关于使用fragstats的一点总结
1.环境变量的设置
步骤：我的电脑->属性->高级->环境变量，在系统变量那里，新建，变量名为path，变量值为X:\ESRI\AV_GIS30\ARCVIEW\BIN32，X为Arcview安装所在的盘符。
至于如何借助Arcgis，我还没有试成功过。
2.grid文件的选择
正确地设置了环境变量之后，Run Parameters对话框中，Input Data Type的Arc Grid选项会变成可选。
选择Arc Grid选项，点击Grid name按钮，弹出文件选择对话框，在左边的树目录选择Grid文件所在的目录，在右边的列表框中就会出现可以选择的Grid文件列表，选择一个，ok之后，就可以将Grid文件导入。
3.Grid文件的生成
建议用Arcgis事先处理地图数据，因为Arcgis中间产生的数据格式大多为Grid格式，这样不用另外转换格式，也方便景观指数的计算。
如果用Arcview处理地图数据，可以在加载空间分析模块的基础上，Theme->Convert to Grid来生成。加载空间分析模块的方法：File->Extensions，选择Spatial Analyst，ok。
4.指数的选择。
指数一共有三个级别，path、class、land三个级别。不同级别对应不同的指数，对应着不同的生态学意义。所以选择指数的时候，一定要清楚所选择的指数对应的级别。
5.背景的问题
在景观计算中，需要注意背景的影响。这是景观指数误差中很大的一部分。
比如说，我们拿到的遥感图像，校正好后，边界裁剪后，一般不是规则的矩形。在边边角角存在没有信息的像元。图像分类后，没有信息的像元也是作为分类的一种的。因此，需要对其进行去除。这个操作可以在Arcgis下操作。
首先就是安装，使用这个软件，一般电脑上也需要安装arcview软件，因为它们是配合着使用的。但是在安装好后，要注意修改环境变量，具体方法是右键我的电脑-属性-高级-环境变量--系统变量-Path-变量值，修改这里的变量值，将arcview的安装路径考在路径值的最后面，现在启动Fragstats，应该就能用了。
打开软件后，首先要设置运行参数，在fragstats下点击set run parameters，设置初始化变量和值。注意在Input Data Type中选择你输入的变量类型，我当时学习的是ASCII码格式的数据，选择这个就可以了。下面有个Unique Patch ID，主要就是便于在ARCVIEW下查看计算好的数据时的ID号，可以选择第二项。然后是Grid Attributes选项，第一个是单元格的大小，这个在你的数据属性里应该有，接下来的背景值，多少是采用9999这个值，下面的两项还是你的数据属性值，自己添上就可以了。再下面的patch neighbors是领域规则，这里只有两个，四邻和八邻，自己选择好了。右面的输出统计包括斑块指数、类型指数和景观指数，一
般的话都打上勾就可以了，点击ok，进入计算步骤。
还有一步差点忘了，要建立几个.csv的文件格式，在txt和excel里都可以创建，分别是isolaation proximity\edge depth\contrast metrics\，之后计算过程就看你是选择什么样的指数了。
fragstats 部分景观指数生态学含义
拼块类型面积（CA），单位：ha，范围：CA>0
公式描述：CA等于某一拼块类型中所有拼块的面积之和（m2），除以10000后转化为公顷（ha）；即某拼块类型的总面积。
生态意义：CA度量的是景观的组分，也是计算其它指标的基础。它有很重要的生态意义，其值的大小制约着以此类型拼块作为聚居地（Habitation）的物种的丰度、数量、食物链及其次生种的繁殖等，如许多生物对其聚居地最小面积的需求是其生存的条件之一；不同类型面积的大小能够反映出其间物种、能量和养分等信息流的差异，一般来说，一个拼块中能量和矿物养分的总量与其面积成正比；为了理解和管理景观，我们往往需要了解拼块的面积大小，如所需要的拼块最小面积和最佳面积是极其重要的两个数据。
景观面积（TA），单位：ha，范围：TA>0
公式描述：TA等于一个景观的总面积，除以10000后转化为公顷（ha）。
生态意义：TA决定了景观的范围以及研究和分析的最大尺度，也是计算其它指标的基础。在自然保护区设计和景观生态建设中，对于维护高数量的物种，维持稀有种、濒危种以及生态系统的稳定，保护区或景观的面积是最重要的因素。
拼块所占景观面积的比例（%LAND），单位：百分比，范围：0< %LAND<=100
公式描述：%LAND等于某一拼块类型的总面积占整个景观面积的百分比。其值趋于0时，说明景观中此拼块类型变得十分稀少；其值等于100时，说明整个景观只由一类拼块组成。
生态意义：%LAND度量的是景观的组分，其在拼块级别上与拼块相似度指标（LSIM）的意义相同。由于它计算的是某一拼块类型占整个景观的面积的相对比例，因而是帮助我们确定景观中模地（Matrix）或优势景观元素的依据之一；也是决定景观中的生物多样性、优势种和数量等生态系统指标的重要因素。
拼块个数（NP），单位：无，范围：NP>=1
公式描述：NP在类型级别上等于景观中某一拼块类型的拼块总个数；在景观级别上等于景观中所有的拼块总数。
生态意义：NP反映景观的空间格局，经常被用来描述整个景观的异质性，其值的大小与景观的破碎度也有很好的正相关性，一般规律是NP大，破碎度高；NP小，破碎度低。NP对许多生态过程都有影响，如可以决定景观中各种物种及其次生种的空间分布特征；改变物种间相互
作用和协同共生的稳定性。而且，NP对景观中各种干扰的蔓延程度有重要的影响，如某类拼块数目多且比较分散时，则对某些干扰的蔓延（虫灾、火灾等）有抑制作用。
最大拼块所占景观面积的比例（LPI），单位：百分比，范围：0
公式描述：LPI等于某一拼块类型中的最大拼块占据整个景观面积的比例。
生态意义：有助于确定景观的模地或优势类型等。其值的大小决定着景观中的优势种、内部种的丰度等生态特征；其值的变化可以改变干扰的强度和频率，反映人类活动的方向和强弱。
拼块平均大小（MPS），单位：ha，范围：MPS>0
公式描述：MPS在拼块级别上等于某一拼块类型的总面积除以该类型的拼块数目；在景观级别上等于景观总面积除以各个类型的拼块总数。
生态意义：MPS代表一种平均状况，在景观结构分析中反映两方面的意义：景观中MPS值的分布区间对图像或地图的范围以及对景观中最小拼块粒径的选取有制约作用；另一方面MPS可以指征景观的破碎程度，如我们认为在景观级别上一个具有较小MPS值的景观比一个具有较大MPS值的景观更破碎，同样在拼块级别上，一个具有较小MPS值的拼块类型比一个具有较大MPS值的拼块类型更破碎。研究发现MPS值的变化能反馈更丰富的景观生态信息，它是反映景观异质性的关键。
面积加权的平均形状因子（AWMSI），
公式描述：AWMSI在拼块级别上等于某拼块类型中各个拼块的周长与面积比乘以各自的面积权重之后的和；在景观级别上等于各拼块类型的平均形状因子乘以类型拼块面积占景观面积的权重之后的和。其中系数0.25是由栅格的基本形状为正方形的定义确定的。公式表明面积大的拼块比面积小的拼块具有更大的权重。当AWMSI=1时说明所有的拼块形状为最简单的方形（采用矢量版本的公式时为圆形）；当AWMSI值增大时说明拼块形状变得更复杂，更不规则。
生态意义：AWMSI是度量景观空间格局复杂性的重要指标之一，并对许多生态过程都有影响。如拼块的形状影响动物的迁移、觅食等活动[14，64]，影响植物的种植与生产效率；对于自然拼块或自然景观的形状分析还有另一个很显著的生态意义，即常说的边缘效应。
面积加权的平均拼块分形指数（AWMPFD），单位：无，范围：1<=AWMPFD<=2
公式描述：AWMPFD的公式形式与AWMSI相似，不同的是其运用了分维理论来测量拼块和景观的空间形状复杂性。AWMPFD=1代表形状最简单的正方形或圆形，AWMPFD=2代表周长最复杂的拼块类型，通常其值的可能上限为1.5。
生态意义：AWMPFD是反映景观格局总体特征的重要指标，它在一定程度上
也反映了人类活动对景观格局的影响。一般来说，受人类活动干扰小的自然景观的分数维值高，而受人类活动影响大的人为景观的分数维值低。应该指出的是，尽管分数维指标被越来越多地运用于景观生态学的研究，但由于该指标的计算结果严重依赖于空间尺度和格网分辨率[67]，因而我们在利用AWMPFD指标来分析景观结构及其功能时要更为审慎。
平均最近距离（MNN），单位：m，范围：MNN>0
公式描述：MNN在拼块级别上等于从拼块ij到同类型的拼块的最近距离之和除以具有最近距离的拼块总数；MNN在景观级别上等于所有类型在拼块级别上的MNN之和除以景观中具有最近距离的拼块总数。
生态意义：MNN度量景观的空间格局。一般来说MNN值大，反映出同类型拼块间相隔距离远，分布较离散；反之，说明同类型拼块间相距近，呈团聚分布。另外，拼块间距离的远近对干扰很有影响，如距离近，相互间容易发生干扰；而距离远，相互干扰就少。但景观级别上的MNN在拼块类型较少时应慎用。
平均邻近指数（MPI），单位：无，范围：MPI>=0
公式描述：给定搜索半径后，MPI在拼块级别上等于拼块ijs的面积除以其到同类型拼块的最近距离的平方之和除以此类型的拼块总数；MPI在景观级别上等于所有拼块的平均邻近指数。MPI=0时说明在给定搜索半径内没有相同类型的两个拼块出现。MPI的上限是由搜索半径和拼块间最小距离决定的。
生态意义：MPI能够度量同类型拼块间的邻近程度以及景观的破碎度，如MPI值小，表明同类型拼块间离散程度高或景观破碎程度高；MPI值大，表明同类型拼块间邻近度高，景观连接性好。研究证明MPI对拼块间生物种迁徙或其它生态过程进展的顺利程度都有十分重要的影响[68]。
景观丰度（PR），单位：无，范围：PR>=1
公式描述：PR等于景观中所有拼块类型的总数。
生态意义：PR是反映景观组分以及空间异质性的关键指标之一，并对许多生态过程产生影响。研究发现景观丰度与物种丰度之间存在很好的正相关，特别是对于那些生存需要多种生境条件的生物来说PR就显得尤其重要。
香农多样性指数（SHDI），单位：无，范围：SHDI>=0
公式描述：SHDI在景观级别上等于各拼块类型的面积比乘以其值的自然对数之后的和的负值。SHDI=0表明整个景观仅由一个拼块组成；SHDI增大，说明拼块类型增加或各拼块类型在景观中呈均衡化趋势分布。
生态意义：SHDI是一种基于信息理论的测量指数，在生态学中应用很广泛。该指标能反映景观异质性，特别对景观中各拼块类型非均衡分布状况较为敏感，即强调稀有拼块类型对信息
的贡献，这也是与其它多样性指数不同之处。在比较和分析不同景观或同一景观不同时期的多样性与异质性变化时，SHDI也是一个敏感指标。如在一个景观系统中，土地利用越丰富，破碎化程度越高，其不定性的信息含量也越大，计算出的SHDI值也就越高。景观生态学中的多样性与生态学中的物种多样性有紧密的联系，但并不是简单的正比关系，研究发现在一景观中二者的关系一般呈正态分布。
香农均度指数（SHEI），单位：无，范围：0<=SHEI<=1
公式描述：SHEI等于香农多样性指数除以给定景观丰度下的最大可能多样性（各拼块类型均等分布）。SHEI=0表明景观仅由一种拼块组成，无多样性；SHEI=1表明各拼块类型均匀分布，有最大多样性。
生态意义：SHEI与SHDI指数一样也是我们比较不同景观或同一景观不同时期多样性变化的一个有力手段。而且，SHEI与优势度指标（Dominance）之间可以相互转换（即evenness=1-dominance）,即SHEI值较小时优势度一般较高，可以反映出景观受到一种或少数几种优势拼块类型所支配；SHEI趋近1时优势度低，说明景观中没有明显的优势类型且各拼块类型在景观中均匀分布。
散布与并列指数（IJI），单位：百分比，范围：0
公式描述：IJI在拼块类型级别上等于与某拼块类型i相邻的各拼块类型的邻接边长除以拼块i的总边长再乘以该值的自然对数之后的和的负值，除以拼块类型数减1的自然对数，最后乘以100是为了转化为百分比的形式；IJI在景观级别上计算各个拼块类型间的总体散布与并列状况。IJI取值小时表明拼块类型i仅与少数几种其它类型相邻接；IJI=100表明各拼块间比邻的边长是均等的，即各拼块间的比邻概率是均等的。
生态意义：IJI是描述景观空间格局最重要的指标之一。IJI对那些受到某种自然条件严重制约的生态系统的分布特征反映显著，如山区的各种生态系统严重受到垂直地带性的作用，其分布多呈环状，IJI值一般较低；而干旱区中的许多过渡植被类型受制于水的分布与多寡，彼此邻近，IJI值一般较高。
蔓延度指数（CONTAG），单位：百分比，范围：0
公式描述：CONTAG等于景观中各拼块类型所占景观面积乘以各拼块类型之间相邻的格网单元数目占总相邻的格网单元数目的比例，乘以该值的自然对数之后的各拼块类型之和，除以2倍的拼块类型总数的自然对数，其值加1后再转化为百分比的形式。理论上，CONTAG值较小时表明景观中存在许多小拼块；趋于100时表明景观中有连通度极高的优势拼块类型存在。应该指出的是，该指标只能运行在FRAGSTATS软件的栅格版本中。
生态意义：CONTA
G指标描述的是景观里不同拼块类型的团聚程度或延展趋势。由于该指标包含空间信息，是描述景观格局的最重要的指数之一。一般来说，高蔓延度值说明景观中的某种优势拼块类型形成了良好的连接性；反之则表明景观是具有多种要素的密集格局，景观的破碎化程度较高。而且研究发现蔓延度和优势度这两个指标的最大值出现在同一个景观样区。该指标在景观生态学和生态学中运用十分广泛，如Graham等曾用蔓延度指标进行生态风险评估；Musick和Grover 用它来量测图像的纹理等。
Metric Definitions (from McGarigal and Marks, 1994 and McGarigal and Marks, 1995)
Class Area (CA)
Sum of areas of all patches belonging to a given class.
Example: Conifer Class Area (CA) = 359047.844+......+65819.984
CA = 69.6626 hectares
If the map units are not specified (i.e., Data Frame properties; see Set map units) and "State areas in Hectares" has not been selected in the "Advanced Options" of the "Spatial Statistics" dialog box, then the resulting statistics will be reported in native map units (vector layers (themes) only).
In the example; CA = 696626.012 (map units). This is the case for most statistics.
Landscape Area (TLA)
Sum of areas of all patches in the landscape.
Example: Landscape Area (TLA) = 46872.719 + 359047.844 +... + 62423.574
TLA = 184.11 hectares
Percentage of Landscape (ZLAND)
When analyzing by class, ZLAND is the percentage of the total landscape made up of the corresponding class (patch type).
Number of Patches (NumP)
Total number of patches in the landscape if "Analyze by Landscape" is selected, or Number of Patches for each individual class, if "Analyze by Class" is selected.
Example: Class Level: Number of Patches (NumP)
Mixedwood = 5, Conifer = 4, Deciduous = 5
Landscape Level: Number of Patches (NumP) = 14
Patch Richness (PR)
PR is the number of different patch types within the landcape's boundary.
Patch Richness Density (PRD)
PRD is equal to PR divided by the total area of the landscape (metres squared) multiplied by 10,000 and then 100 (to convert to hundreds of hectares).
Largest Patch Index (LPI)
The LPI is equal to the percent of the total landscape that is made up by the largest patch.
When the entire landscape is made up of a single patch, the LPI will equal 100. As the size of the largest patch decreases, the LPI approaches 0.
Mean Patch Size (MPS)
Average patch size.
Example: Mean Patch Size of Conifer Patches (Class Level)
MPS = (359047.844 + 139531.484 ...+ 65819.984)/4
MPS = 17.42 hectares
Example: Mean Patch Size of Patches (Landscape Level)
MPS = (46872.719 + 359047.844 + ... + 62432.574)/14
MPS = 13.15 hectares
Median Patch Size (MedPS)
The middle patch size, or 50th percentile.
Example: Median Patch size of Conifer Patches (Class Level)
MedPS = 1
3.22 hectares
Example: Median Patch size of all patches (Landscape Level)
MedPS = 7.59 hectares
Patch Size Standard Deviation (PSSD)
Standard Deviation of patch areas.
Example: Patch Size Standard Deviation of Conifer Patches (Class Level)
PSSD = 11.05 hectares
Example: Patch Size Standard Deviation of all patches (Landscape Level)
PSSD = 9.51 hectares
Patch Size Coefficient of Variance (PSCoV)
Coefficient of variation of patches.
Example: Coefficient of Variation of Conifer patches (Class Level)
PSCoV = PSSD/MPS = (11.05 hectares / 17.42 hectares) *100 = 63
Example: Coefficient of Variation of all patches (Landscape Level)
PSCoV = (9.51 hectares / 13.15 hectares)*100 =72
Total Edge (TE)
Perimeter of patches.
Example: Total Edge Conifer (Class Level)
TE = Sum of perimeter of all conifer patches.
TE = 10858.88 metres
Units are expressed in native maps units.
Example: Total Edge all patches (Landscape Level)
TE = Sum of perimeter of all patches
TE = 28607.27 metres
Important
In the case of vector layers (themes), edge calculations include all the edge on the landscape including boundary edge. The contrasted weighted edge feature allows edge weight at the boundaries to be set to zero. In the case of raster (grid) layers (themes), edge calculations do not include the edges that surround the landscape boundary edge or any interior edges that include pixels classified as No Data.
Edge Density (ED)
Amount of edge relative to the landscape area.
Example: Edge Density Conifer (Class Level)
ED = TE / TLA
ED = 10858.88 metres/184.11 hectares = 58.98 metres/hectare
Example: Edge Density of all Patches (Landscape Level)
ED = 28607.27 metres/184.11 hectares = 155.38 metres/hectare
Mean Patch Edge (MPE)
Average amount of edge per patch.
Example: Mean Patch Edge Conifer (Class Level)
MPE = TE / NumP
MPE = 10858.88 metres/4 patches = 2714.72 metres/patch
Example: Mean Patch Edge all Patches (Landscape Level)
MPE = TE / NumP
MPE = 28607.27 metres/14 patches = 2043.38 metres/patch
Contrasted Weighted Edge Density (CWED)
CWED is a measure of density of edge in a landscape (metres per hectare) with a user-specified contrast weight.
CWED is equal to 0 when there is no edge in the landscape, in other words the whole landscape and it's border are made up of a single patch. It's value increases as the amount of edge in the landscape increases and/or as the user increases the contrast weight.
Landscape Shape Index (LSI)
LSI is the total landscape boundary and all edge within the boundary divided by the square root of the total landscape area (square metres) and adjusted by a constant (circular standard for vector layers, square standard for rasters). The LSI will increase with increasing landscape shape irregularity or increasing amounts of edge within the landscape.
Double Log Fractal Dimension (DLFD)
DLFD is a measure of patch perimeter complexity. It nears 1 when patch shapes are 'simple', such as circles or squares and it approaches 2 as patch shape perimeter complexity increases.
Mean Perimeter-Area Ratio (MPAR)
Shape Complexity.
Example: Mean perimeter-area ratio Conifer (Class Level)
MPAR = Sum of each patches perimeter/area ratio divided by number of patches.
MPAR = (132 m/ha + 112 m/ha + 201 m/ha + 84 m/ha)/4 patches
MPAR = 182 metres/hectare
Example: Mean perimeter-area ratio all patches (Landscape Level)
MPAR = (200 m/ha + 132 m/ha + ... + 175 m/ha)/14 patches
MPAR = 185 metres/hectare
Mean Shape Index (MSI)
Shape Complexity.
MSI is equal to 1 when all patches are circular (for polygons) or square (for rasters (grids)) and it increases with increasing patch shape irregularity.
MSI = sum of each patch's perimeter divided by the square root of patch area (in hectares) for each class (when analyzing by class) or all patches (when analyzing by landscape), and adjusted for circular standard ( for polygons), or square standard (for rasters (grids)), divided by the number of patches.
Area Weighted Mean Shape Index (AWMSI)
AWMSI is equal to 1 when all patches are circular (for polygons) or square (for rasters (grids)) and it increases with increasing patch shape irregularity.
AWMSI equals the sum of each patch's perimeter, divided by the square root of patch area (in hectares) for each class (when analyzing by class) or for all patches (when analyzing by landscape), and adjusted for circular standard ( for polygons), or square standard (for rasters (grids)), divided by the number of patches. It differs from the MSI in that it's weighted by patch area so larger patches will weigh more than smaller ones.
Mean Patch Fractal Dimension (MPFD)
Shape Complexity.
Mean patch fractal dimension (MPFD) is another measure of shape complexity. Mean fractal dimension approaches one for shapes with simple perimeters and approaches two when shapes are more complex.
Area Weighted Mean Patch Fractal Dimension (AWMPFD)
Shape Complexity adjusted for shape size.
Area weighted mean patch fractal dimension is the same as mean patch fractal dimension with the addition of individual patch area weighting applied to each patch. Because larger patches tend to be more complex than smaller patches, this has the effect of determining patch complexity independent of its size. The unit of measure is the same as mean patch fractal dimension.
Mean Nearest Neighbor (MNN)
Measure of patch isolation.
The nearest neighbor distance of an individual patch is the shortest distance to a similar patch (edge to edge). The mean nearest neighbor distance is the average of these distances (metres) for individual classes at the class level and the mean of the class nearest neighbor distances at the landscape level.
Interspersion Juxtaposition Index (IJI)
Measure of patch adacency.
Approaches zero
when the distribution of unique patch adjacencies becomes uneven and 100 when all patch types are equally adjacent.
Interspersion requires that the landscape be made up of a minimum of three classes. At the class level interspersion is a measure of relative interspersion of each class. At the landscape level it is a measure of the interspersion of the each patch in the landscape.
Mean Proximity Index (MPI)
Measure of the degree of isolation and fragmentation.
Mean proximity index is a measure of the degree of isolation and fragmentation of a patch. MPI uses the nearest neighbor statistic. The distance threshold default is 1,000,000. If MPI is required at specific distances, select Set MPI Threshold from the main Patch pull-down menu and enter a threshold distance.
Both MNN and MPI use the nearest neighbor statistic of similar polygons in their algorithm. Occasionally a blank or zero will be reported in MNN and MPI fields. This happens when one polygon vertex touches another polygons border but the two similar polygons do not share a common border. When this happens a manual edit (move) of the touching vertex will correct the problem in the layer (theme). This problem will not happen when analyzing raster (grid) layers (themes).
Shannon's Diversity Index (SDI)
Measure of relative patch diversity.
Shannon's diversity index is only available at the landscape level and is a relative measure of patch diversity. The index will equal zero when there is only one patch in the landscape and increases as the number of patch types or proportional distribution of patch types increases.
Shannon's Evenness Index (SEI)
Measure of patch distribution and abundance.
Shannon's evenness index is equal to zero when the observed patch distribution is low and approaches one when the distribution of patch types becomes more even. Shannon's evenness index is only available at the landscape level.
Modified Simpson's Diversity Index (MSIDI)
MSIDI is a measure of patch diversity. It equals zero when there is only one patch in the landscape and increases as the number of different patch types (PR) increases and the area among patch types becomes more equal.
Simpson's Evenness Index (SIEI)
SIEI is a measure of the distribution of area among patch types. It equals 1 when the distribution of area among patches is exactly even. SIEI approaches 0 as the distribution of area among the patches become more and more dominated by one patch type.
Modified Simpson's Evenness Index (MSIEI)
MSIEI is a measure of the distribution of area among patch types. It equals 1 when the distribution of area among patches is exactly even. SIEI approaches 0 as the distribution of area among the patches become more and more dominated by one patch type. It differs from SIEI in that it is derived from the Modified Simpson's Diversity Index (MSIDI) rather than the Simpson's Diversity Index (SIDI).
Important
Direct analyses of Core Area thro
ugh the spatial statistics dialogue are only available for raster (grid) layers (themes). If core area statistics are required for vector layers (themes), first Create Core Areas (create a new core area theme) from the Patch pull-down menu and then calculate statistics for the new layer (theme) as you would for a normal vector layer (theme). The results will be core area statistics.
Total Core Area (CA)
The total size of disjunct core patches.
The total size of disjunct core area patches (hectares).
Mean Core Area (MCA)
The average size of disjunct core patches.
The mean size of disjunct core area patches (hectares).
Number of Core Areas (NCA)
The total number of disjunct core areas within each patch of a corresponding patch type (or class).
Mean Core Area Index (MCAI)
MCAI is the average percentage of a landscape patch that is core area. It will be equal to 0 when there is no core area present in any patch in the landscape and it increases (towards 100%) when patches contain mostly core area.
Core Area Standard Deviation (CASD)
Measure of variability in core area size.
The standard deviation of disjunct core areas (hectares).
Core Area Density (CAD)
The relative number of disjunct core patches relative to the landscape area.
The total number of all disjunct patches divided by the landscape area (number of disjunct core patches/hectare).
Total Core Area Index (TCAI)
Measure of amount of core area in the landscape.
Total core area index is a measure of the amount of core area in the landscape. Total core area index is a proportion of core area in the entire landscape and is equal to zero when no patches in the landscape contain core and approaches one as the relative proportion of core area in the landscape increases.
Core Area Percentage of Land (C_LAND)
C_LAND is the percentage of the total landscape which is made up of core area.
Mean Core Area per Patch (MCA1)
MCA1 is the average core area per patch (as opposed to all distunct core areas).
It equals the sum of the core areas of each patch or corresponding patch type, divided by the number of total patches of the same type, divided by 10, 000 (to convert to hectares).
Core Area Coefficient of Variance (CACOV)
CACOV represents the variability in size of disjunct core areas in relation to the mean core area.
Patch Core Area Standard Deviation (CASD1)
Measure of variability in patch core area size.
The standard deviation of patch core areas (hectares).
Patch Core Area Coefficient of Variation (CACV1)
The standard deviation in core areas (CASD) divided by the mean core area per patch (MCA) and multplied by 100 (%).
The variablility in core area among patches relative to the mean core area.