Predicting bird phenology from space: satellite‐derived vegetation green‐up signal uncovers spatial variation in phenological synchrony between birds and their environment

Population‐level studies of how tit species (Parus spp.) track the changing phenology of their caterpillar food source have provided a model system allowing inference into how populations can adjust to changing climates, but are often limited because they implicitly assume all individuals experience similar environments. Ecologists are increasingly using satellite‐derived data to quantify aspects of animals' environments, but so far studies examining phenology have generally done so at large spatial scales. Considering the scale at which individuals experience their environment is likely to be key if we are to understand the ecological and evolutionary processes acting on reproductive phenology within populations. Here, we use time series of satellite images, with a resolution of 240 m, to quantify spatial variation in vegetation green‐up for a 385‐ha mixed‐deciduous woodland. Using data spanning 13 years, we demonstrate that annual population‐level measures of the timing of peak abundance of winter moth larvae (Operophtera brumata) and the timing of egg laying in great tits (Parus major) and blue tits (Cyanistes caeruleus) is related to satellite‐derived spring vegetation phenology. We go on to show that timing of local vegetation green‐up significantly explained individual differences in tit reproductive phenology within the population, and that the degree of synchrony between bird and vegetation phenology showed marked spatial variation across the woodland. Areas of high oak tree (Quercus robur) and hazel (Corylus avellana) density showed the strongest match between remote‐sensed vegetation phenology and reproductive phenology in both species. Marked within‐population variation in the extent to which phenology of different trophic levels match suggests that more attention should be given to small‐scale processes when exploring the causes and consequences of phenological matching. We discuss how use of remotely sensed data to study within‐population variation could broaden the scale and scope of studies exploring phenological synchrony between organisms and their environment.     

Characterization of moorland vegetation and the prediction of bird abundance using remote sensing

Aims To characterize and identify upland vegetation composition and height from a satellite image, and assess whether the resulting vegetation maps are accurate enough for predictions of bird abundance. Location South‐east Scotland, UK. Methods Fine‐taxa vegetation data collected using point samples were used for a supervised classification of a Landsat 7 image, while linear regression was used to model vegetation height over the same image. Generalized linear models describing bird abundance were developed using field‐collected bird and vegetation data. The satellite‐derived vegetation data were substituted into these models and efficacy was examined. Results The accuracy of the classification was tested over both the training and a set of test plots, and showed that more common vegetation types could be predicted accurately. Attempts to estimate the heights of both dwarf shrub and graminoid vegetation from satellite data produced significant, but weak, correlations between observed and predicted height. When these outputs were used in bird abundance–habitat models, bird abundance predicted using satellite‐derived vegetation data was very similar to that obtained when the field‐collected data were used for one bird species, but poor estimates of vegetation height produced from the satellite data resulted in a poor abundance prediction for another. Conclusions This pilot study suggests that it is possible to identify moorland vegetation to a fine‐taxa level using point samples, and that it may be possible to derive information on vegetation height, although more appropriate field‐collected data are needed to examine this further. While remote sensing may have limitations compared with relatively fine‐scale fieldwork, when used at relatively large scales and in conjunction with robust bird abundance–habitat association models, it may facilitate the mapping of moorland bird abundance across large areas.     

The development of a Canadian dynamic habitat index using multi-temporal satellite estimates of canopy light absorbance

Monitoring patterns of fauna diversity across the landscape, both spatially and temporally, presents special challenges due to the dynamic nature of populations and complex interactions with the local and regional environment. One area where progress is being made is the development of relationships between regional biodiversity with indirect indicators or surrogates, such as vegetative production. In this paper we discuss implementation of a dynamic habitat index, originally developed in Australia, to Canadian conditions. The index, based on the fraction of photosynthetically active radiation (fPAR) absorbed by vegetation, a variable which is analogous to green vegetation cover, is derived solely from satellite data. The index utilizes time series of satellite observations of greenness to derive three indicators of the underlying vegetation dynamics; the cumulative annual greenness, the minimum level of perennial cover, and the degree of vegetation seasonality. We apply the index across Canada and compare the three components by ecozones, demonstrating that Canada's terrestrial environment can effectively be clustered into five major dynamic habitat regimes. These range from those with low cumulative greenness and highly seasonal variation in cover, to regimes which have high canopy light absorbance with limited seasonality and continuous annual green cover. By comparing data from multiple years, our analysis indicates that a number of these ecozones have experienced changes in their composition over the past 6 years. We believe this methodology can provide an initial stratification of large areas for biodiversity monitoring and can be used to focus finer scale approaches to specific regions of interest or monitor regions too remote for comprehensive field surveys.     

Differentiating geological fertility derived vegetation zones in Kruger National Park,South Africa,using Landsat and MODIS imagery

Spatial technologies present possibilities for producing frequently updated and accurate habitat maps, which are important in biodiversity conservation. Assemblages of vegetation are equivalent to habitats. This study examined the use of satellite imagery in vegetation differentiation in South Africa's Kruger National Park (KNP). A vegetation classification scheme based on dominant tree species but also related to the park's geology was tested, the geology generally consisting of high and low fertility lithology. Currently available multispectral satellite imagery is broadly either of high spatial but low temporal resolution or low spatial but high temporal resolution. Landsat TM/ETM+ and MODIS images were used to represent these broad categories. Rain season dates were selected as the period when discrimination between key habitats in KNP is most likely to be successful. Principal Component Analysis enhanced vegetated areas on the Landsat images, while NDVI vegetation enhancement was employed on the MODIS image. The images were classified into six field sampling derived classes depicting a vegetation density and phenology gradient, with high (about 89%) indicative classification accuracy. The results indicate that, using image processing procedures that enhance vegetation density, image classification can be used to map the park's vegetation at the high versus low geological fertility zone level, to accuracies above 80% on high spatial resolution imagery and slightly lower accuracy on lower spatial resolution imagery. Rainfall just prior to the image date influences herbaceous vegetation and, therefore, success at image scene vegetation mapping, while cloud cover limits image availability. Small scale habitat differentiation using multispectral satellite imagery for large protected savanna areas appears feasible, indicating the potential for use of remote sensing in savanna habitat monitoring. However, factors affecting successful habitat mapping need to be considered. Therefore, adoption of remote sensing in vegetation mapping and monitoring for large protected savanna areas merits consideration by conservation agencies.     

融入植被结构因子的生态单元制图法在城市生物多样性信息采集中的应用

以前所使用的生态单元制图模型中没有涉及植被时间和空间结构因子,然而一些研究结果显示植被的时空结构对于生物多样性有着重要的影响。因此,设计一个融入植被结构因子的改良城市生态单元制图模型,并且将其应用于瑞典赫尔辛堡市的绿色空间个案研究中,以期能够获得有关生物多样性方面的信息。这个改良的制图方法基于一个生态单元分类系统,此分类系统融入了4个植被结构因子,分别是:植被覆盖的连续性因子、优势乔木种的年龄因子、横向结构因子以及竖向结构因子。公共绿色空间信息的采集借助于全彩色航空照片的分析以及实地调查,生态单元图谱的绘制基于各个层级的生态单元分类重点。通过使用原生林地指示种或林地连续性指示种(AWIS)鉴定林地的连续性以及观测不同的植被结构下动物的分布情况得出,一些含有AWIS的生态单元是长连续性的林地,并且其含有较高的或潜在高的生物多样性;同时,植被的横向和竖向结构以及树木的年龄结构影响着鸟类和哺乳动物的分布、丰富度和多样性。得出融入植被时空结构的生态单元制图法是一个重要的调查城市生物多样性的方法,图谱能够显示出各个生态单元含有生物多样性价值的信息,基于此可以对今后的城市生物多样性保护和提高提出相应的策略。     

Making remote sense of biodiversity: What grassland characteristics make spectral diversity a good proxy for taxonomic diversity?

Aim

The spectral variability hypothesis (SVH) predicts that spectral diversity, defined as the variability of radiation reflected from vegetation, increases with biodiversity. While confirmation of this hypothesis would pave the path for use of remote sensing to monitor biodiversity, support in herbaceous ecosystems is mixed. Methodological aspects related to scale have been the predominant explanation for the mixed support, yet biological characteristics that vary among herbaceous systems may also affect the strength of the relationship. Therefore, we examined the influence of three biological characteristics on the relationship between spectral and taxonomic diversity: vegetation density, spatial species turnover and invasion by non-native species. We aimed to understand when and why spectral diversity may serve as an indicator of taxonomic diversity and be useful for monitoring.

Location

Continental U.S.A.

Time Period

Peak greenness in 2017.

Major Taxa Studied

Grassland and herbaceous ecosystems.

Methods

For nine herbaceous sites in the National Ecological Observatory Network, we calculated taxonomic diversity from field surveys of 20 m × 20 m plots and derived spectral diversity for those same plots from airborne hyperspectral imagery with a spatial resolution of 1 m. The strength of the taxonomic diversity–spectral diversity relationship at each site was subsequently assessed against measurements of vegetation density, spatial species turnover and invasion.

Results

We found a significant relationship between taxonomic and spectral diversity at some, but not all, sites. Spectral diversity was more strongly related to taxonomic diversity in sites with high species turnover and low invasion, but vegetation density had no effect on the relationship.

Main Conclusions

Using spectral diversity as a proxy for taxonomic diversity in grasslands is possible in some circumstances but should not just be assumed based on the SVH. It is important to understand the biological characteristics of a community prior to considering spectral diversity to monitor taxonomic diversity.     

应用间接排序识别盐沼植被的光谱特征:以崇明东滩为例

遥感技术已成为大尺度植被分类的重要手段,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键。该研究选择上海崇明东滩自然保护区的盐沼植物群落为对象,应用ASD地物光谱仪测定其植物群落的光谱反射率,并采用10个小型机载成像光谱仪(CASI)默认植被波段组,应用主分量分析法和相关分析分析了不同群落光谱特征与生态环境因子之间的关系。分析结果表明,间接排序法PCA能够识别盐沼植被中光滩、海三棱 草(Scirpus mariqueter)群落、芦苇(Phragmites australis)群落和互花米草(Spartina alterniflora)等群落的光谱特征,绝大多数盐沼湿地植物群落组成与光谱特征之间有显著的相关,识别效果最好的波段组是736~744 nm、746~753 nm、775~784 nm、815~824 nm和860~870 nm;对光谱反射率影响最大的生态环境因子分别是植物群落的高度和盖度,高程和其它环境因子的影响次之。研究成果可为遥感监测崇明东滩自然保护区内入侵种互花米草的空间分布和扩散规律提供技术支撑,为高光谱遥感影像的影像判读和解译分类以及盐沼湿地植被制图提供科学依据。     

Remote sensing of vegetation cover change in islands of the Kerguelen archipelago

The plant communities in the Iles Kerguelen (South Indian Ocean) have been extensively modified by human activities, particularly through the deliberate release of rabbits, and the intentional or accidental introduction of several plant species. During the 1990 and 2000s, a decrease in precipitation resulted in a drastic reduction of some native plant species and in the increase in alien taxa. To monitor at a wide spatial scale the rapid changes of vegetation cover induced by summer droughts, we developed a method combining field data and satellite image analysis. A long-term field monitoring of plant communities was initiated on five small islands in 1992, and annually continued for over 15?years on a total of 161 line transects. Among these islands, the rabbit??which was the only introduced herbivore??was eradicated on three, remained on one control island, and had never been present on a second control island. We computed a linear model to link remote sensored vegetation indexes to plant cover deduced from line transects in numerous habitat types. After testing 14 vegetation indexes, we used a model based on the normalized difference vegetation index to precisely map the vegetation cover at several dates. A map of differences and spatial statistics indicated that vegetation cover, as a whole, decreased over the 15-year period. This study provides a reliable tool for long-term monitoring of the dynamics of plant cover in relation to climate change on the Iles Kerguelen.     

Mapping and monitoring of coastal wetlands of Çukurova Delta in the Eastern Mediterranean region

The aim of this research was to link vegetation characteristics, such as spatial and temporal distribution, and environmental variables, with land cover information derived from remotely sensed satellite images of the Eastern Mediterranean coastal wetlands of Turkey. The research method was based on (i) recording land cover characteristics by means of a vegetation indicator, and (ii) classifying and mapping coastal wetlands utilizing a Landsat Thematic Mapper (TM) image of Çukurova Deltas in Turkey. Vegetation characteristics of various habitats, such as sand dunes, salt marshes, salty plains and afforestation areas, were identified by field surveys. A Landsat TM image of 4 July 1993 was pre-processed and then classified using the Maximum Likelihood (ML) algorithm and Artificial Neural Networks (ANN). As a result of this supervised classification, the land cover types were classified with a largest accuracy of 90.2% by ANN. The classified satellite sensor imagery was linked to vegetation and bird census data, which were available through literature in a Geographical Information System (GIS) environment to determine the spatial distribution of plant and bird biodiversity in this coastal wetland. The resulting data provide an important baseline for further investigations such as monitoring, change detections and designing conservation policies in this coastal ecosystem.     

不同端元模型下湿地植被覆盖度的提取方法——以北京市野鸭湖湿地自然保护区为例

植被覆盖度作为反映湿地植物生长状况的重要生态学参数,在评估和检测湿地生态环境方面起着关键的作用.以华北内陆典型的淡水湿地——北京市野鸭湖湿地自然保护区为研究对象,中等分辨率的Landsat TM影像为数据源,基于线性光谱混合模型(LSMM)对研究区的植被覆盖度进行了估算.针对湿地植被类型丰富、土地利用类型多样化的特点,利用归一化植被指数(NDVI)在反映植物生长状况、覆盖程度以及区分地表覆盖类型方面的优势,通过对原始Landsat TM影像增加NDVI数据维对影像进行维度扩展,克服了传统研究中通常从Landsat TM影像上提取3-4种端元的局限,经最小噪声分离变换(MNF变换)、纯像元指数(PPI)计算以及人机交互端元选取等一系列运算,构建以陆生植物、水生植物、高反射率地物、低反射率地物、裸露土壤为组分的五端元模型来反映研究区的地物组成;同时,以原始Landsat TM影像为基础,构建植物、高反射率地物、低反射率地物、裸露土壤为组分的四端元模型.针对两种端元模型,采用全约束下的LSMM算法进行混合像元分解以获取研究区的植被覆盖度,其次辅以研究区的纯水体信息对其进行优化.精度检验采用相同时期的高分辨率WorldView-2多光谱影像来进行.研究表明:虽然四端元模型与五端元模型对植被覆盖度的估算结果在空间上具有基本一致的分布趋势,但是前者的估算结果在数值上要普遍低于后者,在研究区的水体及其附近,四端元模型难以体现水生植物的植被覆盖信息;另外,五端元模型的估算结果与检验数据的相关系数R达到0.9023,均方根误差(RMSE)为0.0939,明显优于四端元模型的R=0.8671和RMSE=0.1711.这反映了通过对影像进行维度扩展的方法来改进端元提取的数量是可行的,而由此构建的五端元模型可以更充分的反映研究区地物之间的光谱差异,从而获得更好的估算精度.     

High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment

Physics-based remote sensing in littoral environments for ecological monitoring and assessment is a challenging task that depends on adequate atmospheric conditions during data acquisition, sensor capabilities and correction of signal disturbances associated with water surface and water column. Airborne hyper-spectral scanners offer higher potential than satellite sensors for wetland monitoring and assessment. However, application in remote areas is often limited by national restrictions, time and high costs compared to satellite data. In this study, we tested the potential of the commercial, high-resolution multi-spectral satellite QuickBird for monitoring littoral zones of Lake Sevan (Armenia). We present a classification procedure that uses a physics-based image processing system (MIP) and GIS tools for calculating spatial metrics. We focused on classification of littoral sediment coverage over three consecutive years (2006–2008) to document changes in vegetation structure associated with a rise in water levels. We describe a spectral unmixing algorithm for basic classification and a supervised algorithm for mapping vegetation types. Atmospheric aerosol retrieval, lake-specific parameterisation and validation of classifications were supported by underwater spectral measurements in the respective seasons. Results revealed accurate classification of submersed aquatic vegetation and sediment structures in the littoral zone, documenting spatial vegetation dynamics induced by water level fluctuations and inter-annual variations in phytoplankton blooms. The data prove the cost-effective applicability of satellite remote-sensing approaches for high-resolution mapping in space and time of lake littoral zones playing a major role in lake ecosystem functioning. Such approaches could be used for monitoring wetlands anywhere in the world.     

航空航天遥感在物种多样性研究与保护中的应用

人类活动导致全球范围内生物多样性丧失日趋严重。物种多样性是研究最为深入以及最贴近生物多样性管理的层次。物种多样性的研究往往受到多时空尺度生态过程的影响, 传统物种多样性调查方法受到人力物力影响, 局限性大, 物种多样性的研究与管理亟需整合不同来源的数据。遥感技术从传统的光学遥感阶段发展到不同平台、不同维度相结合的多源遥感阶段, 并逐渐进入以高空间分辨率和高光谱为特征、以激光雷达为前沿发展方向的综合遥感阶段。遥感技术因为其监测范围广、能监测人迹罕至地区以及长期可重复等特性, 为研究不同时空尺度的生态学科学问题提供了更新更优的研究手段。本文围绕种群动态、种间关系与群落多样性、功能属性及功能多样性以及生物多样性保护管理等生物多样性研究热点问题, 系统地论述了航空航天遥感技术在物种多样性研究与保护领域的应用, 总结了航空航天遥感技术在研究与物种多样性有关的主要生态学问题中的机遇与挑战。我们认为航空航天遥感技术利用多光谱甚至高光谱与激光技术从空中监测物种多样性, 从不同视角、基于不同光源提供了物种多样性不同侧面的信息, 能够减小地面调查强度, 在大范围和边远地区的物种多样性调查研究中有着至关重要的作用。依据光谱特性的物种判别以及依据激光雷达的三维结构量测将促进生物多样性的研究与管理, 加强遥感学家和生物多样性研究者的沟通交流将有助于促进不同时空尺度的生物多样性与遥感技术的结合。     

Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km² study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (r_s = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery.     

On the relationship between a resource based measure of geodiversity and broad scale biodiversity patterns

Geodiversity, (diversity of the geosphere) incorporates many of the environmental patterns and processes that are considered drivers of biodiversity. Components of geodiversity (climate, topography, geology and hydrology) can be considered in terms of their resource giving potential, where resources are taken as energy, water, space and nutrients. The total amount of these resources, along with their spatial and temporal variation, is herein proposed as a compound index of geodiversity that has the potential to model broad scale biodiversity patterns. This paper outlines potential datasets that could be used to represent geodiversity, and then reviews the theoretical links between each element of the proposed compound index of geodiversity (overall resource availability, temporal variation and spatial variation in those resources) and broad-scale patterns of biodiversity. Support for the influence of each of the elements of geodiversity on overall biodiversity patterns was found in the literature, although the majority of relevant research focuses on resource availability, particularly available energy. The links between temporal and spatial variation in resources and biodiversity have been less thoroughly investigated in the literature. For the most part, it was reported that overall resource availability, temporal variation and spatial variation in those resources do not act in isolation in terms of controlling biodiversity. Overall there are sufficient datasets to calculate the proposed compound index of geodiversity, and evidence in the literature for links between the geographical distribution of biodiversity and each of the elements of the compound index defined. Since data for measuring geodiversity is more spatially consistent and widely available (thanks to satellite remote sensing) geodiversity has potential as a conservation planning tool, especially where biological data are not available or sparsely distributed.     

Stream communities across a rural–urban landscape gradient

Rapid urbanization throughout the world is expected to cause extensive loss of biodiversity in the upcoming decades. Disturbances associated with urbanization frequently operate over multiple spatial scales such that local species extirpations have been attributed both to localized habitat degradation and to regional changes in land use. Urbanization also may shape stream communities by restricting species dispersal within and among stream reaches. In this patch-dynamics view, anthropogenic disturbances and isolation jointly reduce stream biodiversity in urbanizing landscapes. We evaluated predictions of stream invertebrate community composition and abundance based on variation in environmental conditions at five distinct spatial scales: stream habitats, reaches, riparian corridors and watersheds and their spatial location within the larger three-river basin. Despite strong associations between biodiversity loss and human density in this study, local stream habitat and stream reach conditions were poor predictors of community patterns. Instead, local community diversity and abundance were more accurately predicted by riparian vegetation and watershed landscape structure. Spatial coordinates associated with instream distances provided better predictions of stream communities than any of the environmental data sets. Together, results suggest that urbanization in the study region was associated with reduced stream invertebrate diversity through the alteration of landscape vegetation structure and patch connectivity. These findings suggest that maintaining and restoring watershed vegetation corridors in urban landscapes will aid efforts to conserve freshwater biodiversity.     

高时空分辨率植被覆盖获取方法及其在土壤侵蚀监测中的应用

土壤侵蚀是全球性生态问题,准确监测区域土壤侵蚀状况是评估区域生态质量和生态保护成效的基础。准确获取高时空分辨率植被覆盖信息并与降水动态匹配是土壤侵蚀准确监测的关键。然而,受卫星传感器限制,大区域高时间分辨率与高空间分辨率遥感数据无法同时获取,高空间分辨率植被动态遥感监测面临巨大挑战。为解决这一问题,本研究提出了一套多源遥感数据融合的高时空分辨率绿色植被覆盖度(半月尺度,空间分辨率2 m)获取方法,并与半月尺度的降水因子匹配应用于CSLE开展了天津市蓟州区的土壤侵蚀监测。研究结果表明:1)降雨和植被覆盖度因子在一年之内变异较大,半月降雨量的平均值为43.32 mm,变异系数可达150%,绿色植被半月植被覆盖度的平均值为54.74%,变异系数为18%。考虑土地覆盖类型的高时空分辨率绿色植被覆盖度融合方法,可以获取合理的高空间分辨率绿色植被覆盖度动态,为高空间分辨率土壤侵蚀监测提供了一个有效手段;2)土壤侵蚀发生范围与强度与降水及植被因子在年内的动态匹配高度相关,土壤侵蚀发生范围最大为10月上半月,发生面积为137.55 km~2,土壤侵蚀发生强度最为严重为7月下半月,25 t/hm~2以上土壤侵蚀发生面积为12.70 km~2;3)高时空分辨率植被与降水因子耦合下的土壤侵蚀监测结果与地面一致性较好(判定系数可达0.88),明显好于仅用一期高空间分辨率植被因子的土壤侵蚀监测结果(判定系数仅为0.097),采用高时空分辨率植被与降水因子耦合的土壤侵蚀监测方法可以大幅度提高土壤侵蚀监测的准确性,本研究为其他区域准确开展土壤侵蚀监测提供了一套有效的方法。     

Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities,Taiwan

The purpose of this study is to apply different remote sensing techniques to monitor shifting mangrove vegetation in the Danshui River estuary in Taipei, Taiwan, in order to evaluate a long-term wetland conservation strategy compromising between comprehensive wetland ecosystem management and urban development. In the Danshui estuary, mangrove dominated by Kandelia candel is the major vegetation, and a large area of marsh with freshwater grasses has been protected in three reserves along the river shore. This study applied satellite imagery from different remote sensors of various resolutions for spectral analysis in order to compare shifting wetland vegetation communities at different times. A two-stage analytical process was used for extracting vegetation area and types. In the first-stage, a normalized difference vegetation index (NDVI) was adopted to analyze SPOT, Landsat, and QuickBird imagery to obtain the spatial distribution of vegetation covers. In the second stage, a maximum likelihood classification (MLC) program was used to classify mangrove and non-mangrove areas. The results indicated that the spatial distribution of mangroves expanded 15.18 and 40 ha in two monitoring sites in 10 years, demonstrating the success of establishing reserves for protecting mangrove habitats. The analytical results also indicated that satellite imagery can easily discern the difference in characteristics between imagery of mangrove and other vegetation types, and that the logistical disadvantages of monitoring long-term vegetation community changes as well as evaluating an inaccessible area may be overcome by applying remote sensing techniques.