;; Landscape Diversity model: ;; Generating random landscape and measuring diversity ;; ;; by Ervin Wirth ;; Contact: wirthervin@gmail.com globals [ potential ] breed [ scouts scout ] patches-own [ cluster ] scouts-own [ memory path ] ;; Generating a landscape to setup clear-all let dim 100 let ext dim ^ 2 resize-world 0 (dim - 1) 0 (dim - 1) set-patch-size 4 ask patches [ set cluster nobody ] ;; Define the number of seed points from the slider let seedcount dim * dim * seed-percent let seedlist [] ;; Generate a list for the land cover seeds ;; Notice: The sum would converge to the seedcount by sampling while [length seedlist != land-cover-classes][ set seedlist lput (random (seedcount / land-cover-classes * 2) + 1) seedlist ] ;; Criteria and scaling on very high seed counts if sum seedlist > ext [ let c ext / sum seedlist set seedlist map floor map [c * ?] seedlist let change (item 0 seedlist + ext - sum seedlist) set seedlist replace-item 0 seedlist change ] ;; Scatter seed let i 0 foreach seedlist [ set i i + 1 repeat ? [ ask one-of (patches with [cluster = nobody])[ set cluster i set pcolor 3 + 10 * i ] ] ] ;; Fill the voids by propagation ;; Credit: Uri Wilensky, Patch Cluster Example while [any? patches with [cluster = nobody]] [ ask patches with [cluster = nobody][ let c [cluster] of one-of neighbors4 if c != nobody [ set cluster c set pcolor 3 + 10 * c ] ] ] reset-ticks end ;; Distribute scout agents on the ground to setup-scouts reset-ticks clear-plot clear-drawing clear-turtles create-scouts num-scouts [ set color black set heading random 360 set xcor random-xcor set ycor random-ycor set size 2 set memory [] set memory lput pcolor memory set potential 0 set path [] set path lput list xcor ycor path pen-down ] end ;; Scouting with agents to scouting let i 0 while [i < scouting-interval][ ask scouts [ ;; Generating a random float to the optional stohastic nature let rf random-float 1 ;; Rotations by cases ifelse any? patches in-cone 50 120 with [not member? pcolor [memory] of myself] ;; If there is a new land cover type in the view [ifelse rf < (1 - stohastic-noise) ;; Turn to its closest part [face min-one-of patches in-cone 50 120 with [not member? pcolor [memory] of myself] [distance myself]] ;; Otherwise change the direction [rt random-normal 0 120] ] ;; If there isn't, then change the direction [ rt random-normal 0 120 ] ;; Step in the right direction fd 1 ;; Save the position set path lput list xcor ycor path ;; Save to memory only if it is new if not member? pcolor memory [set memory lput pcolor memory set potential potential + 1] ] set i i + 1 tick ] end @#$#@#$#@ GRAPHICS-WINDOW 191 53 601 484 -1 -1 4.0 1 10 1 1 1 0 0 0 1 0 99 0 99 0 0 1 ticks 30.0 SLIDER 6 79 181 112 land-cover-classes land-cover-classes 1 12 6 1 1 class HORIZONTAL BUTTON 42 124 139 157 Setup world setup NIL 1 T OBSERVER NIL NIL NIL NIL 1 SLIDER 610 80 782 113 num-scouts num-scouts 1 200 1 1 1 scout HORIZONTAL BUTTON 647 196 750 229 Setup scouts setup-scouts NIL 1 T OBSERVER NIL NIL NIL NIL 1 SLIDER 610 156 782 189 scouting-interval scouting-interval 0 200 100 10 1 NIL HORIZONTAL BUTTON 647 236 750 269 Scouting scouting NIL 1 T OBSERVER NIL NIL NIL NIL 1 SLIDER 610 118 782 151 stohastic-noise stohastic-noise 0 1 0 0.1 1 NIL HORIZONTAL SLIDER 191 10 601 43 seed-percent seed-percent 0.001 1 0.0010 0.001 1 NIL HORIZONTAL INPUTBOX 96 10 181 70 seed-percent 0.0010 1 0 Number INPUTBOX 611 10 682 70 num-scouts 1 1 0 Number MONITOR 647 281 709 326 Potential potential 17 1 11 PLOT 612 333 812 483 Potential in time tick potential 0.0 100.0 0.0 1.0 true false "" "" PENS "default" 1.0 0 -16777216 true "" "plot potential" @#$#@#$#@ ## WHAT IS IT? The model's goal is to introduce an agent-based method for landscape diversity measurements. The landscape's structural complexity depends on the composition and the configuration as well. The composition refers to the number and occurrence of different landscape types while the configuration means the physical distribution or spatial character within a landscape. (McGarigal et al. 1994) By the landscape discovery agents (scouts) we collect the different kind of land covers into a common sum as diversity potential. It gives a result in real complixity (on low abstraction level), which includes composition and configuration. The model describes the landscape diversity as a living creature, instead of decomposition the problem to different indices and metrics - like edge density, number of classes, patch number, Shannon index, Moran’s I. ## HOW IT WORKS The scouts have a memory, since they individually stores those land cover what they have seen. In addition they search in a conic shape of view with a given distance. Their task is the collection of different land cover types as fast as possible. If they discover something new land cover, then one potential unit is added to the world's landscape diversity potential (as Monte Carlo integral). ## HOW TO USE IT First you have to click ‘Setup world’, before this part you can predefine the number of classes, and the graininess (seed percent) of the landscape. Then the agents (here scouts) must be placed with the ‘Setup scouts’ button. At this step you can vary the parameters of the scout discovery, for example you can put a stochastic uncertainty on them. Finally you can run the simulation with the Scouting button. ## THINGS TO TRY First you should generate a world with the initial setting, and test the model with only one scout. The scout's path helps you to understand the underlying behaviour. You can check that the last potential is equal with the number of the discovered land cover types (original land cover not included). Then you can create a more complex world with a bit higher seed percent (e.g. 0.01) and land cover class (e.g. 10). Run the simulation with 50 scouts for 100 ticks, and note down the potential what you got. Without changing the agent parameters rerun the discovery on a less diverse landscape (have only 3 kind of land cover) and compare the results. ## EXTENDING THE MODEL The model is expandable, we could order higher scores for particular land covers like forests. An elevation model could be added to model. It could have an effect on the agents' speed or their visibility distance. The simulation time could be changed to agent energy. For example, they would have an initial energy which increases by discovering land cover types, and decreases by moving or rotating. The user can import and analyse real landscape models through NetLogo's gis extension. ## CREDITS AND REFERENCES Wirth, E., Szabó, Gy., and Czinkóczky, A.: MEASURE OF LANDSCAPE HETEROGENEITY BY AGENT-BASED METHODOLOGY, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 145-151, http://dx.doi.org/10.5194/isprs-annals-III-8-145-2016, 2016. McGarigal, K., Marks, B.: Fragstats: Spatial Pattern Analysis Program for Quantifying Landscape Structure. Forest Science Department, Oregon State University, Corvallis, 1994 Wilensky, U.: NetLogo, http://ccl.northwestern.edu/netlogo/. 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