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lesson_18 [2020/02/11 10:43]
argemiro 		lesson_18 [2020/02/13 06:59]
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 * Functors: \\ - //​[[:​Determine Weights Of Evidence Ranges]]//​\\ - //​[[:​Determine Weights Of Evidence Coefficients]]//​\\ - //​[[:​calc_w._of_e._probability_map|Calc W. Of E. Probability Map]]//\\ - //[[:Calc Change Matrix]]//​\\ - //​[[:​Patcher]]//​\\ - //​[[:​Expander]]//​\\  ​ * Functors: \\ - //​[[:​Determine Weights Of Evidence Ranges]]//​\\ - //​[[:​Determine Weights Of Evidence Coefficients]]//​\\ - //​[[:​calc_w._of_e._probability_map|Calc W. Of E. Probability Map]]//\\ - //[[:Calc Change Matrix]]//​\\ - //​[[:​Patcher]]//​\\ - //​[[:​Expander]]//​\\  ​
  
-This lesson explores the use of Dinamica EGO as a simulation platform for land-use and cover change (LUCC) models. The goal is to calibrate, run and validate a LUCC model, in this case a simulation model of deforestation. You will need to go through 10 steps in order to complete the model, as depicted in the fig.1 . To facilitate this process, each one of these steps will be represented as a separate model. Although, all steps could be joined into a single model, for reason ​of simplicity we will keep them as separate models.+This lesson explores the use of Dinamica EGO as a simulation platform for land-use and cover change (LUCC) models. The goal is to calibrate, run and validate a LUCC model, in this case a simulation model of deforestation. You will need to go through 10 steps in order to complete the model, as depicted in the fig.1 . To facilitate this process, each one of these steps will be represented as a separate model. Although, all steps could be joined into a single model, for sake of simplicity we will keep them as separate models.
  
 {{ :​steps_lucc_model.png?​900 |}} {{ :​steps_lucc_model.png?​900 |}}
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-The input dataset represents a region of Rondonia state, around the town of Ariquenes, in the Brazilian Amazon, fig.2. Open the maps ''​23267_1997.ers''​ and ''​23267_2000.ers'',​ located in ''​\Examples\setup_run_and_validate_a_lucc_model\originals''​ using the Color Palette, "​Amazon"​. These maps correspond to a Landsat image (232/67) classified by PRODES [[http://​www.obt.inpe.br/​prodes|(INPE,​ 2008)]] – Brazilian program for monitoring deforestation - for the years 1997 and 2000.\\+The input dataset represents a region of Rondonia state, around the town of Ariquenes, in the Brazilian Amazon, fig.2. Open the maps ''​23267_1997.ers''​ and ''​23267_2000.ers'',​ located in ''​\Guidebook_Dinamica_5\Database\LUCC_files''​ using the Color Palette, "​Amazon"​. These maps correspond to a Landsat image (232/67) classified by PRODES [[http://​www.obt.inpe.br/​prodes|(INPE,​ 2008)]] – Brazilian program for monitoring deforestation - for the years 1997 and 2000.\\
  
 In this LUCC model, Dinamica EGO will use the 1997 map as the initial landscape and the 2000 map as the final, considering the  landscape as a bi-dimensional array of land use types.\\ In this LUCC model, Dinamica EGO will use the 1997 map as the initial landscape and the 2000 map as the final, considering the  landscape as a bi-dimensional array of land use types.\\
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-<note tip>​**TIP**:​ Keep in mind the numbers that identify the map classes, since Dinamica EGO does not explicitly handle class names.</​note>​+<note tip>​**TIP**:​ Keep in mind the numbers that identify the map classes, since Dinamica EGO does not explicitly handle class names, although they can be visualized in the viewer with customized colors and specified in the metadata as well as in the header of geotiff files. See functor Assign Map Categories.</​note>​
  
 ==== First step: Calculating transition matrices ==== ==== First step: Calculating transition matrices ====
  
-First, you need to calculate the historical transition matrices. The transition matrix describes a system that changes over discrete time increments, in which the value of any variable in a given time period is the sum of fixed percentages of values of all variables in the previous time step. The sum of fractions along the column of the transition matrix is equal to one. The diagonal line of the transition matrix does not need to be specified since Dinamica EGO does not model the percentage of unchangeable cells, nor do the transitions equal to zero. The transition rate can be passed to the LUCC model as a fixed parameter or be updated from model feedback. ​+First, you need to calculate the historical transition matrices. The transition matrix describes a system that changes over discrete time increments, in which the value of any variable in a given time period is the sum of fixed percentages of values of all variables in the previous time step. The sum of fractions along the column of the transition matrix is equal to one. The diagonal line of the transition matrix does not need to be specified since Dinamica EGO does not model the percentage of unchangeable cells, nor the transitions equal to zero. The transition rate can be passed to the LUCC model as a fixed parameter or be updated from model feedback. ​
  
 {{:​tutorial:​lucc_3.jpg?​300|}}(1) {{:​tutorial:​lucc_3.jpg?​300|}}(1)
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 The single-step matrix corresponds to a time period represented as a single time step, in turn the multiple-step matrix corresponds to a time step unit (year, month, day, etc) specified by dividing the time period by a number of time steps. For Dinamica EGO, time step can comprise any span of time, since time unit is only an external reference. A multiple-step transition matrix can only be derived from an Ergodic matrix, i.e. a matrix that has real number Eigen values and vectors. The single-step matrix corresponds to a time period represented as a single time step, in turn the multiple-step matrix corresponds to a time step unit (year, month, day, etc) specified by dividing the time period by a number of time steps. For Dinamica EGO, time step can comprise any span of time, since time unit is only an external reference. A multiple-step transition matrix can only be derived from an Ergodic matrix, i.e. a matrix that has real number Eigen values and vectors.
  
-The transition rates set the net quantity of changes, that is, the percentage of land that will change to another state (land use and cover attribute), and thus they are known as net rates, ​being adimensional. In turn, gross rates are specified as an area unit, such as hectares or km<​sup>​2</​sup>​ per unit of time. In the case that there is not a solution for the multiple-step transition matrix, you still can run the model in several time steps, as defined above, calculating a fixed gross rate per time step (e.g. year) by dividing the accumulated change over the period by the number of steps over which the period is composed (this might not apply to complex transition model). Dinamica EGO converts gross rates into net rate, dividing the extent of change by the fraction of each land use and cover class prior to change, before passing it to the transition functors: //​[[:​Patcher]]//​ and //​[[:​Expander]]//​.\\+The transition rates set the net quantity of changes, that is, the percentage of land that will change to another state (land use and cover attribute), and thus they are known as net rates, ​and denoted in percentage. In turn, gross rates are specified as an area unit, such as hectares or km<​sup>​2</​sup>​ per unit of time. In the case that there is not a solution for the multiple-step transition matrix, you still can run the model in several time steps, as defined above, calculating a fixed gross rate per time step (e.g. year) by dividing the accumulated change over the period by the number of steps over which the period is composed (this might not apply to complex transition model). Dinamica EGO converts gross rates into net rate, dividing the extent of change by the fraction of each land use and cover class prior to change, before passing it to the transition functors: //​[[:​Patcher]]//​ and //​[[:​Expander]]//​.\\
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-Open the model ''​determine_transition_matrix.egoml''​ located in ''​\setup_run_and_validate_a_lucc_model\1_transition_matrix_calculation''​\\+Open the model ''​determine_transition_matrix.egoml''​ located in ''​\Guidebook_Dinamica_5\Models\LUCC_model\1_transition_matrix_calculation''​\\
  
 This model calculates the single-step and multiple step matrices. ​ This model calculates the single-step and multiple step matrices. ​
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-Now, open the model ''​determine_weights_of_evidence_ranges.egoml''​ located in ''​\setup_run_and_validate_a_lucc_model\2_weights_of_evidence_ranges_calculation''​.+Now, open the model ''​determine_weights_of_evidence_ranges.egoml''​ located in ''​\Guidebook_Dinamica_5\Models\LUCC_model\2_weights_of_evidence_ranges_calculation''​.
  
 This model calculates ranges in order to categorize continuous gray-tone variables for deriving the Weights of Evidence. It selects the number of intervals and their buffer sizes aiming to better preserve the data structure. See the help for a further description of this method. As a result, its output is used as input for the calculation of Weights of Evidence coefficients. ​ This model calculates ranges in order to categorize continuous gray-tone variables for deriving the Weights of Evidence. It selects the number of intervals and their buffer sizes aiming to better preserve the data structure. See the help for a further description of this method. As a result, its output is used as input for the calculation of Weights of Evidence coefficients. ​
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 In addition to the initial and final landscape maps, this model receives a raster cube composed of a series of static maps, e.g. vegetation, soil, altitude (they are named so because they do not change during model iteration. A raster cube encompasses a set of co-registered map layers. ​ In addition to the initial and final landscape maps, this model receives a raster cube composed of a series of static maps, e.g. vegetation, soil, altitude (they are named so because they do not change during model iteration. A raster cube encompasses a set of co-registered map layers. ​
-Open the file ''​23267statics.ers''​ from ''​\setup_run_and_validate_a_lucc_model\originals''​ on the Map Viewer. An option to select the layer will appear on the Maps (right of window), it has a combobox into field Layer to select it. Change the layer to examine the other maps. <note tip>​**TIP**:​ you can build a raster cube assembling a set of co-registered raster maps through the functor //[[:Create Cube Map]]// and extract a layer from the cube using the functor //​[[:​Extract Map Layer]]//. Cube raster data are only supported in ER format.</​note>​+Open the file ''​23267statics.ers''​ from ''​\Guidebook_Dinamica_5\Database\LUCC_files''​ on the Map Viewer. An option to select the layer will appear on the Maps (right of window), it has a combobox into field Layer to select it. Change the layer to examine the other maps. <note tip>​**TIP**:​ you can build a raster cube assembling a set of co-registered raster maps through the functor //[[:Create Cube Map]]// and extract a layer from the cube using the functor //​[[:​Extract Map Layer]]//. Cube raster data are only supported in ER format.</​note>​
  
 Furthermore,​ Dinamica EGO can incorporate dynamic layers into the simulation, which are so-called because they are updated during model iteration. For this model you will include the variable "​distance to previously deforested areas" as a dynamic map.  For this purpose, the model employs the functor //[[:Calc Distance Map]]//. Open it with the Edit Functor Ports. ​ Furthermore,​ Dinamica EGO can incorporate dynamic layers into the simulation, which are so-called because they are updated during model iteration. For this model you will include the variable "​distance to previously deforested areas" as a dynamic map.  For this purpose, the model employs the functor //[[:Calc Distance Map]]//. Open it with the Edit Functor Ports. ​
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 ==== Third step: Calculating Weights of Evidence coefficients ==== ==== Third step: Calculating Weights of Evidence coefficients ====
  
-Open the model ''​determine_weights_of_evidence_coefficients.egoml''​ located in ''​setup_run_and_validate_a_lucc_model\3_weights_of_evidence_coefficient_calculation''​.+Open the model ''​determine_weights_of_evidence_coefficients.egoml''​ located in ''​\Guidebook_Dinamica_5\Models\LUCC_model\3_weights_of_evidence_coefficient_calculation''​.
  
 {{ :​tutorial:​lucc_19.jpg |}} {{ :​tutorial:​lucc_19.jpg |}}
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 The only assumption for the Weights of Evidence method is that the input maps have to be spatially independent. A set of measures can be applied to assess this assumption, such as the Cramer test and the Joint-Uncertainty Information [[https://​books.google.com/​books?​printsec=frontcover&​vid=ISBN0080424201&​vid=ISBN0080418678&​vid=LCCN94028315#​v=onepage&​q&​f=false|(Bonham-Carter,​ 1994)]]. As a result, correlated variables must be disregarded or combined into a third that will replace the correlated pair in the model. ​ The only assumption for the Weights of Evidence method is that the input maps have to be spatially independent. A set of measures can be applied to assess this assumption, such as the Cramer test and the Joint-Uncertainty Information [[https://​books.google.com/​books?​printsec=frontcover&​vid=ISBN0080424201&​vid=ISBN0080418678&​vid=LCCN94028315#​v=onepage&​q&​f=false|(Bonham-Carter,​ 1994)]]. As a result, correlated variables must be disregarded or combined into a third that will replace the correlated pair in the model. ​
  
-Open model ''​weights_of_evidence_correlation.egoml''​ in ''​setup_run_and_validate_a_lucc_ model\4_weights_of_evidence_correlation''​.+Open model ''​weights_of_evidence_correlation.egoml''​ in ''​\Guidebook_Dinamica_5\Models\LUCC_model\4_weights_of_evidence_correlation''​.
  
 The model in ''​3_and_2_weights_of_evidence_ranges_and_coefficient_calculation''​ corresponds to steps 2 and 3 joined together. The model in ''​3_and_2_weights_of_evidence_ranges_and_coefficient_calculation''​ corresponds to steps 2 and 3 joined together.
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 Although there is no agreement on what threshold should be used to exclude a variable, all tests highlight a high correlation for this pair of variables. Hence you must exclude one of these. Let’s remove "​d_major_rivers"​. Delete the variable from the Weights of Evidence file using its graphical editor. Open it on the //[[:Load Weights]]// using the eye icon.  Although there is no agreement on what threshold should be used to exclude a variable, all tests highlight a high correlation for this pair of variables. Hence you must exclude one of these. Let’s remove "​d_major_rivers"​. Delete the variable from the Weights of Evidence file using its graphical editor. Open it on the //[[:Load Weights]]// using the eye icon. 
  
-Now save the WEOFE coefficients as ''​new_weights.dcf''​ in the folder ''​\setup_run_and_validate_a_lucc_model\4_weights_of_evidence_correlation''​.+Now save the WEOFE coefficients as ''​new_weights.dcf''​ in the folder ''​\Guidebook_Dinamica_5\Models\LUCC_model\4_weights_of_evidence_correlation''​.
  
 Great, you have come through the calibration process, now you can start setting up the simulation model. Let’s move forward. Great, you have come through the calibration process, now you can start setting up the simulation model. Let’s move forward.
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 The method we apply here is a modification of the latter and named in Dinamica EGO as //[[:Calc Reciprocal Similarity Map]]//. This method employs an exponential decay function with distance to weight the cell state distribution around a central cell (See scheme in fig. 5 about the Fuzzy comparison method and then go to Help for further details on this method). The method we apply here is a modification of the latter and named in Dinamica EGO as //[[:Calc Reciprocal Similarity Map]]//. This method employs an exponential decay function with distance to weight the cell state distribution around a central cell (See scheme in fig. 5 about the Fuzzy comparison method and then go to Help for further details on this method).
  
-Open the model ''​determine-similarity-of-differences.egoml''​ in ''​\6_validate_using_exponential_ decay_function folder''​. ​+Open the model ''​determine-similarity-of-differences.egoml''​ in ''​\Guidebook_Dinamica_5\Models\LUCC_model\6_validate_using_exponential_decay_function''​. ​
  
 {{ :​tutorial:​lucc_35.2.jpg |}} {{ :​tutorial:​lucc_35.2.jpg |}}