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Journal of Geo-information Science >

2017 , Vol. 19 >Issue 8: 1132 - 1140

DOI: https://doi.org/10.3724/SP.J.1047.2017.01132

Orginal Article

Analysis of Antarctic Emperor Penguins Colonies Changes Based on Remote Sensing

  • SHEN Xiaoyi ,
  • KE Changqing , * ,
  • ZHANG Jie
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  • 1. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023;2. Collaborative Innovation Center of South China Sea Studies, Nanjing 210023;3. The First Institute of Oceanography, SOA, Qingdao 266061
*Corresponding author: KE Changqing, E-mail:

Received date: 2016-11-28

  Request revised date: 2017-06-20

  Online published: 2017-08-20

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《地球信息科学学报》编辑部 所有
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Abstract

Emperor penguin is the indicator of Antarctic ecosystems. The distribution of its colonies owns essential significance for the study of Antarctic climate. Emperor penguins are sensitive to changes of the sea ice concentration and distribution. Thus, they have become an essential species for investigation on the effect of climate changes on the Antarctic ecosystems. However, it is difficult for the traditional manual investigation to obtain comprehensive and accurate information of the population colonies. Although some researchers have devoted to find emperor penguin colonies using remote sensing imageries in recent years, but their methods require considerable human involvement and cannot be used to detect all colonies rapidly and effectively. Emperor penguins breed and rest on land-fast sea ice and live in the same area for about six months, leaving extensive yellowish brown faeces which are significantly different from the white ice and snow around them. Inspired by the normalized difference snow index (NDSI), which is used for delineating snow cover, we can establish a/some similar index(s) to extract the faeces from extensive snow cover. On the basis of the difference between the reflectance of the faeces produced by emperor penguin in blue and red band, near infrared and shortwave infrared bands, two spectral indexes (NDII, EI) are putted forward to effectively recognize the faeces produced by emperor penguins, and determine their colony locations. According to the 195 scenes appropriate and quality-good Landsat 7 ETM+ imagery in 2009, a total of 38 emperor penguins colonies are obtained, 7 colonies of which are newly discovered (Bowman Island, Dibble Glacier, Auster, Point Geologie, Cape Crozier, Brownson Islands and Rupert Coast), 2 colonies have disappeared (Amundsen Bay disappeared and Ledda Bay), and the positions of the other 25 colonies (except Thuston Glacier, Luitpold, Sanae, Gould, Ragnhild and Beaufort Island) do not change significantly. The overall accuracy of colony detection is about 94% and the performance of the colony detection is influenced by the data quality and colony size. The performance of this method improves with increasing colony population size. Although spectral attributes are chosen to identify faeces produced by emperor penguins, some misclassifications may happen. This method may miss a few small colonies of which the sizes are smaller than spatial resolution of the imagery. The failure of detecting these colonies of small size is most likely due to the mixing of non-colony terrain in the Landsat pixels. These smaller colonies may well be identified by the satellites that have higher pixel resolution, and this method can be adapted to other high spatial resolution of satellite data in the future. The distribution of emperor penguin colonies is closely related to the climatic factors, and colonies tend to gather at the regions where temperature is low and ice concentration is high. It is different for climate change occurring at different colonies. Long-time and regional observations are needed to study the relationship between climate and the changes to the distribution of colonies. With the continued rise of the air temperature and the change of ice concentration, the colonies of which the latitude are below 70 °S are facing greater threat, and emperor penguin population shows a trend to shrink to the southern pole.

Key words： Emperor penguin; population colonies; change; detecton; climate; Antarctica

Cite this article

SHEN Xiaoyi , KE Changqing , ZHANG Jie . Analysis of Antarctic Emperor Penguins Colonies Changes Based on Remote Sensing[J]. Journal of Geo-information Science, 2017 , 19(8) : 1132 -1140 . DOI: 10.3724/SP.J.1047.2017.01132

1 引言

帝企鹅是南大洋的标志性生物之一,同时也是研究极地海洋生态系统的重要生物[1-2]。多数研究表明帝企鹅对海冰分布和气温变化十分敏感,气候变化对其种群的生育能力和生活习性具有重要影响[3]。随着气候变化的持续推进,海冰变化随之加剧[4],进而引起的海洋食物网的变化、物种生存竞争的加剧和暴风雪等极端天气现象的频发等,都会对帝企鹅的生存繁衍造成极大的影响[5-9]。虽然帝企鹅拥有较长的寿命和快速存储足量能量的能力,使其在历时千年的气候变化中生存下来[10],但由于身形和生活习性等因素限制了其捕食的范围,使帝企鹅的生存繁衍与气候变化息息相关。
目前对于帝企鹅种群分布和数目的研究十分有限,多数种群研究仅仅依赖于人工实地调查,受限于后勤和危险性等因素,人工调查难以获得理想的结果。多数帝企鹅在海冰上生育繁衍,而这些海冰通常会在来年夏天破裂。人工考察只能选择在晚冬或早春,寒冷的气候和恶劣的环境给现场考察带来较大的难度。帝企鹅种群广泛分布于南极大陆海岸带附近,依靠人工难以实现大范围的观测,每年仅有几个种群得到常规监测且分布相对集中,相关资料十分有限。研究气候变化对南极的影响迫切要求更加广泛的种群位置和数目信息,尤其是在典型的气候变化区域[4]。
Woehler于1993年整合了原先关于帝企鹅种群的实地观测资料,认为共有36个帝企鹅种群和 15 300对帝企鹅[11]。种群数目和帝企鹅数量均是从历史资料中获取,这些数据是否可靠具有很大的不确定性[12]。Fretwell等基于Landsat 7 ETM+影像利用人工目视识别帝企鹅种群排泄物来确定种群栖息地位置,共获取了33个帝企鹅种群[13]。LaRue等通过对航空像片上帝企鹅的数目进行人工计数来分析其种群迁徙[14]。显然,上述研究需要较多的人工参与,需要实地探测或者基于遥感影像的人工判别,难以实现快速、高效的种群识别,而实地探测或是利用航空相片仅局限于小尺度研究,无法全面、便捷地获取大范围的帝企鹅种群信息。但是,上述方法表明了利用遥感手段识别帝企鹅种群栖息地的可行性,但仍需要一种基于遥感影像的更加准确、快速的种群识别方法,实现帝企鹅种群栖息地的获取。
常规的种群调查需要研究人员实地考察种群分布的位置,但受限于南极大陆严峻的气候和复杂的地形条件,实地考察难以获得预期的结果且观测范围有限。然而,遥感技术尤其是高空间分辨率技术的发展给大规模、精细调查帝企鹅种群带来了机遇。对于多数光学遥感传感器（Landsat、MODIS等）,受限于传感器的空间分辨率,难以直接从影像上获取帝企鹅目标,因此需要利用某种“特征”来间接识别帝企鹅种群。帝企鹅种群在海冰上繁衍栖息达6个月之久,留下了大量的生活痕迹（黄褐色排泄物等）,这与白色的冰雪背景有很大的不同,根据卫星影像上排泄物的光谱特征可以很好地进行识别从而获取帝企鹅种群栖息地[15]。若采用传统的基于像元的分类方法进行提取,由于其只考虑单个像元的光谱值或亮度值,忽视地物的空间特征,故难以俘获分类目标的特征,分类精度受到一定影 响[16]。面向对象的分类方法,首先将影像分割成若干像元集组成的对象集合,再利用不同的光谱或空间特征对对象进行分类。这种方法既考虑了像元的光谱值,又考虑了对象的纹理、几何形状等特征,相较于面向像元的分类方法,可以有效地避免椒盐效应,分类精度有明显提高[17-18]。因此,根据遥感影像上排泄物与周围冰雪的光谱特征、几何纹理特征的差别,可以实现种群排泄物的识别,从而实现栖息地的获取。建立基于排泄物面向对象识别南极帝企鹅种群栖息地的遥感调查,主要包括以下步骤：① 遥感影像分割方法（基于光谱、几何特征）;② 南极帝企鹅排泄物的光谱特征分析及识别特征建立;③ 南极帝企鹅种群栖息地调查。
本文提出一种通过提取帝企鹅种群排泄物来精确定位其种群栖息地位置的方法。基于Landsat 7 ETM+卫星影像,对影像进行分割,在对象层上基于光谱特征进行帝企鹅种群排泄物的提取,进而获取帝企鹅种群位置。这种方法实现了基于Landsat 7 ETM+影像的帝企鹅种群栖息地位置的快速和全面提取,可以发现新的栖息地和确认疑似的栖息地。

2 研究数据和方法

2.1 数据

帝企鹅是南极唯一在南半球冬季孵卵繁衍的鸟类,大多数种群栖息地位于海岸带附近[19]。选取2009年10-12月云量较少、质量较好且覆盖南极大陆海岸带周边的195景Landsat 7 ETM+卫星影像进行帝企鹅种群排泄物的识别和定位（其中发现帝企鹅种群栖息地为49景）。Landsat 7 ETM+包含11个波段,其中可见光波段（0.45~1.65μm）的空间分辨率为30 m,热红外（10.40~12.50 μm）和全色波段（0.52~0.90 μm）的空间分辨率分别为60 m和15 m,辐射分辨率均为8bit。
南极月平均表面气温和月平均海冰密集度数据均来源于NOAA地球系统研究实验室（http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html）,空间分辨率均为2.5°×2.5°。数据时间始于1981年且每月均有更新,来源于基于多源遥感数据和观测数据的再分析数据。

2.2 研究方法

Landsat 7 ETM+影像经过预处理后,确定影像的分割尺度,基于分割窗口内像元的光谱特征和几何特征计算对象的光谱异质性和形状异质性,进而生成相应的对象。提取帝企鹅种群排泄物对象与其他地物对象典型的光谱特征差异以实现种群排泄物的识别,从而获取种群栖息地的位置并进行精度评价（图1）。
Fig. 1 Flowchart of this method

图1 方法流程图

2.2.1 影像分割
Landsat 7 ETM+卫星影像经过辐射定标、几何校正、大气校正(包括去除云层)[20]等处理后,采用自下向上、区域增长的方法将影像分割成若干个对象,对象由同质、相邻的像元组成[21]。该分割方法需要4个控制参数：各波段权重、光谱异质性权重、紧致度权重以及分割尺度。各波段权重表示分割时每个波段所占的比重。依据文献[21],光谱异质性权重和紧致度权重用来计算对象的异质性值Y,计算公式如下：
Y = X spectral × T spectral + X s h ape × T s h ape （1）
式中：Xspectral表示光谱异质性;Tspectral表示光谱性异质性的权重;Xshape表示形状异质性;Tshape表示形状异质性的权重;Tspectral、Tshape取值均在[0,1],Tspectral+Tshape=1。Xspectral和Xshape的计算公式如下：
X spectral = ∑ t = 1 n P t [ N merge × σ merge - ( N i × σ i + N j × σ j ) ] （2）
X s h ape = X compact × T compact + X smoot h × T smoot h （3）
其中, X compact = N merge L merge N merge – N i L i N i – N j L j N j （4）
X smoot h = N merge L merge H merge – N i L i H i – N j L j H j （5）
式中： t 表示波段; i 和 j 表示相邻的对象;merge表示合并后的对象; P 表示波段的权重值; N 表示对象包含的像元数; σ 表示对象内像元值的标准差; L 表示对象的周长; H 表示包含对象最小矩形的周长;Tsmooth、Tcompact取值均在[0,1],Tsmooth+Tcompact=1。分割尺度可以控制对象的大小,若异质性值小于分割尺度,则继续分割。分割尺度过大或过小都对分类精度有影响。
将Landsat 7 ETM+卫星影像除热红外波段外（分辨率与可见光波段不同）和全色波段所有可见光波段作为输入,且权重值均设置为1。经过大量实验且对分割结果进行目视分析后将分割尺度设置为50,形状异质性权重设置为0.5,光滑度权重设置为0.8。这种分割方案既减少了后期分类的数据量,同时也保存了相应的光谱信息,可以获得最佳的分割结果。
2.2.2 种群排泄物提取
种群排泄物在可见光波段（0.45~0.70 μm）的反射率约为65%,在0.86 μm处反射率最大（约70%）,而在0.86~1.62 μm陡然下降,当波长大于1.62 μm时,反射率仅约为3%左右（图2）。因此,本文利用种群排泄物在0.86 μm和1.62 μm处分别取得反射率极大值和极小值的这一特性,利用比值指数Normalized difference infrared index (NDII) 来扩大二者的差距,使种群排泄物在影像上得到增强,同时抑制其他地物,该指数进行了归一化处理,将数值范围统一到-1~1范围内,一定程度上可以消除地形的影响[22]。NDII指数定义如下[23-24]：
NDII = ( NIR - SWIR ) ( NIR + SWIR ) （6）
式中：NIR和SWIR分别代表近红外和短波红外波段反射率。
Fig. 2 Spectral reflectance of major ground objects in Antarctica

图2 南极主要地物光谱反射率

然而,NDII只考虑了岩石和水体的影响,无法与雪区分。雪同样在0.86~1.62 μm处存在“陡坡”,并且反差比种群排泄物更大。显然,使用NDII来提取种群排泄物会受到雪的强烈干扰,无法获取满意的结果。但是,种群排泄物和雪的反射率在蓝、红波段存在差异,种群排泄物在蓝波段处的反射率大于红波段反射率,而雪恰恰相反,因此构建差值指数EI（Extraction Index）可以将雪和种群排泄物区分开。这样降低了雪对种群排泄物提取的干扰,有利于种群排泄物的精确提取。差值指数EI定义如下：
EI = R - B （7）
式中：R和B分别代表红色和蓝色波段反射率。有学者直接采用此指数进行种群栖息地的确认[13],但其是在通过目视判读已选择疑似种群栖息地的基础上进行的,若直接使用该指数岩石会对提取结果产生强烈干扰。
综上,当分割对象满足NDII>0.6且EI>0时,可判定为种群排泄物;5 km以内的种群排泄物认为产生于同一种群;提取同一种群排泄物的几何中心作为该种群栖息地的位置。

3 结果与讨论

3.1 结果

图3给出了该方法应用于Astrid（8.3° E,69.96° S）处的示例,数据信息：LE71691092009320SGS00。图3（a）表示该地区真彩色合成影像,可以发现帝企鹅种群特征比较明显。图3（b）表示应用上述光谱指数组合后的提取结果,结果表明该方法可以有效地识别种群特征。
Fig. 3 One example about areas covered by faeces in real-color image and the extraction result

图3 帝企鹅种群排泄物真彩色影像和提取结果示例

基于2009年南极大陆海岸带195景Landsat 7 ETM+卫星影像,共计发现38个帝企鹅种群栖息地（49景影像发现帝企鹅栖息地）。对比Fretwell等2002年获取的帝企鹅种群信息,本文定位了7个新的帝企鹅种群栖息地,并且确认和重新定位了31个原先记录的帝企鹅种群栖息地（表1）,2个有记录的种群栖息地（Amundsen Bay 和Ledda Bay）已经不存在或是迁徙到别处。多数种群栖息地（除Thuston Glacier, Luitpold, Sanae, Gould, Ragnhild和Beaufort Island）位置并未发生明显的偏移（约几百米左右）,而由于当地气候等原因6个帝企鹅种群发生了较大的迁徙（大于8 km）。2个种群栖息地（Amundsen Bay 和Ledda Bay）未发现,其中Amundsen Bay历史记录帝企鹅数目较少,可能由于面积太小而未监测到。新发现的和未发现的种群栖息地均得到其他学者研究结果的佐证[19],即新发现的种群栖息地存在,而未发现的栖息地消失。然而,或许依然存在其他因面积较小从而未发现的栖息地,但小种群的数目是有限的,因为其难以有效地抱团取暖从而孵卵繁殖。
帝企鹅种群栖息地广泛分布于南极大陆海岸带周边,其中以罗斯海和威德尔海分布最为密集（图4）,占全部种群栖息地的52%左右。在西太平洋分布和阿蒙森/别林斯高晋海分布较少,这可能与当地地势有关,但新发现的帝企鹅种群栖息地（Bowman Island, Dibble Glacier , Point Geologie, Cape Crozier, Brownson Islands和Rupert Coast）也大多处于该区域。2个消失的种群栖息地（Amundsen Bay 和Ledda Bay）分别位于别林斯高晋海和印度洋,但其周围存在其他种群栖息地,可能迁徙到了别的区域。总体看来,帝企鹅种群栖息地分布广泛,新增栖息地和消失栖息地并没有明显的特征,这需要结合当地的气候和海冰条件进行进一步分析。
Tab. 1 Details information of emperor penguin colonies in 2009

表1 2009年南极主要帝企鹅种群栖息地

地点 经度 纬度 影像时间（月/年） 与2002年位置的距离/km
Cape Colbeck 158°W 77°S 10/2009 1.49
Rupert Coast 143°W 75°S 10/2008
Thuston Glacier 126°W 74°S 10/2009 10.32
Bear Peninsula 110°W 74°S 11/2009 3.27
Brownson Islands 104°W 74°S 11/2009
Noville Peninsula 98°W 72°S 11/2009 0.00
Smyley 79°W 72°S 11/2009 1.16
Smith 61°W 74°S 10/2009 1.26
Snowhill 57°W 65°S 10/2009 0.96
Gould 48°W 78°S 10/2009 9.14
Luitpold 34°W 77°S 11/2009 21.50
Dawson 27°W 76°S 10/2009 3.16
Halley 27°W 76°S 10/2009 6.77
Stancomb 23°W 74°S 10/2009 4.93
Drescher 19°W 73°S 10/2009 7.95
Riiser 15°W 72°S 10/2009 2.33
Atka 8°W 71°S 09/2009 1.11
Sanae 1°W 70°S 10/2009 49.72
Princess Astrid Coast 8°E 70°S 11/2009 1.12
Ragnhild 27°E 70°S 10/2009 8.62
Gunnerus 34°E 69°S 10/2009 3.59
Kloa Point 57°E 67°S 10/2009 0.68
Auster 64°E 67°S 10/2009
Cape Darnley 70°E 68°S 10/2009 0.16
Amanda Bay 77°E 69°S 10/2009 0.12
Haswell Island 93°E 67°S 08/2009 1.11
Shackleton Ice Shelf 96°E 65°S 10/2009
Bowman Island 103°E 65°S 10/2009
Dibble Glacier 135°E 66°S 10/2009
Point Geologie 140°E 67°S 10/2009 0.44
Mertz Glacier Tongue 147°E 67°S 11/2009 8.48
Davis Bay 158°E 69°S 10/2009 3.86
Cape Washington 165°E 75°S 10/2009 1.15
Beaufort Island 167°E 76°S 10/2009 60.07
Franklin Island 168°E 76°S 10/2009 0.80
Cape Crozier 169°E 77°S 10/2009
Coulman Island 170°E 73°S 10/2009 1.44
Cape Roget 171°E 72°S 10/2009 1.55
Fig. 4 Spatial distribution of 38 emperor penguin colonies in 2009 in this study

图4 2009年南极38个帝企鹅种群栖息地的空间分布

3.2 精度分析

Fretwell等2002年获取了首个全面的南极帝企鹅种群栖息地的分布信息[13],将本文获取的2009年帝企鹅栖息地的数目和分布信息与2002年所获取的结果进行对比以获取该方法的精度并研究种群栖息地分布变化的规律。Fretwell等获取了2002年南极共计33个帝企鹅种群栖息地,并与历史记录进行了匹配分析,得到的结果较为全面、准确。
将本文获取的种群栖息地与2002年的结果进行位置匹配,共匹配31个种群栖息地,栖息地位置的平均偏差为7.04 km,标准差为13.60 km,发现7个新种群栖息地,2个栖息地没有发现。本文利用正确率来检验提取结果的准确性,计算公式如下：
正确率 = 正确栖息地个数 栖息地总个数 （8）
与2002年的结果对比,该方法成功识别了94%的种群栖息地。结果表明该方法可以准确、全面地识别种群排泄物从而确定其种群位置,确认原先存在的栖息地和发现新的栖息地。

3.3 帝企鹅种群栖息地变化的气候特征

Fretwell等认为帝企鹅种群栖息地广泛的分布于南极大陆海岸带周边,而地势险峻、裂冰现象频发的区域如Banzare Coast等地较少有分布[12],这与本文的研究结果一致。基于2009年南极海岸带帝企鹅种群栖息地的分布,使得分析帝企鹅种群的分布与海冰、气温和纬度位置等环境因素的关系成为可能。厄尔尼诺和南方涛动是影响南极生态系统的两种最主要的气候变化模式,它们之间的复杂关联随时间而变化,帝企鹅种群相应的反应也随之变化。Ainley等认为全球气温上升2 ℃,南纬70°以北的帝企鹅种群栖息地将会消失[25]。据此评估,约有15个种群栖息地将会面临迁徙或是消失的风险 （图5）,约占全部种群数目的39%。帝企鹅应对气候变化的方式有2种：消失或是适应。古生态学研究表明帝企鹅种群会选择迁徙到适合生存的栖息 地[26-27]。新增的栖息地大多处于气温相对较低的区域,而2个消失的种群栖息地Amundsen Bay 和Ledda Bay的冬季平均气温约为-10~-6℃,这个气温几乎是适宜帝企鹅生存的海冰的极限温度（图6）。多年来在Point Geologie处的帝企鹅数目保持稳定,而尽管在Gunnerus多年来数目持续在减少,但仍然维持在1980年左右的水平[28]。这些区域冬季平均气温均比种群消失的区域低,因此帝企鹅种群并未受到很大的生存威胁。但最近有研究显示,未来气候变化对海冰的作用将会使Point Geologie处的帝企鹅数量在2100年左右急剧下降[2]。由于人类活动的影响,南极大陆整体气温上升[29],但在部分地区气温却稍有下降[30],因此精确分析气温对帝企鹅种群的影响需要构建区域性、较为复杂的气候模型来进行。
Fig. 5 Distribution of emperor penguin colonies along latitude in 2009

图5 2009年南极帝企鹅种群纬度位置的分布

Fig. 6 Distribution of average air temperature and ice concentration in emperor penguin colonies in winter

图6 南极帝企鹅种群位置的冬季平均气温和冬季海冰密集度分布

帝企鹅种群的分布对海冰密集度的变化十分敏感,多数帝企鹅种群分布在海冰密集度较高的地区,低密集度区域种群栖息地倾向于迁徙或消失,而新增的7个栖息地多数海冰密集度较高（图6）。尽管帝企鹅种群受气候变化的影响,但不同区域受到的影响是不同的。在罗斯海海域,随着海冰面积的增长和密集度的增加,该地区种群数量稳定或略有增加,而在西印度洋海冰密集度有所降低,种群数目逐年减少[3,28]。Dion Islands和Snowhill是纬度最低的2个帝企鹅种群栖息地,也是受全球变暖影响最大的区域。Dion islands由于帝企鹅数目过少或是已经迁徙已无法在本文的研究中发现,Snowhill依然存在较为明显的图像特征,但其仍然是最易受气候变化影响的区域。
评估气候变化对帝企鹅种群的影响需要进行定时、区域性的数目调查,笼统的分析并不能得到准确的结果。气温、海冰密集度、海冰分布和风速等都会对帝企鹅种群带来影响,尽管其如何具体影响帝企鹅种群还不得而知,但帝企鹅种群的确已经对气候变化做出了反应,具体反应需要在未来气候变化的预测中评估出来。就发现帝企鹅种群的卫星影像的获取时间而言,2002年卫星影像多获取于11月和12月,然而2009年影像多获取于10月和1月,12月的卫星影像几乎已不能发现帝企鹅种群栖息地,这也间接说明了气候变化对帝企鹅生活习性的影响。预计随着部分区域气温的上升和海冰密集度的变化[31],帝企鹅种群的地理分布会向极地移动,随之而来的是分布范围的收缩,由此也会造成生活习性（如捕食时间和效率）的变化。

4 结论与讨论

根据帝企鹅种群排泄物的在近红外和短波红外反射率的极大差异,结合其与雪在蓝、红波段反射率的高低,本文提出了一种基于NDII和EI两种光谱指数进行种群排泄物的自动提取进而精确确定其种群位置的方法。根据195景覆盖南极大陆海岸带Landsat 7 ETM+卫星影像,获取了2009年南极大陆共38个帝企鹅种群栖息地,广泛的分布于南极大陆海岸带周边,其中以罗斯海和威德尔海最密集。
相比于2002年的研究结果,帝企鹅种群的数量有所上升,这可能是因为已往的研究存在未曾发现的种群或是产生了新的帝企鹅种群。考虑到全球变暖和南极气候的复杂性,种群数目的增加有可能是因为一个种群分裂成多个种群,而帝企鹅实际数量并没有增加甚至减少,但这需要更详细的帝企鹅种群数据进行佐证。对比2002年的研究结果,新发现7个种群栖息地,25个种群未发生明显的偏移,6个种群栖息地发生了较大的偏移,未发现的2个种群栖息地已经迁徙或是不存在。帝企鹅种群每年的栖息地位置不会发生较大的变化,发生一定程度上的偏移是合理的。增加和消失的种群栖息地均与当地的气候相关,增加的种群大多出现在气温较低和海冰密集度较高的区域,而消失的种群栖息地情况大致相反。
种群栖息地提取的正确率为94%,提取效果受卫星影像质量和种群规模的制约。光学影像极易受云层的影响,合适的影像对研究帝企鹅种群变化具有重要作用。受限于大多数遥感影像的分辨率,规模稍小的种群容易出现漏分的情况。虽然基于对象层面利用典型的光谱特性可以有效地识别种群排泄物,但仍然会受岩石、裸土等地物的干扰,研究更具有区分度的种群特征是未来研究的重要方向。
种群分布和数目的变化与气候息息相关,帝企鹅种群偏向于海冰密集度高和气温较低的区域,帝企鹅种群呈现向极点移动且分布范围逐渐收缩的趋势。虽然帝企鹅种群与气候变化的具体关系还不得而知,但部分种群的迁徙已经表明了帝企鹅种群应对气候变化所作出的反应,具体关系需要更详实的种群资料和更准确、复杂的气候分析来进行研究。未来可以选用更高空间分辨率的遥感影像进一步发展帝企鹅种群的识别方法,提高识别正确率并尝试进行帝企鹅数目的估算。研究帝企鹅种群数目和栖息地位置的变化与气候变化的具体关系,对研究气候变化对关键物种（帝企鹅）的影响具有深刻的意义。

The authors have declared that no competing interests exist.

References

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Fretwell P T, Trathan P N.Penguins from space: Faecal stains reveal the location of emperor penguin colonies[J]. Global ecology and biogeography, 2009,18(5):543-552.Aim To map and assess the breeding distribution of emperor penguins ( Aptenodytes forsteri ) using remote sensing. Location Pan-Antarctic. Methods Using Landsat ETM satellite images downloaded from the Landsat Image Mosaic of Antarctica (LIMA), we detect faecal staining of ice by emperor penguins associated with their colony locations. Emperor penguins breed on sea ice, and their colonies exist in situ between May and December each year. Faecal staining at these colony locations shows on Landsat imagery as brown patches, the only staining of this colour on sea ice. This staining can therefore be used as an analogue for colony locations. The whole continental coastline has been analysed, and each possible signal has been identified visually and checked by spectral analysis. In areas where LIMA data are unsuitable, freely available Landsat imagery has been supplemented. Results We have identified colony locations of emperor penguins at a total of 38 sites. Of these, 10 are new locations, and six previously known colony locations have been repositioned (by over 10 km) due to poor geographical information in old records. Six colony locations, all from old or unconfirmed records, were not found or have disappeared. Main conclusions We present a new pan-Antarctic species distribution of emperor penguins mapped from space. In one synoptic survey we locate extant emperor penguin colonies, a species previously poorly mapped due to its unique breeding habits, and provide a vital geographical resource for future studies of an iconic species believed to be vulnerable to future climate change.

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LaRue M A, Kooyman G, Lynch H J, et al. Emigration in emperor penguins: implications for interpretation of long-term studies[J]. Ecography, 2015,38(2):114-120.Site fidelity is an important evolutionary trait to understand, as misinterpretation of philopatric behavior could lead to confusion over the key drivers of population dynamics and the environmental or anthropogenic factors influencing populations. Our objective was to explore the hypothesis that emperor penguins are strictly philopatric using satellite imagery, counts from aerial photography, and literature reports on emperor penguin distributions. We found six instances over three years in which emperor penguins did not return to the same location to breed. We also report on one newly-discovered colony on the Antarctic Peninsula that may represent the relocation of penguins from the Dion Islands, recently confirmed as having been abandoned. Using evidence from aerial surveys and the historical literature, we suggest that emigration may have been partly responsible for the population decline at Pointe G茅ologie during the 1970s. Our study is the first to use remote sensing imagery to suggest that emperor penguins can and do move between, and establish new, colonies. Metapopulation dynamics of emperor penguins have not been previously considered and represent an exciting, and important, avenue for future research. Life history plasticity is increasingly being recognized as an important aspect of climate change adaptation, and in this regard our study offers new insight for the long-term future of emperor penguins.

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Fretwell P T, Trathan P N, Wienecke B, et al.Emperor penguins breeding on iceshelves[J]. PloS one, 2014,9(1):e85285.We describe a new breeding behaviour discovered in emperor penguins; utilizing satellite and aerial-survey observations four emperor penguin breeding colonies have been recorded as existing on ice-shelves. Emperors have previously been considered as a sea-ice obligate species, with 44 of the 46 colonies located on sea-ice (the other two small colonies are on land). Of the colonies found on ice-shelves, two are newly discovered, and these have been recorded on shelves every season that they have been observed, the other two have been recorded both on ice-shelves and sea-ice in different breeding seasons. We conduct two analyses; the first using synthetic aperture radar data to assess why the largest of the four colonies, for which we have most data, locates sometimes on the shelf and sometimes on the sea-ice, and find that in years where the sea-ice forms late, the colony relocates onto the ice-shelf. The second analysis uses a number of environmental variables to test the habitat marginality of all emperor penguin breeding sites. We find that three of the four colonies reported in this study are in the most northerly, warmest conditions where sea-ice is often sub-optimal. The emperor penguin's reliance on sea-ice as a breeding platform coupled with recent concerns over changed sea-ice patterns consequent on regional warming, has led to their designation as "near threatened" in the IUCN red list. Current climate models predict that future loss of sea-ice around the Antarctic coastline will negatively impact emperor numbers; recent estimates suggest a halving of the population by 2052. The discovery of this new breeding behaviour at marginal sites could mitigate some of the consequences of sea-ice loss; potential benefits and whether these are permanent or temporary need to be considered and understood before further attempts are made to predict the population trajectory of this iconic species.

DOI PMID

[16]
Yu Q, Gong P, Clinton N, et al.Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery[J]. Photogrammetric Engineering and Remote Sens, 2006,72(7):799-811.In this paper, we evaluate the capability of the high spatial resolution airborne Digital Airborne Imaging System (DAIS) imagery for detailed vegetation classification at the alliance level with the aid of ancillary topographic data. Image objects as minimum classification units were generated through the Fractal Net Evolution Approach (FNEA) segmentation using eCognition software. For each object, 52 features were calculated including spectral features, textures, topographic features, and geometric features. After statistically ranking the importance of these features with the classification and regression tree algorithm (CART), the most effective features for classification were used to classify the vegetation. Due to the uneven sample size for each class, we chose a non-parametric (nearest neighbor) classifier. We built a hierarchical classification scheme and selected features for each of the broadest categories to carry out the detailed classification, which significantly improved the accuracy. Pixel-based maximum likelihood classification (MLC) with comparable features was used as a benchmark in evaluating our approach. The object-based classification approach overcame the problem of salt-and-pepper effects found in classification results from traditional pixel-based approaches. The method takes advantage of the rich amount of local spatial information present in the irregularly shaped objects in an image. This classification approach was successfully tested at Point Reyes National Seashore in Northern California to create a comprehensive vegetation inventory. Computer-assisted classification of high spatial resolution remotely sensed imagery has good potential to substitute or augment the present ground-based inventory of National Park lands.

DOI

[17]
Li Q, Wang C, Zhang B, et al.Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data[J]. Remote Sensing, 2015,7(12):16091-16107.Cropland mapping via remote sensing can provide crucial information for agri-ecological studies. Time series of remote sensing imagery is particularly useful for agricultural land classification. This study investigated the synergistic use of feature selection, Object-Based Image Analysis (OBIA) segmentation and decision tree classification for cropland mapping using a finer temporal-resolution Landsat-MODIS Enhanced time series in 2007. The enhanced time series extracted 26 layers of Normalized Difference Vegetation Index (NDVI) and five NDVI Time Series Indices (TSI) in a subset of agricultural land of Southwest Missouri. A feature selection procedure using the Stepwise Discriminant Analysis (SDA) was performed, and 10 optimal features were selected as input data for OBIA segmentation, with an optimal scale parameter obtained by quantification assessment of topological and geometric object differences. Using the segmented metrics in a decision tree classifier, an overall classification accuracy of 90.87% was achieved. Our study highlights the advantage of OBIA segmentation and classification in reducing noise from in-field heterogeneity and spectral variation. The crop classification map produced at 30 m resolution provides spatial distributions of annual and perennial crops, which are valuable for agricultural monitoring and environmental assessment studies.

DOI

[18]
Miao X., Xie H., Ackley S.F., et al. “Object-based detection of Arctic sea ice and melt ponds using high spatial resolution aerial photographs[J]. Cold Regions Science and Technoogy, 2015,119:211-222.High resolution aerial photographs used to detect and classify sea ice features can provide accurate physical parameters to refine, validate, and improve climate models. However, manually delineating sea ice and melt ponds is time-consuming and labor-intensive. In this study, an object-based classification algorithm is developed to automatically extract sea ice and melt ponds efficiently from 163 aerial photographs taken during the Chinese National Arctic Research Expedition in summer 2010 (CHINARE 2010) in the Arctic Pacific Sector. The photographs are selected from 599 cloud-free photographs based on their image quality and representativeness in the marginal ice zone (MIZ). The algorithm includes three major steps: (1) the image segmentation groups the neighboring pixels into objects according to the similarity of spectral and textural information; (2) the random forest ensemble classifier distinguishes four general classes: water, general submerged ice (GSI, including melt ponds and submerged ice along ice edges), shadow, and ice/snow; and (3) the polygon neighbor analysis further separates melt ponds and submerged ice from the GSI according to their spatial relationships. The overall classification accuracy for the four general classes is 95.5% based on 178 ground reference objects. Furthermore, the producer's accuracy of 90.8% and user's accuracy of 91.8% are achieved for melt pond detection through 98 independent reference objects. For the 163 photos examined, a total of 19,438 melt ponds larger than 102m 2 are detected, with a pond density of 867.202km 61022 , mean pond size of 32.602±020.0302m 2 , and mean pond fraction of 0.0602±020.006; a total of 42,468 ice floes are detected, with the mean floe size of 173.302±020.102m 2 (majority in 1–3002m 2 ) and mean ice concentration of 46.102±020.5% (ranging from 18.6–98.6%). These results matched well with ship-based visual observations in the MIZ in the same area and time. The method presented in the paper can be applied to data sets of high spatial resolution Arctic sea ice photographs for deriving detailed sea ice concentration, floe size, and melt pond distributions over wider regions, and extracting sea ice physical parameters and their corresponding changes between years.

DOI

[19]
Fretwell P T, LaRue M A, Morin P, et al. An emperor penguin population estimate: the first global, synoptic survey of a species from space[J]. PLoS One, 2012,7(4): e33751.Abstract Our aim was to estimate the population of emperor penguins (Aptenodytes fosteri) using a single synoptic survey. We examined the whole continental coastline of Antarctica using a combination of medium resolution and Very High Resolution (VHR) satellite imagery to identify emperor penguin colony locations. Where colonies were identified, VHR imagery was obtained in the 2009 breeding season. The remotely-sensed images were then analysed using a supervised classification method to separate penguins from snow, shadow and guano. Actual counts of penguins from eleven ground truthing sites were used to convert these classified areas into numbers of penguins using a robust regression algorithm.We found four new colonies and confirmed the location of three previously suspected sites giving a total number of emperor penguin breeding colonies of 46. We estimated the breeding population of emperor penguins at each colony during 2009 and provide a population estimate of ~238,000 breeding pairs (compared with the last previously published count of 135,000-175,000 pairs). Based on published values of the relationship between breeders and non-breeders, this translates to a total population of ~595,000 adult birds.There is a growing consensus in the literature that global and regional emperor penguin populations will be affected by changing climate, a driver thought to be critical to their future survival. However, a complete understanding is severely limited by the lack of detailed knowledge about much of their ecology, and importantly a poor understanding of their total breeding population. To address the second of these issues, our work now provides a comprehensive estimate of the total breeding population that can be used in future population models and will provide a baseline for long-term research.

DOI PMID

[20]
Zhu Z, Woodcock C E.Object-based cloud and cloud shadow detection in Landsat imagery[J]. Remote Sensing of Environment, 2012,118:83-94.A new method called Fmask (Function of mask) for cloud and cloud shadow detection in Landsat imagery is provided. Landsat Top of Atmosphere (TOA) reflectance and Brightness Temperature (BT) are used as inputs. Fmask first uses rules based on cloud physical properties to separate Potential Cloud Pixels (PCPs) and clear-sky pixels. Next, a normalized temperature probability, spectral variability probability, and brightness probability are combined to produce a probability mask for clouds over land and water separately. Then, the PCPs and the cloud probability mask are used together to derive the potential cloud layer. The darkening effect of the cloud shadows in the Near Infrared (NIR) Band is used to generate a potential shadow layer by applying the flood-fill transformation. Subsequently, 3D cloud objects are determined via segmentation of the potential cloud layer and assumption of a constant temperature lapse rate within each cloud object. The view angle of the satellite sensor and the illuminating angle are used to predict possible cloud shadow locations and select the one that has the maximum similarity with the potential cloud shadow mask. If the scene has snow, a snow mask is also produced. For a globally distributed set of reference data, the average Fmask overall cloud accuracy is as high as 96.4%. The goal is development of a cloud and cloud shadow detection algorithm suitable for routine usage with Landsat images.

DOI

[21]
赵子莹,刘臻,宫鹏.基于对象的南极大陆边缘浮冰自动提取技术研究[J].中国科学:地球科学, 2012,1:8. 南极大陆边缘区域浮冰提取对于南极浮冰变化以及全球变化的研究有重要意义,提出一种基于区域增长图像分割技术的南极大陆边缘浮冰信息自动提取方法.结合浮冰的灰度、轮廓、位置关系等信息进行合并和验证,有效解决图像分割过程中的过度分割以及分割不足的问题.还提出一种基于像素检测的小面积浮冰提取算法,有效提取像素个数小于5的浮冰目标.分别选取了LandSatETM+数据、中国环境减灾卫星HJ1BCCD1数据和MODIS1B数据,进行大范围的实验测试,并对不同数据的实验结果进行对比,结果表明三种数据的浮冰面积提取精度均高于81%,其中ETM+数据和HJ1B数据的提取精度大于90%.实验结果说明面向对象的信息提取方法不仅可以得到整个区域浮冰总的面积和数量,还可以比较准确地得到单个浮冰的详细信息.

DOI

[ Zhao Z Y, Liu Z, Gong P. Automatic extraction of floating ice at Antractic continental margin from remotely sensed imagery using object-based segmentation[J]. Sci China Earch Sci, 2012,1:8. ]

[22]
徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595.在对M cfeeters提出的归一化差异水体指数(NDWI)分析的基础上,对构成该指数的波长组合进行了修改,提出了改进的归一化差异水体指数MNDWI(M odified NDWI),并分别将该指数在含不同水体类型的遥感影像进行了实验,大部分获得了比NDWI好的效果,特别是提取城镇范围内的水体。NDWI指数影像因往往混有城镇建筑用地信息而使得提取的水体范围和面积有所扩大。实验还发现MNDWI比NDWI更能够揭示水体微细特征,如悬浮沉积物的分布、水质的变化。另外,MNDWI可以很容易地区分阴影和水体,解决了水体提取中难于消除阴影的难题。

DOI

[ Xu H Q.A study on information extraction of water body with the modified normalized difference water index (MNDWI)[J]. Journal of remote sensing, 2005,9(5):589-595. ]

[23]
Kimes D S, Markham B L, Tucker C J, et al.Temporal relationships between spectral response and agronomic variables of a corn canopy[J]. Remote Sensing of Environment, 1981,11(81):401-411.There is growing interest in employing hand-held radiometry as a nondestructive research tool in lieu of or support of more tedious vegetation measurements. The objective of this study was to evaluate such techniques on corn. The spectral radiances from corn plots 1.8 m in diameter were measured using a three-band radiometer elevated 3.7 m above the ground. The three spectral bands used corresponded to NASA'S Landsat-D Thematic Mapper bands TM3 (0.63–0.69 μm), TM4 (0.76–0.90 μm), and TM5 (1.55–1.75 μm). Periodically throughout the growing season a plot was selected and radiometrically measured then harvested for measurement of several agronomic variables. By the end of the growing season, a total of 43 plots had been measured with solar zenith angles ranging between 16 and 44°. Significant relationships were found between various combinations of the radiance data and the wet and dry total biomass, plant height, fraction of ground covered by plants, wet and dry green leaf biomass, green leaf area index, fraction of leaf chlorosis, and total plant water content. Some of these relationships were found to be redundant since several of the agronomic variables were highly correlated to one another. In addition, the TM5 band did not provide any marked improvement in the relationships to the agronomic variables. The relationships between the radiance data and agronomic variables represent a nondestructive remote sensing technique for researching the growth of corn canopies.

DOI

[24]
Hardisky, M. S, Klemas V, et al. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina Alterniflora canopies[J]. Photogrammetric Engineering & Remote Sensing, 1983,48(1):77-84.ABSTRACT Spectra of Spartina alterniflora were measure under different salinity, growth form and moisture conditions.

[25]
Ainley D., Russell J., Jenouvrier S..

[26]
Emslie S D, Fraser W, Smith R C, et al.Abandoned penguin colonies and environmental change in the Palmer Station area, Anvers Island, Antarctic Peninsula[J]. Antarctic Science, 1998,10(3):257-268.Abstract Six abandoned colonies of Adélie penguin (Pygoscelis adeliae) were excavated near Palmer Station, Anvers Island, Antarctic Peninsula, to investigate the occupation history of this species. Sediments from each site yielded abundant fish bones and otoliths and squid beaks that represent prey remains deposited by penguins during the nesting period. Radiocarbon analyses indicate that colony occupation began prior to the Little Ice Age (LIA; 1500–1850 AD), with the oldest site dating to 644 yrs before present (BP; average reservoir-corrected date with Is range, 603–679 yr BP). Food remains indicate that the non-euphausiid prey of penguins consisted primarily of a mesopelagic squid (Psychroteuthis glacialis) and two species of fish (Pleuragramma antarcticun and Electrona antarctica). The relative abundance of the first two prey taxa varied significantly among six sites (X2>34.6; df = 10; P <0.001) with colonies dating prior to the LIA having greater representation of squid, and less of silverfish, than those occupied during the LIA. Data from control excavations at three modern colonies indicate a diet similar to that of the pre-LIA sites. These results suggest that Adélie penguins may have changed their diet in response to warming and cooling cycles in the past. In addition, only Adélie penguins are known to have nested in the Palmer Station area prior to the l950s; gentoo (Pygoscelis papua) and chinstrap (P. antarctica) penguins now breeding in this region have expanded their ranges southward in the Peninsula within the past 50 yrs, in correlation with pronounced regional warming.

DOI

[27]
Smith R C, Ainley D, Baker K, et al.Marine ecosystem sensitivity to climate change historical observations and paleoecological records reveal ecological transitions in the Antarctic Peninsula region[J]. BioScience, 1999,49(5):393-404.Summarizes data on climate variability and trends in the western Antarctic Peninsula (WAP) region. Analysis of the data in the context of long-term climate variability in the last 8000 years of the Holocene period; Evidence for WAP climate change; Ecological responses to climate change; Mechanisms of climate change; Future challenges.

DOI

[28]
Kato A, Watanabe K, Naito Y.Population changes of Adelie and emperor penguins along the Prince Olav Coast and on the Riiser-Larsen Peninsula[J]. Polar Bioscience, 2004, 17:117-122.ABSTRACT The trends of penguin populations are thought to be reliable indicators of ecosystem changes in the Southern Ocean. There are seven Ad茅lie penguin colonies and one emperor penguin colony along the Prince Olav Coast and one emperor penguin colony on the Riiser-Larsen Peninsula. We compiled and analyzed the available data collected by airborne and ground census between +31+ and ,*** in order to determine the breeding status of penguins in this area. Ad茅lie penguin populations increased at two colonies; no apparent trends were observed at other colonies. Emperor penguin populations were high in the mid-+33*s and suddenly decreased in ,***. Populations need to be carefully monitored in the coming years.

[29]
Gillett N P, Stone D A, Stott P A, et al.Attribution of polar warming to human influence[J]. Nature Geoscience, 2008,1(11):750-754.The polar regions have long been expected to warm strongly as a result of anthropogenic climate change, because of the positive feedbacks associated with melting ice and snow. Several studies have noted a rise in Arctic temperatures over recent decades, but have not formally attributed the changes to human influence, owing to sparse observations and large natural variability. Both warming and cooling trends have been observed in Antarctica, which the Intergovernmental Panel on Climate Change Fourth Assessment Report concludes is the only continent where anthropogenic temperature changes have not been detected so far, possibly as a result of insufficient observational coverage. Here we use an up-to-date gridded data set of land surface temperatures and simulations from four coupled climate models to assess the causes of the observed polar temperature changes. We find that the observed changes in Arctic and Antarctic temperatures are not consistent with internal climate variability or natural climate drivers alone, and are directly attributable to human influence. Our results demonstrate that human activities have already caused significant warming in both polar regions, with likely impacts on polar biology, indigenous communities, ice-sheet mass balance and global sea level.

DOI

[30]
Turner J, Colwell S R, Marshall G J, et al.Antarctic climate change during the last 50 years[J]. International Journal of Climatology, 2005,25(3):279-294.The Reference Antarctic Data for Environmental Research (READER) project data set of monthly mean Antarctic near-surface temperature, mean sea-level pressure (MSLP) and wind speed has been used to investigate trends in these quantities over the last 50 years for 19 stations with long records. Eleven of these had warming trends and seven had cooling trends in their annual data (one station had too little data to allow an annual trend to be computed), indicating the spatial complexity of change that has occurred across the Antarctic in recent decades. The Antarctic Peninsula has experienced a major warming over the last 50 years, with temperatures at Faraday/Vernadsky station having increased at a rate of 0.56 掳C decade-1 over the year and 1.09 掳C decade-1 during the winter; both figures are statistically significant at less than the 5% level. Overlapping 30 year trends of annual mean temperatures indicate that, at all but two of the 10 coastal stations for which trends could be computed back to 1961, the warming trend was greater (or the cooling trend less) during the 1961-90 period compared with 1971-2000. All the continental stations for which MSLP data were available show negative trends in the annual mean pressures over the full length of their records, which we attribute to the trend in recent decades towards the Southern Hemisphere annular mode (SAM) being in its high-index state. Except for Halley, where the trends are constant, the MSLP trends for all stations on the Antarctic continent for 1971-2000 were more negative than for 1961-90. All but two of the coastal stations have recorded increasing mean wind speeds over recent decades, which is also consistent with the change in the nature of the SAM.

DOI

[31]
周秀骥,陆龙骅,卞林根,等.南极地区温度、海冰和臭氧的变化特征[J].自然科学进展,1997(4):78-84.

[ Zhou X J, Lu L H, Bian L G et al. Variation characteristics of temperature, sea ice and ozone in the Antarctic[J]. Progess in natural science, 1997,4:78-84. ]

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