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geodma_2:features [2017/02/15 18:06]
raian [Landscape-based features]
geodma_2:features [2021/08/27 19:14] (current)
tkorting [Segmentation-based spatial features]
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-====== GeoDMA ​2.0 Features ======+====== GeoDMA Features ======
  
 GeoDMA has metrics integrating Polygons, Cells, and Images. Through image segmentation,​ GeoDMA creates Polygons. ​ GeoDMA has metrics integrating Polygons, Cells, and Images. Through image segmentation,​ GeoDMA creates Polygons. ​
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 | Name             | Description | Formula | Range | Units | | Name             | Description | Formula | Range | Units |
-AMPLITUDE_BAND ​  | Defines ​the amplitude of the pixels inside the object. The amplitude means the maximum pixel value minus the minimum pixel value. | $amp = px_{max} - px_{min}$ | $\geq 0$ | $px$ | +AMPL_BAND ​  | Amplitude defines ​the amplitude of the pixels inside the object. The amplitude means the maximum pixel value minus the minimum pixel value. | $amp = px_{max} - px_{min}$ | $\geq 0$ | $px$ | 
-BAND_RATIO_BAND ​ ​| ​Describes ​the contribution of the given band to the region. | $bandRatio = \frac{\mu_{B_i}}{\sum_{j = 1}^{NB} \mu_{B_j}}$ | $\geq 0$ | $px$ | +BRATIO_BAND ​ ​| ​Band Ratio describes ​the contribution of the given band to the region. | $bandRatio = \frac{\mu_{B_i}}{\sum_{j = 1}^{NB} \mu_{B_j}}$ | $\geq 0$ | $px$ | 
-| COUNT_BAND ​      ​| ​Defines ​the total number of pixels inside the object, including pixels with dummy values. ​                                        | $N_{tot} = cout(X)$ ​        | $\geq 0$ | $N$  | +| COUNT_BAND ​      ​| ​Count defines ​the total number of pixels inside the object, including pixels with dummy values. ​                                        | $N_{tot} = cout(X)$ ​        | $\geq 0$ | $N$  | 
-CONTRAST_SE_BAND ​Returns ​a measure of the intensity contrast between a pixel and its southeast neighbor over the object. Contrast is 0 for a constant object. It is also known as //Sum of Squares Variance// | $Contrast = \sum_{i=0}^{D-1}\sum_{j=0}^{D-1}p_{i,​j} . |i - j|^2$         | $[0, (size(GLCM, 1)-1)^2]$ | - | +CONTSE_BAND ​Contrast SE returns ​a measure of the intensity contrast between a pixel and its southeast neighbor over the object. Contrast is 0 for a constant object. It is also known as //Sum of Squares Variance// | $Contrast = \sum_{i=0}^{D-1}\sum_{j=0}^{D-1}p_{i,​j} . |i - j|^2$         | $[0, (size(GLCM, 1)-1)^2]$ | - | 
-DISSIMILARITY_SE_BAND ​Measures ​how different the elements of the GLCM are from each other and it is high when the local region has a high contrast. | $dissimilarity = \sum_{i=0}^{D-1}\sum_{j=0}^{D-1}p_{i,​j}|i-j|$ | $\geq 0$ | - | +DISSE_BAND ​Dissimilarity SE measures ​how different the elements of the GLCM are from each other and it is high when the local region has a high contrast. | $dissimilarity = \sum_{i=0}^{D-1}\sum_{j=0}^{D-1}p_{i,​j}|i-j|$ | $\geq 0$ | - | 
-ENERGY_SE_BAND ​   ​| ​It returns the squared root of Angular Second Moment, computed by the sum of the squared elements in GLCM. Energy is 1 for a constant image. ​ | $energy = \sum_{i=0}^{D-1} \sum{j=0}^{D-1} p_{i,j}^2}$ | $[0,1]$ | - | +ENERGSE_BAND ​   ​| ​Energy SE returns the squared root of Angular Second Moment, computed by the sum of the squared elements in GLCM. Energy is 1 for a constant image. ​ | $energy = \sum_{i=0}^{D-1} \sum{j=0}^{D-1} p_{i,j}^2}$ | $[0,1]$ | - | 
-ENTROPY_SE_BAND ​  | Measures ​the disorder in an image. When the image is not uniform, many GLCM elements have small values, resulting in large entropy. | $entropy = -\sum_{i=1}^{D - 1} \sum_{j=1}^{D - 1} p_{ij} . \log{p_{ij}}$ | $\geq 0$ | - | +ENTRSE_BAND ​  | Entropy SE measures ​the disorder in an image. When the image is not uniform, many GLCM elements have small values, resulting in large entropy. | $entropy = -\sum_{i=1}^{D - 1} \sum_{j=1}^{D - 1} p_{ij} . \log{p_{ij}}$ | $\geq 0$ | - | 
-HOMOGENEITY_SE_BAND ​Assumes ​higher values for smaller differences in the GLCM. Also called //Inverse Difference Moment//. Homogeneity is 1 for a diagonal GLCM. | $homog = \sum_{i=1}^{D - 1} \sum_{j=1}^{D - 1} \frac{p_{ij}}{1 + (i - j)^2}$ | $\geq 0$ | - | +HOMOGSE_BAND ​Homogeneity SE assumes ​higher values for smaller differences in the GLCM. Also called //Inverse Difference Moment//. Homogeneity is 1 for a diagonal GLCM. | $homog = \sum_{i=1}^{D - 1} \sum_{j=1}^{D - 1} \frac{p_{ij}}{1 + (i - j)^2}$ | $\geq 0$ | - | 
-KURTOSIS_BAND ​    | Returns ​the kurtosis value for all the valid pixels (not dummy) inside the object. ​                                               | $K = \frac{n(n+1)(n-1)}{(n-2)(n-3)} . \frac{\sum_{i=1}^{N}(px_i-\mu)^4}{(\sum_{i=1}^N (px_i - \mu)^2)^2$ | $\geq 1$ | -  | +KURT_BAND ​    | Kurtosis returns ​the kurtosis value for all the valid pixels (not dummy) inside the object. ​                                               | $K = \frac{n(n+1)(n-1)}{(n-2)(n-3)} . \frac{\sum_{i=1}^{N}(px_i-\mu)^4}{(\sum_{i=1}^N (px_i - \mu)^2)^2$ | $\geq 1$ | -  | 
-MAXIMUM_VAL_BAND ​ ​| ​Computes ​the maximum gray level value (not dummy) inside the object. ​                                               | $maxVal = max(X)$ | $X$ | $px$  | +MAX_VAL_BAND ​ ​| ​Maximum Value computes ​the maximum gray level value (not dummy) inside the object. ​                                               | $maxVal = max(X)$ | $X$ | $px$  | 
-| MEAN_BAND ​        ​| ​Computes ​the average value for all $N$ pixels inside the object. | $\mu = \frac{\sum_{i=1}^N px_i}{N}$ | $\geq 0$ | $px$ | +| MEAN_BAND ​        ​| ​Mean computes ​the average value for all $N$ pixels inside the object. | $\mu = \frac{\sum_{i=1}^N px_i}{N}$ | $\geq 0$ | $px$ | 
-| MEDIAN_BAND ​      ​| ​Computes ​the median for all $N$ pixels inside the object. | $median = p_{\frac{(n+1)}{2}}$ | $X$ | $px$ |  +| MEDIAN_BAND ​      ​| ​Median computes ​the median for all $N$ pixels inside the object. | $median = p_{\frac{(n+1)}{2}}$ | $X$ | $px$ |  
-MINIMUM_VAL_BAND ​ ​| ​Computes ​the minimum gray level value (not dummy) inside the object. ​                                               | $minVal = min(X)$ | $X$ | $px$  | +MIN_VAL_BAND ​ ​| ​Minimum Value computes ​the minimum gray level value (not dummy) inside the object. ​                                               | $minVal = min(X)$ | $X$ | $px$  | 
-| MODE_BAND ​        ​| ​Returns ​the most occurring gray level value (mode) for all $N$ (not dummy) pixels inside the object. When the object is multimodal, the first value is assumed. | | $X$ | $px$ | +| MODE_BAND ​        ​| ​Mode returns ​the most occurring gray level value (mode) for all $N$ (not dummy) pixels inside the object. When the object is multimodal, the first value is assumed. | | $X$ | $px$ | 
-NUM_MODES_BAND ​   ​| ​Returns ​the number of modes for the object. ​        | | $\geq 1$ | $px$ | +N_MOD_BAND ​   ​| ​Num Modes returns ​the number of modes for the object. ​        | | $\geq 1$ | $px$ | 
-SKEWNESS_BAND ​    | Returns the skewness ​value for all the valid pixels (not dummy) inside the object. ​                                               | $S = \frac{n\sqrt{n-1}}{n-2} . \frac{\sum_{i=1}^{N}(px_i-\mu)^3}{(\sum_{i=1}^N (px_i - \mu)^2)^\frac{3}{2}$ | - | -  | +SKEW_BAND ​    | Returns the Skewness ​value for all the valid pixels (not dummy) inside the object. ​                                               | $S = \frac{n\sqrt{n-1}}{n-2} . \frac{\sum_{i=1}^{N}(px_i-\mu)^3}{(\sum_{i=1}^N (px_i - \mu)^2)^\frac{3}{2}$ | - | -  | 
-STD_DEVIATION_BAND ​| Returns the standard deviation ​of all $N$ (not dummy) pixels ($\mu$ is the mean value) inside the object. | $\sigma = \sqrt{\frac{1}{N-1}\sum_{i=1}^N \left(px_i - \mu \right)^2}$ | $\geq 0$ | $px$ | +STDDEV_BAND ​| Returns the Standard Deviation ​of all $N$ (not dummy) pixels ($\mu$ is the mean value) inside the object. | $\sigma = \sqrt{\frac{1}{N-1}\sum_{i=1}^N \left(px_i - \mu \right)^2}$ | $\geq 0$ | $px$ | 
-| SUM_BAND | Returns the sum of all $N$ (not dummy) pixels inside the object. ​                                          | $sum = \sum_{i = 1}^{N}px_i$ | $\geq 0$ | $px$ | +| SUM_BAND | Returns the Sum of all $N$ (not dummy) pixels inside the object. ​                                          | $sum = \sum_{i = 1}^{N}px_i$ | $\geq 0$ | $px$ | 
-VALID_COUNT_BAND ​Defines ​the total number of pixels inside the object with not dummy values. ​                                          | $N = count(X)$ | $\geq 0$ | $N$ | +VLDCNT_BAND ​Valid Count defines ​the total number of pixels inside the object with not dummy values. ​                                          | $N = count(X)$ | $\geq 0$ | $N$ | 
-VAR_COEFF_BAND ​| Returns the coefficient ​of variation ​of the values for all the valid pixels (not dummy) inside the object ​              | $c_v = \frac{100\sigma}{\mu}$ | $[0,100]$ | % | +VARCOEF_BAND ​| Returns the Coefficient ​of Variation ​of the values for all the valid pixels (not dummy) inside the object ​              | $c_v = \frac{100\sigma}{\mu}$ | $[0,100]$ | % | 
-VARIANCE_BAND ​| Returns the variance ​of all $N$ (not dummy) pixels ($\mu$ is the mean value) inside the object. | $s^2 = \frac{\sum_{i=1}^N \left(px_i - \mu \right)^2}{N-1}$ | $\geq 0$ | $px$ |+VAR_BAND ​| Returns the Variance ​of all $N$ (not dummy) pixels ($\mu$ is the mean value) inside the object. | $s^2 = \frac{\sum_{i=1}^N \left(px_i - \mu \right)^2}{N-1}$ | $\geq 0$ | $px$ |
 ===== Segmentation-based spatial features =====  ===== Segmentation-based spatial features ===== 
  
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 | POL_ANGLE | Represents the main angle of an object. It is obtained by computing the minimum circumscribing ellipse, and the angle of the biggest radius of the ellipse suits to the object'​s angle. | | $\left[-\pi,​ \pi]$ | $rad$ | | POL_ANGLE | Represents the main angle of an object. It is obtained by computing the minimum circumscribing ellipse, and the angle of the biggest radius of the ellipse suits to the object'​s angle. | | $\left[-\pi,​ \pi]$ | $rad$ |
 | POL_AREA | Returns the area of the object, measured in the unit of measure of the current Spatial Reference System. ​                                  | | $\geq 0$ | $[m,​degrees,​...]^2$ | | POL_AREA | Returns the area of the object, measured in the unit of measure of the current Spatial Reference System. ​                                  | | $\geq 0$ | $[m,​degrees,​...]^2$ |
-POL_BBOX_AREA ​| Returns the bounding box area of an object, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m,​degrees,​...]^2$ |  +PBOX_AREA ​| Returns the bounding box area of an object, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m,​degrees,​...]^2$ |  
-POL_BBOX_PERIM ​| Returns the perimeter of the bounding box of an object, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m,​degrees,​...]$ |  +PBOX_PERIM ​| Returns the perimeter of the bounding box of an object, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m,​degrees,​...]$ |  
-POL_CIRCLE| Relates the areas of the object and the smallest circumscribing circle around the object. In the equation, $R$ is the maximum distance between the centroid and all vertices. | $1 - \frac{\textit{area}}{\pi R^2}$ | $[0, 1)$ | $[m,​degrees,​...]^2$ | +PCIRCLE| Relates the areas of the object and the smallest circumscribing circle around the object. In the equation, $R$ is the maximum distance between the centroid and all vertices. | $1 - \frac{\textit{area}}{\pi R^2}$ | $[0, 1)$ | $[m,​degrees,​...]^2$ | 
-POL_ELLIPTIC_FIT| Finds the minimum circumscribing ellipse to the object and returns the ratio between the object'​s area and the ellipse area. | | $\left[0, 1\right]$ | - | +PELLIP_FIT| Finds the minimum circumscribing ellipse to the object and returns the ratio between the object'​s area and the ellipse area. | | $\left[0, 1\right]$ | - | 
-POL_FRACTALDIM ​| Returns the fractal dimension of an object. | $2 \frac{\log {\frac{\textit{perimeter}}{4}} }{\log area}$ | $[1, 2]$ | - | +P_FRACDIM ​| Returns the fractal dimension of an object. | $2 \frac{\log {\frac{\textit{perimeter}}{4}} }{\log area}$ | $[1, 2]$ | - | 
-POL_GYRATION_RATIUS ​| This feature is equals the average distance between each vertex of the polygon and it's centroid. The more similar to a circle is the object, the more likely the centroid will be inside it, and therefore this feature will be closer to 0. | $\frac{\sum{\sqrt{(x_i - x_C)^2 + (x_i - x_C)^2}}}{N}$ | $\geq 0$ | $[m, degrees, ...]$ | +PGYRATIUS ​| This feature is equals the average distance between each vertex of the polygon and it's centroid. The more similar to a circle is the object, the more likely the centroid will be inside it, and therefore this feature will be closer to 0. | $\frac{\sum{\sqrt{(x_i - x_C)^2 + (x_i - x_C)^2}}}{N}$ | $\geq 0$ | $[m, degrees, ...]$ | 
-POL_BBOX_LENGTH ​| It is the height of the object'​s bounding box, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m, degrees, ...]$ | +POLRADIUS | Returns the polygon radius. It corresponds to the maximum distance between the polygon centroid its vertexes. | $R$ | $>0$ | - | 
-POL_PERIMETER ​| Returns the perimeter of the object, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m,​degrees,​...]$ | +| PBOX_LEN ​| It is the height of the object'​s bounding box, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m, degrees, ...]$ | 
-POL_PERIM_AREA_RATIO ​| Calculates the ratio between the perimeter and the area of an object. | $\frac{\textit{perimeter}}{\textit{area}}$ | $\geq 0$ | $px^{-1}$ | +P_PERIM ​| Returns the perimeter of the object, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m,​degrees,​...]$ | 
-POL_RECTANGULAR_FIT ​| This feature fits a minimum rectangle outside the object and calculates the ratio between its area and the area of this rectangle. The closer to $1$ is this feature, the most similar to a rectangle. | $\frac{\textit{area}}{\textit{minBoxArea}}$ | $\left[0, 1\right]$ | - | +P_PERARAT ​| Calculates the ratio between the perimeter and the area of an object. | $\frac{\textit{perimeter}}{\textit{area}}$ | $\geq 0$ | $px^{-1}$ | 
-POL_BBOX_WIDTH ​| Returns the width of the object'​s bounding box, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m, degrees, ...]$ | +PRECTFIT ​| This feature fits a minimum rectangle outside the object and calculates the ratio between its area and the area of this rectangle. The closer to $1$ is this feature, the most similar to a rectangle. | $\frac{\textit{area}}{\textit{minBoxArea}}$ | $\left[0, 1\right]$ | - | 
-POL_COMPACITY ​| Returns the compacity of the object. | $\frac{\textit{perimAreaRatio}}{\sqrt{\textit{area}}}}$ | $\geq 0$ | - | +PBOX_WIDTH ​| Returns the width of the object'​s bounding box, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m, degrees, ...]$ | 
-POL_DENSITY ​| This feature corresponds to the ratio between the polygon area and the polygon radius. | $\frac{\textit{area}}{\textit{radius}}$ | $\geq 0$ | - | +P_COMPAC ​| Returns the compacity of the object. | $\frac{\textit{perimAreaRatio}}{\sqrt{\textit{area}}}}$ | $\geq 0$ | - | 
-POL_SHAPE_INDEX ​| This feature corresponds to the ratio between the polygon perimeter and the squared root of the polygon area. | $\frac{\textit{perimeter}}{4 * \sqrt{\textit{area}}}$ | $\geq 0$ | - |+PDENSITY ​| This feature corresponds to the ratio between the polygon area and the polygon radius. | $\frac{\textit{area}}{\textit{radius}}$ | $\geq 0$ | - | 
 +PSHAPEIDX ​| This feature corresponds to the ratio between the polygon perimeter and the squared root of the polygon area. | $\frac{\textit{perimeter}}{4 * \sqrt{\textit{area}}}$ | $\geq 0$ | - |
  
  
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 | c_MSI | MSI stands for Mean Shape Index, which is equals the sum of the patch perimeter ($m$) by divided two times the square root of patch area ($m^2$) multiplied by pi ($\pi$) for each patch of the corresponding patch type, divided by the the number of patches of the same patch type (class). | $MSI = \frac{\sum_{j=1}^n \frac{p_j}{2 \times \sqrt{\pi \times a_j}}}{n}$| $\geq 1$| - | | c_MSI | MSI stands for Mean Shape Index, which is equals the sum of the patch perimeter ($m$) by divided two times the square root of patch area ($m^2$) multiplied by pi ($\pi$) for each patch of the corresponding patch type, divided by the the number of patches of the same patch type (class). | $MSI = \frac{\sum_{j=1}^n \frac{p_j}{2 \times \sqrt{\pi \times a_j}}}{n}$| $\geq 1$| - |
 | c_AWMSI | AWMSI stands for Area-Weighted MSI, which is equals the sum of the landscape boundary and all edge segments ($m$) within the boundary. This sum involves the corresponding patch type (including borders), divided by the two times the square root of the total landscape area ($m^2$) multiplied by pi ($\pi$). This first term is multiplied by the area of the corresponding patch, divided by the sum of the areas of all patches of the same patch type (class).| $AWMSI = \sum_{j=1}^n \left[ \frac{p_j}{2 \sqrt{\pi \times a_j}} \times \frac{a_j}{\sum_{j=1}^n a_j} \right]$| $\geq 1$| - | | c_AWMSI | AWMSI stands for Area-Weighted MSI, which is equals the sum of the landscape boundary and all edge segments ($m$) within the boundary. This sum involves the corresponding patch type (including borders), divided by the two times the square root of the total landscape area ($m^2$) multiplied by pi ($\pi$). This first term is multiplied by the area of the corresponding patch, divided by the sum of the areas of all patches of the same patch type (class).| $AWMSI = \sum_{j=1}^n \left[ \frac{p_j}{2 \sqrt{\pi \times a_j}} \times \frac{a_j}{\sum_{j=1}^n a_j} \right]$| $\geq 1$| - |
-| c_MPFD | MPFD stands for the Mean Patch Fractal Dimension. | $MPFD = \frac{\sum_{j=1}^{n} \frac{2 \times \ln{p_j}}{\ln{a_j}}}{N}$ | | | +| c_MPFD | MPFD stands for the Mean Patch Fractal Dimension. | $MPFD = \frac{\sum_{j=1}^{n} \frac{2 \times \ln{p_j}}{\ln{a_j}}}{n}$ | | | 
-| c_AWMPFD| AWMPFD stands for Area-weighted Mean Patch Fractal Dimension. | $AWMPFD = \sum_{j = 1}^{n} [\frac{2 \times \ln{p_j}}{\ln{a_j}} \times \frac{a_j}{\sum_{j = 1}^{n} a_{ij}}}]$ | | |+| c_AWMPFD| AWMPFD stands for Area-weighted Mean Patch Fractal Dimension. | $AWMPFD = \sum_{j = 1}^{n} [\frac{2 \times \ln{p_j}}{\ln{a_j}} \times \frac{a_j}{\sum_{j = 1}^{n} a_{ij}}]$ | | |
 | c_ED | ED stands for Edge Density, which is equals the sum of the lengths ($m$) of all edge segments involving the corresponding patch type, divided by the total landscape area ($m^2$). | $ED = \frac{\sum_{j=1}^m e_j}{A} 10^{4}$| $\geq 0$| $m/ha$ | | c_ED | ED stands for Edge Density, which is equals the sum of the lengths ($m$) of all edge segments involving the corresponding patch type, divided by the total landscape area ($m^2$). | $ED = \frac{\sum_{j=1}^m e_j}{A} 10^{4}$| $\geq 0$| $m/ha$ |
 | c_MPAR | MPAR stands for Mean Perimeter Area Ratio, which is equals the sum of ratios between perimeters and areas, divided by the number of patches of the same type. | $MPAR = \frac{ \sum_{j=1}^n \frac{p_j}{a_j}}{n}$| $\geq 0$| $m^{-1}$ | | c_MPAR | MPAR stands for Mean Perimeter Area Ratio, which is equals the sum of ratios between perimeters and areas, divided by the number of patches of the same type. | $MPAR = \frac{ \sum_{j=1}^n \frac{p_j}{a_j}}{n}$| $\geq 0$| $m^{-1}$ |
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 | c_IJI | IJI stands for Interspersion and Juxtaposition Index. ​ The observed interspersion over the maximum possible interspersion for the given number of patch types. It only exists for $n > 3$. | $IJI = \frac{-\sum_{j=1}^n (\frac{e_j}{\sum_{k=1}^n e_k}) \times \ln{(\frac{e_j}{\sum_{k=1}^n e_k})}}{\ln(m - 1)} \times 100$ | $[0, 100]$ | $\%$ | | c_IJI | IJI stands for Interspersion and Juxtaposition Index. ​ The observed interspersion over the maximum possible interspersion for the given number of patch types. It only exists for $n > 3$. | $IJI = \frac{-\sum_{j=1}^n (\frac{e_j}{\sum_{k=1}^n e_k}) \times \ln{(\frac{e_j}{\sum_{k=1}^n e_k})}}{\ln(m - 1)} \times 100$ | $[0, 100]$ | $\%$ |
 | c_TABO | TABO stands for the Total Area of the Biggest Object that intersects the landscape. |  |  | $ha$ | | c_TABO | TABO stands for the Total Area of the Biggest Object that intersects the landscape. |  |  | $ha$ |
 +| c_BIA | BIA stands for the Biggest Intersection Area. |  |  | $ha$ |
 +| c_TAOBIA | TAOBIA stands for the Total Area of the Object with Biggest Intersection Area. |  |  | $ha$ |
 | PR | PR stands for Patch Richness, which is equals the number of different patch types (classes) present within the landscape boundary. | $PR = m$ | $\geq0$ |  | | PR | PR stands for Patch Richness, which is equals the number of different patch types (classes) present within the landscape boundary. | $PR = m$ | $\geq0$ |  |
 | PRD | PRD stands for Patch Richness Density, which is equals the number of different patch types present within the landscape boundary divided by total landscape area ($m^2$), multiplied by $10,000$ and $100$ (to convert to $100$ hectares). Note, total landscape area ($A$) includes any internal background present. | $PRD = \frac{m}{A} \times 10000 \times 100$ | $\geq0$ | $Number/100 ha$ | | PRD | PRD stands for Patch Richness Density, which is equals the number of different patch types present within the landscape boundary divided by total landscape area ($m^2$), multiplied by $10,000$ and $100$ (to convert to $100$ hectares). Note, total landscape area ($A$) includes any internal background present. | $PRD = \frac{m}{A} \times 10000 \times 100$ | $\geq0$ | $Number/100 ha$ |

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