetan Plateau with its complex topographic, climate, and vegetation conditions as target area, ERA-Interim weather data link geographic and (U, W) state space, into which local remote sensing Global Inventory Modeling and Mapping Studies (GIMMS) data (NDVI) are embedded; a first and second period (1982-93 and 1994-2006) are chosen for change attribution analysis. The study revealed the following results: 1) State space statistics are characterized by a bimodal distribution with two distinct geobotanic regimes (semidesert and steppe) of low and moderate vegetation greenness separated by gaps at aridity D ~ 2 (net radiation over precipitation) and greenness NDVI ~ 0.3. 2) Changes between the first and second period are attributed to external (about 70%) and internal (30%) processes. 3) Attribution conditioned joint distributions of NDVI (and its change) show 38.2% decreasing (61.8% increasing) area cover with low (moderate) greenness while high greenness areas are slightly reduced. 4) Water surplus regions benefit most from climate change (showing vegetation greenness growth) while the energy surplus change is ambiguous, because ecohydrological diagnostics attributes high mountainous regions (such as the Himalayas) as internal without considering the heat storage deficit due to increasing vegetation.

DOI

[22]
赵英时. 遥感应用分析原理与方法[M].北京:科学出版社,2013.

[ Zhao Y S.Principles and methods of remote sensing application[M]. Beijing: Science Press, 2013. ]

[23]
喻庆国,曹顺伟,邓喜庆,等.云南糯扎渡自然保护区植被垂直分布研究[J].林业科技开发,2006(4):47-50.植被空间分布格局受光、热、水、土等的影响表现出一定的规律性。海拔是影响光、热、水、土等 分配的因子之一，进而影响植被分布。为了探索植被沿海拔空间的分布规律，以糯扎渡自然保护区为例，按每100m为一海拔带把整个保护区划分为13个带，并 利用其数字高程模型和植被图，使用ArcView、ArcMap、ERDAS、EXCEL等软件，进行了该保护区植被沿海拔空间分布的研究。通过研究，给 出了该保护区13种植被类型的分布规律、适生海拔区间和每一海拔带的优势植被类型，并制作了按海拔带分布的三维立体植被图。该研究成果为开展本保护区的科 学研究和管理提供了科学依据。

DOI

[ Yu Q G, Cao S W, Deng X Q, et al.A study on the vertical spatial distribution of vegetation in Nuozhadu nature reserve, Yunnan[J]. China Forestry Science and Technology, 2006,4:47-50. ]

[24]
徐诗涛. 海南热带山地沟谷雨林鸟巢蕨附生特性研究[D].海口:海南大学, 2013.

[ Xu S T.Epiphytic characteristics of asplenium nidus l. (aspleniaceae) complex in tropical montane rain forest, hainan island[D]. Haikou: Hainan University, 2013. ]

[25]
潘树荣. 自然地理学(第二版)[M].北京:高等教育出版社,1985.

[ Pan S R.Physical geography (second edition)[M]. Beijing: Higher Education Press, 1985. ]

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伍光和,蔡运龙.综合自然地理学——第2版[M].北京:高等教育出版社,2004.

[ Wu G H, Cai Y L.Integrated physical geography the second edition[M]. Beijing: Higher Education Press, 2004. ]

[27]
喻庆国,曹顺伟,邓喜庆,等.云南糯扎渡自然保护区植被沿坡度空间分异研究[J].安徽农业科学,2007(28):8994-8996.为了探索糯扎渡自然保护区植被沿坡度的空间分布规律和植被分布、坡度的相关关系,基于数字地形图,使用ArcView和ERDAS软件制作了数字高程模型;基于数字高程模型,使用ArcMap软件提取了植被类型沿平坡、缓坡、斜坡、陡坡、急坡和险坡6个坡度级的分布图层和植被类型按13个海拔带沿6个坡度级分布的图层,并结合植被图制作了植被类型按6个坡度级分布的三维立体植被图。并且,用EXCEL软件统计了9个自然植被类型沿6个坡度级的分布面积和按13个海拔带沿6个坡度级的分布面积,用SPSS统计分析软件计算了坡度与植被分布的相关关系。结果表明:9个自然植被类型在6个坡度级均有分布;6个坡度级中除Ⅰ坡度级优势类型是水域外,其他坡度级的优势植被类型均是季风常绿阔叶林;坡度对季节雨林和暖温性针叶林的分布影响较大,对山地雨林、落叶季雨林和河谷稀树灌木草丛的分布有一定影响,对季风常绿阔叶林和暖热性稀树灌木草丛的分布影响较小,对暖热性针叶林和热性竹林的分布无影响。最后,确定了9个自然植被类型的适生坡度级。

DOI

[ Yu Q G, Cao S W, Deng X Q, et al.Study on the spatial distribution of vegetation along the gradients in Nuozhadu nature reserve of Yunnan Province[J]. China Forestry Science and Technology, 2007,28:8994-8996.

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