EN中文

地球信息科学学报 >

2017 , Vol. 19 >Issue 2: 273 - 280

DOI: https://doi.org/10.3724/SP.J.1047.2017.00273

遥感科学与应用技术

海岛建设引发的植被覆盖度变化的遥感分析

  • 温小乐 , 1, * ,
  • 李洋 1 ,
  • 林征峰 2
展开
  • 1. 福州大学环境与资源学院,福建省水土流失遥感监测评估与灾难防治重点实验室,福州 350108
  • 2. 福建省环境保护设计院,福州 350003

作者简介：温小乐（1976-）,女,博士,副教授,研究方向为环境资源遥感与环境评价。E-mail:

收稿日期: 2016-04-14

  要求修回日期: 2016-06-17

  网络出版日期: 2017-02-17

基金资助

福建省教育厅科技项目（JA15044）

福建省自然科学基金项目（2014J01156）

福州大学科技发展基金项目（2014-XQ-12）

收起

Remote Sensing Analysis of Fractional Vegetation Cover Change Triggered by Island Construction

  • WEN Xiaole , 1, * ,
  • LI Yang 1 ,
  • LIN Zhengfeng 2
Expand
  • 1. College of Environment and Resources, Fujian Provincial Key Laboratory of Remote Sensing ofSoil Erosion and Disaster Protection, Fuzhou University, Fuzhou 350108, China
  • 2. Fujian Environmental Protection Institute, Fuzhou 350003, China
*Corresponding author: WEN Xiaole, E-mail:

Received date: 2016-04-14

  Request revised date: 2016-06-17

  Online published: 2017-02-17

Copyright

《地球信息科学学报》编辑部 所有
Fold

摘要

海岛生态脆弱、稳定性差,大规模的海岛开发使得原本脆弱的岛上植被生境面临更大的威胁,对海岛开发中的植被覆盖变化开展分析显得尤为重要。本文基于Landsat 5卫星和Landsat 8卫星的2001、2010和2014年的遥感影像,采用Gutmand和Ignatov提出的植被覆盖度计算模型提取福建平潭岛的植被覆盖度,并结合其土地覆盖变化信息,探究平潭综合实验区建设前后植被覆盖度变化特点及其原因。研究结果表明,3个时相的平潭岛植被覆盖度达中度以上的区域面积比例分别为86.00%、58.92%和71.16%,表明研究区整体植被覆盖状况良好。动态变化分析结果显示,2001-2014年研究区植被覆盖度总体呈下降趋势,其中2001-2010年植被覆盖度下降显著,下降区域面积比例高达53.95%;而2010-2014年岛上植被覆盖状况有较大改善,植被覆盖度增加区域面积比例达47.77%,这在一定程度上弥补了之前植被覆盖度大幅下降的影响,分析原因主要得益于平潭综合实验区建立后所采取的科学规划、进一步加大植树造林、逐步完善海岛绿地系统的植被建设与保护措施。

关键词： 海岛建设; 植被覆盖度; 遥感监测; 平潭综合实验区

本文引用格式

温小乐 , 李洋 , 林征峰 . 海岛建设引发的植被覆盖度变化的遥感分析[J]. 地球信息科学学报, 2017 , 19(2) : 273 -280 . DOI: 10.3724/SP.J.1047.2017.00273

Abstract

The original ecological environment of island is fragile and lack of stability. Large-scale development and construction activities of island expose the vulnerable vegetation to a greater danger, which makes the detection and assessment of vegetation cover change especially necessary. Based on the remote sensing images of Landsat 5 and Landsat 8 in 2001, 2010 and 2014, the fractional vegetation cover (FVC) of Pingtan Island in Fujian Province was computed using the FVC calculation model proposed by Gutmand and Ignatov. Combining the FVC data with land cover change information, this study analyzed the variation in FVC of integrated experimental zone on Pingtan Island and explored its reason. The results showed that in 2001, 2010 and 2014, the middle and upper level of FVC in Pingtan Island accounted for 86.00%, 58.92%, 71.16%, respectively, which indicate that the overall vegetation in the study area is in good condition. The analysis of dynamic change showed that there is a declining trend in FVC from 2001 to 2014. The FVC decreased greatly by 53.95% from 2001 to 2010. On the contrast, the FVC rose by 47.77% from 2010 to 2014, which improved overall FVC condition substantially and offset the previously significant decrease of FVC. The comprehensive experimentation area was established in Pingtan Island. Greening projects and reasonable planning gradually improved the vegetation construction and protection of the island green land system. These reasons led to the increase of vegetation.

Key words： island construction; fractional vegetation cover; remote sensing monitoring; Pingtan comprehensive experimental zone

1 引言

海岛是典型的生态脆弱系统,随着中国沿海经济的快速发展和陆域自然资源的日益紧缺,沿海地区掀起了海岛开发的热潮。由于现阶段中国对海岛的开发利用普遍缺少规划,随意性较大,在开发利用海岛资源的同时,不重视海岛的生态保护,破坏岛上植被、滥捕滥采动植物资源等问题突出,人类开发活动对海岛生态造成的冲击影响正日益凸显,海岛生态保护已成为社会普遍关注的焦点。
平潭岛位于福建省东部海域,岛上风沙灾害严重、植被生境脆弱,虽然自20世纪60年代以来,平潭岛逐步建立了木麻黄防风固沙林体系来控制风沙,但随着滨海土地的开发,大量人为的毁林取沙以及防护林树龄老化等问题,使岛上的防护林带日益减少,平潭岛的植被状况备受关注。2010年福建省正式批准在平潭岛成立平潭综合实验区,大规模的海岛开发建设使岛上原本脆弱的植被生境面临更大的威胁。
植被是生态系统极其重要的生态因子,通常用植被覆盖度(Fractional Vegetation Cover,FVC)来表征,植被覆盖度及其变化是反映生态系统变化的重要指标。通过建立植被覆盖度计算模型来获取地表植被覆盖及其变化信息,对于揭示研究区的植被空间变化规律,探究生态系统的变化趋势具有重要的现实意义[1]。遥感技术具有观测面积广、周期性短、时效性强等特点,为大范围植被覆盖信息的快速获取提供了便利,同时利用大量连续遥感数据的综合比较,可以观测到一些依靠地面观测很难发现或需经过长期大面积调查才能发现的宏观规律,已成为对大范围植被覆盖状况进行监测的有效手段。
本研究拟基于多时相遥感影像,对平潭岛2001-2014年的植被覆盖变化及其原因进行分析,为平潭综合实验区在下一步建设中的生态保护提供科学依据。

2 研究区与研究数据

2.1 研究区概况

平潭岛地处福建省东部海域,由126个岛屿组成,其主岛为海坛岛,又名平潭岛（25°15´~25°45′ N、119°32′~120°10′ E）,面积267 km2,是福建省第一大海岛,中国第六大岛。本研究将该主岛作为研究区,当地地貌以丘陵和海积平原为主,属亚热带半湿润海洋性季风气候,降水少、风力大、蒸发强。岛上原始植被已极为罕见,现有林地多为造林绿化后的人工林,主要为木麻黄林、黑松林、台湾相思树林等,局部荒山荒坡还分布有耐旱灌丛和草本植被。

2.2 数据源及预处理

目前,在提取植被覆盖信息的各类遥感数据源中,多光谱数据应用最为广泛,由于其具有较宽的覆盖范围、较高的时间分辨率且适于进行大区域连续观测等优点,使植被覆盖度的研究多基于NOAA/AVHRR[2-3]、MODIS NDVI[4-5]及Landsat系列[6-7]等中低空间分辨率影像,但目前基于Landsat 8卫星影像的研究相对较少。相对于以往的Landsat系列卫星,Landsat 8卫星的优势在于波段数量的增加 （11个波段）和全色波段分辨率的提高（15 m）,这些变化对于植被覆盖度信息的提取更为有利[8]。
为确保遥感卫星数据源的一致性,本研究选用来自USGS网站的Landsat系列遥感影像为数据源,选取的影像为：2001-05-24和2010-05-01的Landsat 5 TM影像,以及2014-09-01的Landsat 8 OLI影像。遥感影像预处理包括几何配准和辐射校正。配准采用二次多项式和最邻近像元法,均方根误差小于0.5个像元。辐射校正则根据Chander[9-10]等和Chavez[11]的模型将影像中每个像元的DN值转换为传感器处反射率。对于2013年发射的Landsat 8卫星,依据美国USGS（http://glovis.usgs.gov/）提供的定标公式将影像的DN值转换为表观反射率。

3 研究方法

3.1 植被覆盖度反演模型的确定

目前,在植被覆盖度的遥感反演研究方法中,较常采用的是混合像元分解法,其直接利用原始数据经正规化处理后得到的植被指数来反演植被覆盖度,人为因素干扰较少,故相对于其它方法[12-14]更具有普遍意义,经过验证后的模型还可以应用到大范围地区,形成通用的植被覆盖度计算方法[15-17]。其中Gutmand和Ignatov提出的模型（简称A模型）[18]和Carlson和Ripley提出的模型（简称B模型）[19]最为常用,其表达式分别为：
F A = ( NDVI - NDV I soil ) ( NDV I veg - NDV I soil ) （1）
F B = [ ( NDVI - NDV I soil ) ( NDV I veg - NDV I soil ) ] 2 （2）
式中：NDVIsoil为完全是裸土或无植被覆盖区域的NDVI值;NDVIveg则代表完全被植被所覆盖的像元的NDVI值,即纯植被像元的NDVI值。
对比式（1）和式（2）发现,二者的最大区别在于是否求平方。Carlson和Ripley通过与叶面积指数的对比研究发现,基于NDVI估算的植被覆盖度在中低植被覆盖区域明显高于其真实数值,因此必须对其求平方[19],但显然这样会在高植被覆盖区出现低估现象。因此,模型的选择主要取决于研究区植被覆盖的实际特点,即研究高植被覆盖区时应选择A模型以避免高植被区域的低估,反之应选择B模型以避免低植被区域的高估。
为比较2种模型的可靠性,选择亚像元对比法对研究区2010-05-01的TM影像和2014-09-01的OLI影像进行精度验证。在近期的Google earth高分辨影像（2.15 m）上根据不同植被覆盖度等级选用分层随机抽样法选择100个样点,在每个TM像元点所对应的195个亚像元中,用人工识别的方法直接判读并计算出实际植被覆盖度,并与模型反演的植被覆盖度进行回归分析,获得系统误差(SE)来判断不同模型的可靠性。SE的计算公式为：
SE = 1 n ∑ i = 1 n FV C ′ i - FV C i （3）
式中：FVC′为模型求出的植被覆盖度值;FVC为实际的植被覆盖度值;n为样本数。
精度验证结果（图1）表明,虽然A、B模型的反演值与真实值相比都存在一定程度的偏差,但相比较而言,A模型的反演值与真实值之间的差异更小,且与实际植被覆盖度的吻合度(R2)也高于B模型,因此,本研究采用A模型来反演平潭岛的植被覆盖度。
Fig. 1 Accuracy comparison between A and B models

图1 A模型和B模型的精度比较

3.2 模型参数的取值

植被覆盖度反演模型中参数NDVIsoil与NDVIveg的取值在不同年份不同地区是不一样的。对于大部分裸地,NDVIsoil值理论上应该接近于0;植被达到全覆盖状态时,NDVIveg值接近于1。但由于不同时间、地域等各种自然条件差异的影响[19-20],这2项参数的取值是不确定的,因此,采用一个固定的NDVIsoil值和NDVIveg值是不准确的。在此,本研究采用近似替代的方法来确定不同影像的NDVIsoil和NDVIveg值,通过分析Landsat影像的NDVI数据,结合平潭岛的现实状况和NDVI值的累积概率分布表,确定置信度为0.5%来计算植被覆盖度。即每幅时相选取累积频率为0.5%的NDVI值为NDVIsoil,99.5%的NDVI值为NDVIveg。若小于NDVIsoil则将其NDVI赋值为0,若大于NDVIveg,则将其NDVI赋值为1。由此得到2001、2010和2014年研究区遥感影像中NDVIsoil和NDVIveg的值,如表1所示。
Tab. 1 The value of NDVIsoil and NDVIveg

表1 NDVIsoil和NDVIveg的取值

2001年 2010年 2014年
NDVIsoil 0.1112 0.1259 0.0921
NDVIveg 0.8526 0.6941 0.8415

4 平潭岛植被覆盖度的空间分布及 变化特征

4.1 不同时相平潭岛植被覆盖度的提取及其分布 特点

运用式（1）和表1中的参数取值,提取出不同时相平潭岛的植被覆盖度,并采用密度分割法将其分为5级（图2）,从低到高分别表征为低植被覆盖度、中低植被覆盖度、中植被覆盖度、中高植被覆盖度和高植被覆盖度。在此基础上,对各等级的面积进行统计（表2）。
Tab. 2 Area and percentage of each fractional vegetation cover level in 2001, 2010 and 2014

表2 研究区不同时相各等级植被覆盖度统计表

植被覆盖度
（FVC）		 2001年 2010年 2014年
面积/km2 百分比/% 面积/km2 百分比/% 面积/km2 百分比/%
低度(0%~20%) 12.14 4.67 32.54 12.57 35.11 13.19
中低度(20%~40%) 24.13 9.28 73.81 28.51 41.66 15.65
中度(40%~60%) 65.04 25.01 71.60 27.65 54.28 20.39
中高度(60%~80%) 116.45 44.78 49.10 18.96 70.47 26.47
高度(80%~100%) 42.31 16.27 31.88 12.31 64.69 24.30
均值/% 61.74 47.92 56.74
标准差/% 19.97 23.80 27.11
图2显示,研究区植被覆盖度在2001-2014年变化显著,总体上看,低植被覆盖度区域（红色斑块）明显增多,中高植被覆盖度区域（浅绿斑块）大幅度减少,高植被覆盖度区域（深绿斑块）略微增加。表2统计结果表明,3个时相中植被覆盖度达中度以上的区域面积分别占总面积的86.00%、58.92%和71.16%。可见,平潭岛有一半以上区域的植被覆盖度都在中度以上,整体植被覆盖状况良好。研究期内,平潭岛的植被覆盖度变化主要表现在：低植被覆盖度和高植被覆盖度的区域都有所扩大,其中低植被覆盖度区域变化显著,13年间面积增加了65.42%,高植被覆盖度区域面积也增加了34.60%;而中高植被覆盖度区域则出现下降趋势,13年间面积减少了39.48%。
Fig. 2 The classification of fraction vegetation cover in 2001, 2010 and 2014

图2 研究区2001-2014年植被覆盖度分级图

此外,通过表2数据分析可以看出,2001-2014年研究区植被覆盖度均值由61.74%下降至56.74%,标准差也由19.97%上升到27.11%,说明在研究期内平潭岛植被覆盖度减少的同时,植被差异性增加,即植被覆盖种类趋于多样化。

4.2 平潭岛植被覆盖度动态变化的分布特征

为揭示平潭岛植被覆盖度的变化特征,进一步采用差值影像算法提取出2001-2014年平潭岛植被覆盖度变化影像（图3）,并对各等级的变化面积进行统计（表3）。蓝绿色调表征植被覆盖度增加,红色调表征植被覆盖度降低,黄色表征植被覆盖度未发生变化。
Tab. 3 Area and percentage changes of fraction vegetation cover between 2001 and 2014

表3 2001-2014年研究区植被覆盖度变化统计表

变化面积等级 2001-2010年 2010-2014年 2001-2014年
面积比例/% 升降合计/% 面积比例/% 升降合计/% 面积比例/% 升降合计/%
-4 0.13 53.95 0.23 11.16 0.34 31.56
-3 3.10 0.77 2.22
-2 14.98 2.22 7.01
-1 35.74 7.94 21.99
0 40.80 40.80 41.07 41.07 47.09 47.09
+1 4.91 5.24 33.67 47.77 18.68 21.36
+2 0.31 11.30 2.51
+3 0.02 2.49 0.16
+4 0.00 0.31 0.01
2010年福建省根据国务院关于加快建设海峡西岸经济区的指示精神正式批准成立了平潭综合实验区,由此在平潭岛展开了一系列的海岛开发建设活动。因此,本文以2010年为时间分界点,分别从2001-2010年、2010-2014年及2001-2014年这3个时间段进行变化分析。
Fig. 3 Maps of fraction vegetation cover change in the study area between 2001 and 2014

图3 2001-2014年研究区植被覆盖度变化图

由图3（a）可知,2001-2010年研究区植被覆盖度的总体变化特征为下降,变化图主要呈现红、黄色调,即植被覆盖度下降和不变的区域分布较多,特别在中部地区,其植被覆盖度下降显著,红色斑块密集。结合表3的统计数据可知,该时间段内,植被覆盖度下降的区域面积比例高达53.95%,不变的面积比例占40.80%,而升高的面积比例仅为5.24%,数据统计结果与变化图的色调变化相吻合,分析结果说明在平潭综合实验区创建之前,研究区的植被覆盖度主要以下降为主,植被覆盖退化现象较为 突出。
由图3（b）可知,2010-2014年研究区植被覆盖度的总体变化特征为增加,变化图主要呈现黄、绿色调,绿色斑块分布区域广泛,表明绝大部分地区植被覆盖度都有所增加。表3显示,该时间段内植被覆盖度增加的区域面积比例高达47.77%,而降低的面积比例只占11.16%,说明在平潭综合实验区开发建设的初期阶段,岛上的植被覆盖度有明显增加趋势,植被覆盖状况得到一定程度的改善。
综合整个研究期的变化来看,2001-2014年研究区接近一半的区域植被覆盖度能保持稳定没有变化,但植被覆盖度下降区域的面积比例（31.56%）仍高于植被覆盖度增加的面积比例（21.36%）。可见,在整个研究时段内平潭岛的植被覆盖度总体上是下降的,主要原因是前一时间段内（2001-2010年）的植被覆盖度下降过于严重,植被退化或破坏的区域面积比重较大,即使在后一时间段内（2010-2014年）植被覆盖度有所增加,但仍未能完全消除前期的下降影响。总体上,研究区大部分区域的植被覆盖度下降等级以-1级为主（21.99%）,属轻微下降,只有小范围区域变化等级达-2、-3级,表明在局部区域植被覆盖下降较为严重。

5 平潭岛植被覆盖度的变化分析

为探究研究区植被覆盖度变化的原因,进一步对研究区进行土地覆盖类型分类,并将其与植被覆盖变化图进行叠加分析。

5.1 研究区土地覆盖类型变化分析

基于遥感影像自身的光谱特征,采用决策树分类方法分别对2001、2010和2014年的影像进行土地覆盖类型分类。将平潭岛的土地覆盖类型划分为5类,即林地、耕地、水体、建筑用地、未利用地。以同期或近期的Google Earth高分辨率影像作为精度验证数据,采用随机抽样法,分别采集330个样点对2001、2010和2014年的分类结果进行精度验证。结果表明,分类精度分别为80.7%、87.6%和90.3%,Kappa系数分别为0.56、0.78和0.86,均满足应用需要。通过使用ENVI软件进行分类后变化检测分析,得到面积转移矩阵表（表4）。
Tab. 4 The area transition matrix of different landcovers in the study area from 2001 to 2014

表4 2001-2014年研究区土地覆盖类型面积转移矩阵

类型/km2 水体 未利用地 建筑用地 林地 耕地
水体 5.95 1.39 0.14 0.30 1.98
未利用地 7.94 8.56 1.11 2.43 4.40
建筑用地 3.73 2.79 26.16 3.51 20.11
林地 1.91 2.45 1.88 54.89 30.24
耕地 0.66 1.84 0.21 6.87 82.33
表4结果表明,2001-2014年研究区分别有3.51 km2的林地和20.11 km2的耕地转化为建筑用地,极大地缩减了植被覆盖区域,同时其他地类转为林地或耕地的面积却非常少,因此总体上土地覆盖类型的变化是导致研究区植被覆盖面积大量减少、植被覆盖度下降的直接原因。

5.2 植被覆盖度变化的具体原因分析

植被覆盖度变化结果显示,2001-2010年植被覆盖度下降最明显的为研究区的中部片区（图3(a)）,这里是平潭县多个乡镇所在地,人口较为集中,在2001-2010年该片区进行了大面积的土地整理[21],大片有林地等高植被覆盖区域面积锐减,转为建筑用地、耕地等低植被覆盖类型,区域植被覆盖度下降显著;此外,由于城镇发展导致的用地需求矛盾日益突出,导致不断向东扩张,占用了大片的东部沿海防护林地,因此也造成了该片区植被覆盖度的大幅下降。在此期间,研究区南部局部片区也出现了一定程度的植被覆盖度下降趋势,这主要是由于当地之前营造的防护林树种单一且老化严重,长期受人类活动干扰和恶劣自然条件的影响,树种退化严重,被砍伐后,造林成活率低,二代幼林矮化,导致局部片区植被覆盖度有所降低[22]。
2010-2014年整个研究区总体上植被覆盖度呈上升趋势,变化图显示大部分区域都出现了植被覆盖状况有所改善的现象（图3(b)）。自2010年平潭综合实验区建立后,在海岛开发伊始就遵循规划先行的原则,制定了科学合理的建设规划,按照其总体规划的近期（2010-2015）建设目标[23],要在确保原有2000 hm2基本农田保护面积的基础上,增划 714 hm2优质耕地与基本农田共同构成基本农田保护区,因此在此后的海岛开发中不断扩增的基本农田保护区在一定程度上改善了区域的植被覆盖度。另外,平潭岛在开发建设中积极执行《平潭综合实验区2010年大绿化工程实施方案》,开发初期就划定了多个生态保护区（南寨山、将军山、君山自然保护区）,并加大力度植树造林,进一步完善了长江澳、燕下埔等5个风口的防护林带建设[24],通过推广生态农业、村镇绿化建设、构建生态廊道及环岛防护绿带等建设措施,以点带面形成日趋完善的海岛绿地系统,为生态海岛构建了重要的绿色屏障。根据《平潭综合实验区森园局2012年工作总结曁2013年工作计划》：2010-2012年连续3年在长江澳风口沿岸前沿造林80 hm2;流东流西风口共完成造林绿化200.01 hm2;在三十六脚湖周边造林绿化240.01 hm2;环岛路、坛西大道等交通干道造林66.67 hm2;2013年在对原有林区进行保护和修复的基础上再造林500.03 hm2[25]。截止2015年,长江澳风口沿岸造林已达133.34 hm2,平潭岛全区共完成造林绿化总面积9527.14 hm2,区域植被覆盖度大幅度上升,海岛的植被覆盖状况得到了较大的改善。但在平潭综合实验区的建设过程中,许多片区的土地覆盖类型也发生了较大的变化,成片的新开发用地占用了大量的农田耕地,纵横交错的道路建设使大片的林地消失,围海造地形成的大量未开发用地,这些都导致局部片区的植被覆盖度又有所下降。
纵观2001-2014年植被覆盖变化,主要是由于之前的海岛开发缺乏科学规划,岛上植被的开发利用不合理,再加上人为破坏,导致植被面积锐减,植被覆盖度严重下降。自平潭综合实验建立以来,得益于所采取的科学规划、大力度植树造林以及日益完善的海岛绿地系统建设等措施,使研究区的植被覆盖度有了快速的提升,整体植被覆盖状况得到了较大的改善,极大地弥补了之前植被覆盖度大幅下降的不利影响。因此,在平潭岛的后续开发建设中,要强化规划意识,继续坚持规划先行,科学制定开发目标,大力推行造林绿化,不断提升平潭岛的植被覆盖度,使平潭综合实验区的开发建设更具有生态可持续性。

The authors have declared that no competing interests exist.

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
潘耀忠,李晓兵,何春阳.中国土地覆盖综合分类研究——基于NOAA/AVHRR和Holdridge PE[J].第四纪研究,2000,20(3):270-281.<p>在地理信息系统支持下，本文探讨了将连续时间序列NOAA／AVHRR的NDVI影像与反映区域植被分布格局的气候综合指标可能蒸散(Holdridge方法的生命地带所拟订的生物温度、降水量和可能蒸散率)，按反映其他带性分布特征的量化数学模式，结合数字高程模型生成数字图像，共同作为分类向量，在多种基础图件支持下，通过主成分分析、非监督分类的方法，对中国的土地覆盖进行了多维信息综合分类。该方法改变了遥感影像的单一信息结构，丰富了信息含量。分类结果显示，与传统的单独利用NDVI时间序列非监督分类的方法相比，克服了个别类别之间的容易造成的混分现象，提高了分类精度，完成了遥感与植被环境参数间的多维信息复合分类，具有深远的应用价值。</p>

[ Pan Y Z, Li X B, He C Y.Research on comprehensive land cover classification in China: based on NOAA/AVHRR and Holdridge PE index[J]. Quaternary Sciences, 2000,20(3):270-282. ]

[2]
Baret F, Weiss M, Lacaze R, et al.GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production[J]. Remote Sensing of Environment, 2013,137:299-309.Essential climate variables such as LAI or FAPAR are required for the monitoring, understanding and modeling of land surfaces at the global scale. While several products were already developed from the current medium resolution sensors, the few validation exercises currently achieved highlighted significant discrepancies and inconsistencies. The objective of this study is to develop improved global estimates of LAI, FAPAR and FCOVER variables by capitalizing on the development and validation of already existing products. In a first step, the performances of the MODIS, CYCLOPES, GLOBCARBON and JRC-FAPAR products were reviewed. The MODIS and CYCLOPES products were then selected since they provide higher level of consistency. These products were fused to generate the improved LAI, FAPAR and FCOVER values that were later scaled to closely match their expected range of variation. Finally, neural networks were trained to estimate these fused and scaled products from SPOT-VEGETATION top of canopy directionally normalized reflectance values. The resulting GEOV1 products are associated to quality control flags as well as quantitative estimates of uncertainties. Performances of the GEOV1 products are finally evaluated in a companion paper. The GEOV1 products are freely available to the community at www.geoland2.eu from 1999 up to present, globally at 1/112 degrees spatial sampling grid at the dekadal time step. (c) 2013 Elsevier Inc. All rights reserved.

DOI

[3]
陈效逑,王恒.1982-2003年内蒙古植被带和植被覆盖度的时空变化[J].地理学报,2009,64(1):84-94.<p>利用内蒙古地区1982-2003 年遥感归一化差值植被指数(NDVI) 数据， 对植被带进行了 分时段的划分， 并以典型草原植被带为例， 分析植被覆盖度时空变化及其与水热因子的关系。 结果表明： 在整个研究期间， 典型草原带的面积呈增加的趋势， 荒漠草原带的面积呈减少的 趋势， 森林带、森林草原带和荒漠带的面积趋势变化不明显。总体上看， 从时段1 （1982－ 1987 年） 到时段2 （1988－1992 年） 植被带进化演变的面积占优势， 从时段2 （1988－1992 年） 到时段3 （1993－1998 年） 进化和退化演变的面积相当， 从时段3 （1993－1998 年） 到 时段4 （1999－2003 年） 退化演变的面积占优势。在典型草原带内， 多年平均植被覆盖度具有明显的季节变化， 从5 月上半月返青开始到8 月下半月达到年最大值， 其空间演进以大兴 安岭两翼为中心， 逐渐向东南的西辽河平原和向西的乌兰察布高原扩展。前期降水量与覆盖 度季节增量年际变化之间呈正相关， 显著正相关的区域位于锡林郭勒高原西部和乌兰察布高原， 而气温与覆盖度季节增量年际变化的相关一般不显著。典型草原年最大覆盖度线性趋势 降低与升高的面积分别占52.6%和47.4%， 其中， 呼伦贝尔高原西部边缘以及大兴安岭山麓 两侧的年最大覆盖度呈显著降低的趋势， 而西辽河平原西南部和努鲁儿虎山东段的年最大覆 盖度呈显著升高的趋势。年降水量是影响年最大覆盖度的主要因子， 而年均温对年最大覆盖度的影响不明显。</p>

[ Chen X Q, Wang H.Spatial and temporal variations of vegetation belts and vegetation cover degrees in Inner Mongolia from 1982 to 2003[J]. Acta Geographica Sinica, 2009,64(1):84-94. ]

[4]
吴昌广,周志翔,肖文发,等.基于MODIS NDVI 的三峡库区植被覆盖度动态监测[J].林业科学,2012,48(1):22-28.<p>基于MODIS&mdash;NDVI遥感数据,采用像元二分模型估算三峡库区2000&mdash;2009年的年最大植被覆盖度,并在像元尺度上分析库区年最大植被覆盖度的时空变化规律及其驱动力。结果表明: 三峡库区大部分区域处于高植被覆盖度,并随高程和坡度的增加而增大,其中年最大植被覆盖度大于60%的区域占92.35%; 近10年来,库区年最大植被覆盖度总体呈微弱上升趋势,其中呈显著增加或降低趋势的像元数仅占7.16%,在20个区县中石柱、江津和丰都的植被覆盖度存在退化风险; 降水是影响库区植被覆盖度年际波动的主导因子,当年5&mdash;8月降水量与年最大植被覆盖度的相关性最高,但在空间上存在差异,其中呈显著正相关区域主要分布于库区西部低山丘陵农业种植区,该区域降水增加有利于植被生长,而部分高海拔地区的年最大植被覆盖度与降水呈显著负相关,过多降水反而会抑制植被生长。</p>

DOI

[ Wu C G, Zhou Z X, Xiao W F, et al.Dynamic monitoring of vegetation coverage in three Gorges Reservoir area based on MODIS NDVI[J]. Scientia Silvae Sinicae, 2012,48(1):22-28. ]

[5]
张喜旺,吴炳方.基于中高分辨率遥感的植被覆盖度时相变换方法[J].生态学报,2015,35(4):1155-1164.植被覆盖度是衡量地表植被状况、指示生态环境变化的一个重要指标,也是许多学科的重要参数。传统的测量方法难以获取时间连续的面状数据,且耗时、耗力,很难大范围推广。遥感估算方法虽然可以弥补传统方法的不足,但由于云覆盖等天气条件的影响,获得同一时相覆盖整个研究区的遥感影像非常困难,时相的差异必然导致研究结果产生误差。针对植被覆盖度这一重要生态参数,结合低分辨率遥感数据的时间优势和中高分辨率遥感数据的空间优势,提出一种时相变换方法,将源于中高分辨率影像的植被覆盖度变换到研究需要的时相上。首先,利用像元二分模型计算MODIS尺度的时间序列植被覆盖度,并利用已经获得的SPOT影像计算其获取时相上的植被覆盖度;其次,利用土地利用图划分植被覆盖类型,并利用MODIS数据和土地利用数据之间的空间对应关系制作MODIS像元内各类植被覆盖的面积百分比数据;再次,利用面积百分比数据提取各类植被覆盖的纯像元,结合MODIS植被覆盖度时间序列,从而提取各类植被覆盖纯像元的植被覆盖度时间序列曲线;最后利用像元分解的方法提取MODIS像元内各类植被覆盖组分的植被覆盖度的变化规律,将其应用到该组分对应位置上SPOT像元的植被覆盖度上,从而将其变换到所需要的时相上。在密云水库上游进行试验,将覆盖研究区的10 景SPOT5多光谱影像计算的植被覆盖度统一变换到7 月上旬,结果显示:视觉效果上明显好转,且空间上连续一致;变换前后植被覆盖度的统计量对比结果也符合植被生长规律;利用外业样点数据与对应位置的植被覆盖度变换结果进行回归分析,结果发现各植被覆盖类型的<i>R</i><sup>2</sup>均在0.8左右,表明变换结果与实测值非常接近,时相变换的效果较好,从而可以很好地促进相关研究精度的提高。

DOI

[ Zhang X W, Wu B F.A temporal transformation method of fractional vegetation cover derived from high and moderate resolution remote sensing data[J]. Acta Ecologica Sinica, 2015,35(4):1155-1164. ]

[6]
Xiao J, Moody A.A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA[J]. Remote Sensing of Environment, 2005,98(2):237-250.We compared a set of methods for estimating fractional green vegetation cover (f

DOI

[7]
马娜,胡云锋,庄大方,等.基于遥感和像元二分模型的内蒙古正蓝旗植被覆盖度格局和动态变化[J].地理科学,2012,32(2):251-256.以内蒙古锡林郭勒盟正蓝旗为研究区,应用中国环境与灾害监测预报小卫星数据(HJ-1ACCD)及美国陆地卫星数据(LandsatETM+),在充分考虑区域土壤和植被类型等背景基础上,先后使用了MNF、二维散点制图以及PPI方法,得到具有时空针对性的纯净像元;以此为基础,运用像元二分模型计算得到研究区植被覆盖度空间分布数据;最后,结合研究区其它权威土地覆被和土地利用(LUCC)数据,对比分析了本研究所得的植被覆盖度数据精度,评价了2000～2009年间区域植被覆盖度动态变化过程,指出本区在实施有关生态工程后植被覆盖度恶化趋势得到遏制。研究结果证明,在锡林郭勒盟正蓝旗地区应用像元二分模型计算植被覆盖度的方法是简洁且有效的。

[ Man, Hu Y F, Zhuang D F, et al. Vegetation coverage distribution and its change in Plan Blue Banner based on remote sensing data and dimidiate pixel model[J]. Scientia Geographica Sinica, 2012,32(2):251-256. ]

[8]
徐涵秋,唐菲.新一代Landsat系列卫星:Landsat 8遥感影像新增特征及其生态环境意义[J].生态学报,2013,33(11):3249-3257.美国Landsat 8卫星的成功发射使一度中断的Landsat对地观测得以继续。Landsat 8除了保持Landsat 7卫星的基本特征外,还在波段的数量、波段的光谱范围和影像的辐射分辨率上进行了改进。基于该卫星的首幅影像,针对这些新的特性进行了分析和研究。研究发现:(1)新增的卷云波段有助于区别点云和高反射地物;(2)卷云波段设计的波长范围位于粘土矿物光谱反射的强吸收带,有利于土壤与建筑不透水面信息的区别; (3)新增的深蓝波段有助于水体悬浮物浓度的监测;(4)全色影像波长范围的收窄有利于该影像上植被和非植被的区别; (5)辐射分辨率的提高可避免极亮/极暗区的灰度过饱和现象,这对反射率极低的水体的细微特征识别有很大帮助。显然,Landsat 8这些新增的优点将会对全球生态环境变化的监测产生积极的作用。

DOI

[ Xu H Q, Tang F.Analysis of new characteristics of the first Landsat 8 image and their eco-environmental significance[J]. Acta Ecologica Sinica, 2013,33(11):3249-3257. ]

[9]
Chander G, Markham B.Revised Landsat-5 TM radiometric calibration procedures and post calibration dynamic ranges[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003,41(11):2674-2677.Effective May 5, 2003, Landsat-5 (L5) Thematic Mapper (TM) data processed and distributed by the U.S. Geological Survey (USGS) Earth Resources Observation System (EROS) Data Center (EDC) will be radiometrically calibrated using a new procedure and revised calibration parameters. This change will improve absolute calibration accuracy, consistency over time, and consistency with Landsat-7 (L7) Enhanced Thematic Mapper Plus (ETM+) data. Users will need to use new parameters to convert the calibrated data products to radiance. The new procedure for the reflective bands (1-5,7) is based on a lifetime radiometric calibration curve for the instrument derived from the instrument's internal calibrator, cross-calibration with the ETM+, and vicarious measurements. The thermal band will continue to be calibrated using the internal calibrator. Further updates to improve the relative detector-to-detector calibration and thermal band calibration are being investigated, as is the calibration of the Landsat-4 (L4) TM.

DOI

[10]
Chander G, Markham B L, Helder D L.Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors[J]. Remote Sensing of Environment, 2009,113(5):893-903.This paper provides a summary of the current equations and rescaling factors for converting calibrated Digital Numbers (DNs) to absolute units of at-sensor spectral radiance, Top-Of-Atmosphere (TOA) reflectance, and at-sensor brightness temperature. It tabulates the necessary constants for the Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Advanced Land Imager (ALI) sensors. These conversions provide a basis for standardized comparison of data in a single scene or between images acquired on different dates or by different sensors. This paper forms a needed guide for Landsat data users who now have access to the entire Landsat archive at no cost.

DOI

[11]
Chavez P S Jr. Image-based atmospheric corrections-revisited and improved[J]. Photogrammetric Engineering and Remote Sensing, 1996,62(9):1025-1036.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.

DOI

[12]
Sellers P J, Tucker C J, Collatz G J, et al.A global 1 by 1 NDVI data set for climate studies. Part2: The generation of global fields of terrestrial biophysical parameters from the NDVI[J]. International Journal of Remote Sensing, 1994,15(17):3519-3545.ABSTRACT A satellite-based 1 by 1 normalized difference vegetation index (NDVI) data set has been processed to derive land surface parameters for general circulation models of the atmosphere (GCMs). Prior to calculation of the land surface parameters, corrections were applied to the source NDVI data set to account for (i) obvious anomalies in the data time-series, (ii) the effect of variations in solar zenith angle, (iii) data dropouts in cold regions where a temperature threshold procedure designed to screen for clouds also eliminates cold land surface points, and (iv) persistent cloud cover in the tropics. An outline of the procedures for calculating land surface parameters from the corrected NDVI data set is given, and a brief description is provided of source material that was used in addition to the NDVI data. The data sets summarized in this paper should represent improvements over prescriptions currently used in land surface parameterizations in that the spatial and temporal dynamics of key land surface parameters, in particular of those related to vegetation, are obtained from direct measurements rather than indirectly inferred from survey-based land cover classifications.

DOI

[13]
Los S O, Collatz G J, Sellers P J, et al.A global 9-yr biophysical land surface dataset from NOAA AVHRR data[J]. Journal of Hydrometeorology, 2000,1(2):183-199.

[14]
Peter R J.North Estimation of f APAR, LAI , and vegetation fractional cover from ATSR-2 imagery[J]. Remote Sensing of Environment, 2002,80(1):114-121.Abstract We examine methodologies for estimation of vegetation cover, leaf area index (LAI), and fraction of absorbed photosynthetically active radiation (fAPAR), considering the spectral sampling and dual-view capability of the ATSR-2 sensor. A set of simulated ATSR-2 reflectance measurements and corresponding vegetation parameters is defined using a Monte Carlo ray-tracing model. The case of semiarid vegetation is considered allowing for varying fractional cover, structure, and presence of standing litter. The error in estimation of vegetation properties using vegetation indices, linear spectral unmixing, and model inversion is compared over this dataset, quantified by a measure of signal to noise (S/N). For the estimation of fAPAR, the NDVI gave best S/N among vegetation indices (S/N 4.5). Linear mixture modelling based on library spectra showed considerable improvement over vegetation indices for estimation of total vegetation cover. LAI is not retrieved with much accuracy by any method in the presence of standing litter and variable fractional cover. Model inversion has potential to be the most accurate method for retrieving all parameters, but only if the model approximates reality within 15%. Overall, the S/N in estimating parameters by any method is considerably lower than the S/N in instrument calibration (20/1). Use of the dual-view showed potential to improve estimates, but requires accurate registration.

DOI

[15]
Gillies R R, Kustas W P, Humes K S.A verification of the triangle method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized difference vegetation Index (NDVI) and surface[J]. International Journal of Remote Sensing, 1997,18(15):3145-3166.An inversion procedure is presented for estimating surface soil water content (as surface moisture availability, Mo ), fractional vegetation cover ( Fr ), and the instantaneous surface energy fluxes, using remote multispectral measurements made from an aircraft. The remotely derived values of these fluxes and the soil water content are compared with field measurements from two intensive field measurement programs FIFE and MONSOON '90. The measurements from the NS001 multispectral radiometer were reduced to fractional vegetation cover, surface soil water content (surface moisture availability), and turbulent energy fluxes, with the application of a soil vegetation atmosphere transfer (SVAT) model. A further step in the inversion process involved 'stretching' the SVAT results between pre-determined boundaries of the distribution of normalized difference vegetation index (NDVI) and surface radiant temperature ( To ). Comparisons with measurements at a number of sites from two field experiments show standard errors, between derived and measured fluxes, generally between 25 and 55Wm-2, or about 10-30 per cent of the magnitude of the fluxes and for surface moisture availability of 16 per cent.

DOI

[16]
杨胜天,刘昌明,杨志峰,等.南水北调西线调水工程区的自然生态环境评价[J].地理学报,2002,57(1):11-18.<p>应用遥感与地理信息系统方法,对南水北调西线调水工程区遥感数据、地理信息数据进行一系列的信息识别、信息提取,获取了工程区地形地势、土地覆盖、植被覆盖度、植被净初级生产力、年均温度和年降雨量等主要的自然环境因子。用海拔高度反映地势状况,土地覆盖类型反映地表覆盖状况,植被覆盖度反映抗侵蚀能力,植被净生产力反映物质能量的循环状况,年均温度反映热量状况,年降水量反映降水状况,建立综合自然环境指数,对调水工程区的自然生态环境现状进行定量评价。按计算结果将调水工程区分为四级区域,并对这四级区域进行空间统计,分析了它们的自然生态环境,为全面深入进行南水北调西线工程区环境影响评价提供可供借鉴的新思路和新方法。</p>

[ Yang S T, Liu C M, Yang Z F, et al.Natural eco-environmental evaluation of West Route Area of Inter-basin Water Transfer Project[J]. Acta Geographic Sinica, 2002,57(1):11-18. ]

[17]
Qi J, Marsett R C, Moran M S, et al.Spatial and temporal dynamics of vegetation in the San Pedro River basin area[J]. Agricultural and Forest Meteorology, 2000,105(1):55-68.Abstract Changes in climate and land management practices in the San Pedro River basin have altered the vegetation patterns and dynamics. Therefore, there is a need to map the spatial and temporal distribution of the vegetation community in order to understand how climate and human activities affect the ecosystem in the arid and semi-arid region. Remote sensing provides a means to derive vegetation properties such as fractional green vegetation cover (fc) and green leaf area index (GLAI). However, to map such vegetation properties using multitemporal remote sensing imagery requires ancillary data for atmospheric corrections that are often not available. In this study, we developed a new approach to circumvent atmospheric effects in deriving spatial and temporal distributions of fc and GLAI. The proposed approach employed a concept, analogous to the pseudoinvariant object method that uses objects void of vegetation as a baseline to adjust multitemporal images. Imagery acquired with Landsat TM, SPOT 4 VEGETATION, and aircraft based sensors was used in this study to map the spatial and temporal distribution of fractional green vegetation cover and GLAI of the San Pedro River riparian corridor and southwest United States. The results suggest that remote sensing imagery can provide a reasonable estimate of vegetation dynamics using multitemporal remote sensing imagery without atmospheric corrections.

DOI

[18]
Gutman G, Ignatov A.The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models[J]. International Journal of Remote sensing, 1998,19(8):1533-1543.Fraction of green vegetation, fg, and green leaf area index, Lg, are needed as a regular space-time gridded input to evapotranspiration schemes in the two National Weather Service (NWS) numerical prediction models regional Eta and global medium range forecast. This study explores the potential of deriving these two variables from the NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data. Obviously, one NDVI measurement does not allow simultaneous derivation of both vegetation variables. Simple models of a satellite pixel are used to illustrate the ambiguity resulting from a combination of the unknown horizontal (fg) and vertical (Lg) densities. We argue that for NOAA AVHRR data sets based on observations with a spatial resolution of a few kilometres the most appropriate way to resolve this ambiguity is to assume that the vegetated part of a pixel is covered by dense vegetation (i.e., its leaf area index is high), and to calculate fg=(NDVI-NDVI0)/(NDVI8-NDVI0), where NDVIo (bare soil) and NDVI (dense vegetation) are specified as global constants independent of vegetation/soil type. Global (0.15o)2 spatial resolution monthly maps of fg were produced from a 5-year NDVI climatology and incorporated in the NWS models. As a result, the model surface fluxes were improved.

DOI

[19]
Carlson T N, Ripley D A.On the relation between NDVI, fractional vegetation cover, and leaf area index[J]. Remote Sensing of Environment, 1997,62(3):241-252.ABSTRACT We use a simple radiative transfer model with vegetation, soil, and atmospheric components to illustrate how the normalized difference vegetation index (NDVI), leaf area index (LAI), and fractional vegetation cover are dependent. In particular, we suggest that LAI and fractional vegetation cover may not be independent quantitites, at least when the former is defined without regard to the presence of bare patches between plants, and that the customary variation of LAI with NDVI can be explained as resulting from a variation in fractional vegetation cover. The following points are made: i) Fractional vegetation cover and LAI are not entirely independent quantities, depending on how LAI is defined. Care must be taken in using LAI and fractional vegetation cover independently in a model because the former may partially take account of the latter; ii) A scaled NDVI taken between the limits of minimum (bare soil) and miximum fractional vegetation cover is insenstive to atmospheric correction for both clear and hazy conditions, at least for viewing angles less than about 20 degrees from nadir; iii) A simple relation between scaled NDVI and fractional vegetation cover, previously described in the literature, is further confirmed by the .simulations; iv) The sensitive dependence of LAI on NDVI when the former is below a value of about 2 4 may be viewed as being due to the variation in the bare soil component.

DOI

[20]
Rundquist B C.The influence of canopy green vegetation fraction on spectral measurements over native tall grass prairie[J]. Remote Sensing of Environment, 2002,81(1):129-135.Spectral vegetation indices (SVIs) calculated from remotely sensed data are routinely used to monitor spatial and temporal changes in vegetation biophysical characteristics. The most commonly used SVI, the Normalized Difference Vegetation Index (NDVI), has been criticized because of its sensitivity to atmospheric conditions and substrate reflectivity, as well as its insensitivity to increases in vegetation biomass past particular thresholds. Yet, the use of NDVI remains widespread and is attractive because of the ease with which it is calculated. This article examines the utility of NDVI for monitoring the biophysical characteristic of green vegetation fraction (GVF) in comparison to other SVIs suggested as improvements. Statistical relationships between spectral response, presented in the form of SVIs, and GVF of a native tallgrass prairie canopy are explored. Broadband spectra were gathered from close-range during the 1999 growing season at the Konza Prairie Biological Station (KPBS), located in the Flint Hills region of Kansas, USA. Through simple regression analyses, spectra were related to GVF estimates derived from digital color photographs. SVIs evaluated are the NDVI, the Soil Adjusted Vegetation Index (SAVI), and the square of scaled NDVI ( N * 2 ). Results show that NDVI and N * 2 were statistically related to GVF ( R 2 for NDVI=.77, N * 2 =.78) throughout the growing season. The least-squares line defining the relationship between N * 2 and GVF approximated a 1:1 line. For June sample dates, all three SVIs were significant statistical predictors of GVF ( R 2 for NDVI=.89, N * 2 =.91, SAVI=.89). Regression coefficients for late-season sample dates were weaker, yet still significant in statistical terms ( R 2 for NDVI=.70, N * 2 =.70). While encouraging, these results suggest that further analyses are required to determine the usefulness of SVIs calculated from broadband devices for estimation of GVF when leaf litter dominates the scene.

DOI

[21]
林忠等. 平潭县土地开发整理专项规划(1997-2010年)[R].福建师范大学地理所,平潭县土地管理局,2001.

[ Lin Z, et al.Special planning of land development and consolidation in Pingtan County(1997-2010)[R]. Geography Institute of Fujian Normal University, Pingtan County Land Administration Bureau, 2001. ]

[22]
郭晓峰,吴耀建,姜尚,等.海岛生态脆弱性驱动机制及对策措施初探——以平潭岛为例[J].海峡科学,2009(3):3-5.海岛是海洋生态系统的重要组成部分,处于海陆相互作用的动力敏感地带,生态环境较为脆弱.本文以福建平潭岛为例,对海岛生态环境脆弱性的表现进行了阐述,分析了海岛生态脆弱性的驱动机制,并提出了平潭岛开发利用和生态环境良性发展的对策措施,以实现资源环境与社会经济的可持续发展.

DOI

[ Guo X F, Wu Y J, Jiang S, et al.The driving mechanism and countermeasures of ecological vulnerability of islands: A case of Pingtan island[J]. Strait Science, 2009,3:3-5. ]

[23]
福建省城乡规划设计研究院.平潭综合实验区总体规划(2010-2030)[R]. 2010.

[ Fujian Urban and rural Planning Design and Research Institute. Master Plan of Pingtan Comprehensive Experimental Zone(2010-2030)[R]. 2010. ]

[24]
陈雪珍. 平潭综合实验区生态防护林体系建设研究[J].环境科学与管理,2014,39(5):152-155.研究将结合平潭自然环境状况、现有及五大风口生态防护林建设现状及存在问题,从生态防护林的功能角度出发,提出加强生态防护体系建设内容与任务、重点建设和保护区域,构建以基干林带为主体,消浪林、防护林、农田林网、道路林网、城乡绿化美化相结合,建成以城市、乡镇、村庄、森林公园、风景区绿化为"点",以基干林带、道路绿化、农田防护林为"线",以水土保持林、水源涵养林、防风固沙林和其他防护林为"面",逐步形成由浅海水域向内陆延伸的带、网、片相结合的林种树种丰富、层次多样、结构稳定、功能完善的综合防护林体系。

DOI

[ Chen X Z.Study on ecological protection forest construction in Pingtan comprehensive experimental zone[J]. Environmental Science and Management, 2014,39(5):152-155. ]

[25]
平潭综合实验区森林与园林局.平潭综合实验区森园局2012年工作总结暨2013年工作计划[R].2013.

[ Bureau of forest and garden of Pingtan comprehensive experimental zone. Bureau of forest and garden of Pingtan comprehensive experimental zone, 2012 work summary and 2013 work plan[R]. 2013. ]

Options
文章导航

/

〈 〉