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1. Elegir la mejor clasificación.
- Debe estar en .tif
- Si es la no-supervisada, recodificarla en las N-clases.
Si es una supervisada, es mejor tener una única banda (sólo la clasificación) (ver padlet anterior).

2. Evaluar si filtrarla.
En QGIS con SCP: página 11 de la guia del TP 8.
En QGIS con SAGA: Majority filter (buscar por Caja de herramientas).
En R, página 26 de la guía del TP 9 (función "focal")

3. Preparar el archivo txt descriptor de las clases (página 14 de la guía TP 9) y ya estamos listos para pasar a FRAGSTAT y seguir la guia...

4. En FRAGSTAT, familiarizarse con el soft y calcular índices por clase y por paisaje, sin estrategia de muestreo.

5. En FRAGSTAT, con una estrategia de muestreo por celdas, comparar con algunos índices de clase o paisaje

6. Gráficos en Excel o R

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Está hecho con el soft NetLogo, "modelo basado en agentes"

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1. Para explicar el comportamiento de los Fuegos que ocurren en muchas partes del país se puede recurrir a la percolación: cuando la densidad del combustible (pasto seco/arboles secos) es menor del 60% aunque se prendan focos el fuego no se extiende: aquí se ve con el 57%

# data[].attributes.wish_content.attachment_props.original_text
1. Para explicar el comportamiento de los Fuegos que ocurren en muchas partes del país se puede recurrir a la percolación: cuando la densidad del combustible (pasto seco/arboles secos) es menor del 60% aunque se prendan focos el fuego no se extiende: aquí se ve con el 57% https://t.co/1bKNvHRZda

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1. Para explicar el comportamiento de los Fuegos que ocurren en muchas partes del país se puede recurrir a la percolación: cuando la densidad del combustible (pasto seco/arboles secos) es menor del 60% aunque se prendan focos el fuego no se extiende: aquí se ve con el 57% pic.twitter.com/1bKNvHRZda
— Leonardo Saravia (@larysar) August 24, 2020

# data[].attributes.attachment_link.metadata.attachment_props_source.data.text
1. Para explicar el comportamiento de los Fuegos que ocurren en muchas partes del país se puede recurrir a la percolación: cuando la densidad del combustible (pasto seco/arboles secos) es menor del 60% aunque se prendan focos el fuego no se extiende: aquí se ve con el 57% https://t.co/1bKNvHRZda

# data[].attributes.attachment_link.metadata.attachment_props.original_text
1. Para explicar el comportamiento de los Fuegos que ocurren en muchas partes del país se puede recurrir a la percolación: cuando la densidad del combustible (pasto seco/arboles secos) es menor del 60% aunque se prendan focos el fuego no se extiende: aquí se ve con el 57% https://t.co/1bKNvHRZda

# data[].attributes.attachment_link.metadata.attachment_props.text
1. Para explicar el comportamiento de los Fuegos que ocurren en muchas partes del país se puede recurrir a la percolación: cuando la densidad del combustible (pasto seco/arboles secos) es menor del 60% aunque se prendan focos el fuego no se extiende: aquí se ve con el 57%

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Overview

landscapemetrics is an R package for calculating landscape metrics for categorical landscape patterns in a tidy workflow. The package can be used as a drop-in replacement for FRAGSTATS (McGarigal et al. 2012), as it offers a reproducible workflow for landscape analysis in a single environment. It also allows for calculations of four theoretical metrics of landscape complexity: a marginal entropy, a conditional entropy, a joint entropy, and a mutual information (Nowosad and Stepinski 2019).

landscapemetrics supports raster spatial objects and takes RasterLayer, RasterStacks, RasterBricks or lists of RasterLayer as input arguments. Every function can be used in a piped workflow, as it always takes the data as the first argument and returns a tibble.

Citation

To cite landscapemetrics or acknowledge its use, please cite the following Software note, substituting the version of the application that you used for 'ver. 0':
Hesselbarth, M.H.K., Sciaini, M., With, K.A., Wiegand, K., Nowosad, J. 2019. landscapemetrics: an open‐source R tool to calculate landscape metrics. Ecography, 42: 1648-1657 (ver. 0).
For more information see Publication record vignette. The get a BibTex entry, please use citation("landscapemetrics").

Installation

There are several ways to install landscapemetrics:

# Get the stable version from CRAN install.packages ( "landscapemetrics" ) # Alternatively, you can install the development version from Github # install.packages("devtools") devtools :: install_github ( "r-spatialecology/landscapemetrics" )

Announcement

Due to an improved connected-component labelling algorithm ( landscapemetrics v1.4 and higher), patches are labeled in a different order than before and therefore different patch IDs might be used compared to previous versions. However, results for all metrics are identical.

Using landscapemetrics

The resolution of a raster cell has to be in meters, as the package converts units internally and returns results in either meters, square meters or hectares. Before using landscapemetrics, be sure to check your raster (see check_raster()).

All functions in landscapemetrics start with lsm_ (for landscapemetrics). The second part of the name specifies the level (patch - p, class - c or landscape - l). The last part of the function name is the abbreviation of the corresponding metric (e.g. enn for the euclidean nearest-neighbor distance):

All functions return an identical structured tibble:

Using metric functions

Every function follows the same implementation design, so the usage is quite straightforward:

 # calculate for example the Euclidean nearest-neighbor distance on patch level ( landscape ) #> [90m# A tibble: 27 x 6[39m #> layer level class id metric value #> [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<dbl>[39m[23m #> [90m 1[39m 1 patch 1 1 enn 7 #> [90m 2[39m 1 patch 1 2 enn 4 #> [90m 3[39m 1 patch 1 3 enn 2.83 #> [90m 4[39m 1 patch 1 4 enn 2 #> [90m 5[39m 1 patch 1 5 enn 2 #> [90m 6[39m 1 patch 1 6 enn 2.83 #> [90m 7[39m 1 patch 1 7 enn 4.12 #> [90m 8[39m 1 patch 1 8 enn 4.12 #> [90m 9[39m 1 patch 1 9 enn 4.24 #> [90m10[39m 1 patch 2 10 enn 4.47 #> [90m# ... with 17 more rows[39m # calculate the total area and total class edge length ( landscape ) #> [90m# A tibble: 1 x 6[39m #> layer level class id metric value #> [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<dbl>[39m[23m #> [90m1[39m 1 landscape [31mNA[39m [31mNA[39m ta 0.09 ( landscape ) #> [90m# A tibble: 3 x 6[39m #> layer level class id metric value #> [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<dbl>[39m[23m #> [90m1[39m 1 class 1 [31mNA[39m te 180 #> [90m2[39m 1 class 2 [31mNA[39m te 227 #> [90m3[39m 1 class 3 [31mNA[39m te 321

There is also a wrapper around every metric in the package to quickly calculate a bunch of metrics:

# calculate all metrics on patch level calculate_lsm ( landscape, level = "patch" ) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> [90m# A tibble: 324 x 6[39m #> layer level class id metric value #> [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<dbl>[39m[23m #> [90m 1[39m 1 patch 1 1 area 0.000[4m1[24m #> [90m 2[39m 1 patch 1 2 area 0.000[4m5[24m #> [90m 3[39m 1 patch 1 3 area 0.014[4m8[24m #> [90m 4[39m 1 patch 1 4 area 0.000[4m1[24m #> [90m 5[39m 1 patch 1 5 area 0.000[4m1[24m #> [90m 6[39m 1 patch 1 6 area 0.001[4m4[24m #> [90m 7[39m 1 patch 1 7 area 0.000[4m3[24m #> [90m 8[39m 1 patch 1 8 area 0.000[4m5[24m #> [90m 9[39m 1 patch 1 9 area 0.000[4m1[24m #> [90m10[39m 1 patch 2 10 area 0.003[4m5[24m #> [90m# ... with 314 more rows[39m

Utility functions

landscapemetrics further provides several visualization functions, e.g. show all labeled patches or the core area of all patches. All visualization functions start with the prefix show_ (e.g. ).

Important building blocks of the package are exported to help facilitate analysis or the development of new metrics. They all start with the prefix get_. All of them are implemented with Rcpp and have either memory or performance advantages compared to raster functions.

For more details, see the utility function vignette.

Contributing

One of the major motivations behind landscapemetrics is the idea to provide an open-source code collection of landscape metrics. This includes, besides bug reports, especially the idea to include new metrics and functions. Therefore, in case you want to suggest new metrics or functions and in the best case even contribute code, we warmly welcome to do so! For more information see CONTRIBUTING.

Maintainers and contributors must follow this repository's CODE OF CONDUCT.

# data[].attributes.attachment_link.title
Landscape Metrics for Categorical Map Patterns

# data[].attributes.attachment_link.description
Calculates landscape metrics for categorical landscape patterns in a tidy workflow. landscapemetrics reimplements the most common metrics from FRAGSTATS ( ) and new ones from the current literature on landscape metrics. This package supports raster spatial objects and takes RasterLayer, RasterStacks, RasterBricks or lists of RasterLayer from the raster package as input arguments.

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Manual de NLMR para modelos neutrales en R

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Manual de NLMR para modelos neutrales en R

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NLMR is an R package for simulating neutral landscape models (NLM). Designed to be a generic framework like NLMpy, it leverages the ability to simulate the most common NLM that are described in the ecological literature. NLMR builds on the advantages of the raster package and returns all simulation as RasterLayer objects, thus ensuring a direct compatibility to common GIS tasks and a flexible and simple usage. Furthermore, it simulates NLMs within a self-contained, reproducible framework.

Installation

Install the release version from CRAN:

To install the developmental version of NLMR, use the following R code:

Example

Each neutral landscape models is simulated with a single function (all starting with nlm_) in NLMR, e.g.:

Overview

NLMR supplies 15 NLM algorithms, with several options to simulate derivatives of them. The algorithms differ from each other in spatial auto-correlation, from no auto-correlation (random NLM) to a constant gradient (planar gradients):

See also

NLMR was split during its development process - to have a minimal dependency version for simulating neutral landscape models and an utility toolbox to facilitate workflows with raster data. If you are interested in merging, visualizing or further handling neutral landscape models have a look at the landscapetools package.

# data[].attributes.attachment_link.description
Provides neutral landscape models ( , ). Neutral landscape models range from "hard" neutral models (completely random distributed), to "soft" neutral models (definable spatial characteristics) and generate landscape patterns that are independent of ecological processes. Thus, these patterns can be used as null models in landscape ecology.