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R-package/hasa/vignettes/images/results.png -- GitLab From 0e063fc624f1f0e31cf9d04da60a2872fa2b6742 Mon Sep 17 00:00:00 2001 From: Romulo Goncalves Date: Mon, 28 Mar 2022 14:54:51 +0200 Subject: [PATCH 02/15] Rename file. --- R-package/hasa/vignettes/{README.md => HabitatSampler.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename R-package/hasa/vignettes/{README.md => HabitatSampler.md} (100%) diff --git a/R-package/hasa/vignettes/README.md b/R-package/hasa/vignettes/HabitatSampler.md similarity index 100% rename from R-package/hasa/vignettes/README.md rename to R-package/hasa/vignettes/HabitatSampler.md -- GitLab From 40fcb669d7bda6c9fd02a48d524f293c27957eef Mon Sep 17 00:00:00 2001 From: Romulo Goncalves Date: Mon, 28 Mar 2022 14:56:02 +0200 Subject: [PATCH 03/15] Generate HabitatSampler.md instead of a README. --- R-package/hasa/vignettes/HabitatSampler.Rmd | 1 - 1 file changed, 1 deletion(-) diff --git a/R-package/hasa/vignettes/HabitatSampler.Rmd b/R-package/hasa/vignettes/HabitatSampler.Rmd index b3fdb3a..840f01b 100644 --- a/R-package/hasa/vignettes/HabitatSampler.Rmd +++ b/R-package/hasa/vignettes/HabitatSampler.Rmd @@ -7,7 +7,6 @@ output: toc: true toc_depth: 2 md_document: - pandoc_args: ["--output", "README.md"] toc: true toc_depth: 2 variant: gfm -- GitLab From 6a5134ec0153bf049ac24144f81f8d471b7788c7 Mon Sep 17 00:00:00 2001 From: Romulo Goncalves Date: Mon, 28 Mar 2022 14:56:19 +0200 Subject: [PATCH 04/15] Remove redundant information. --- .../hasa/vignettes/Habitat_Sampler_Usage.Rmd | 86 ------------------- 1 file changed, 86 deletions(-) delete mode 100644 R-package/hasa/vignettes/Habitat_Sampler_Usage.Rmd diff --git a/R-package/hasa/vignettes/Habitat_Sampler_Usage.Rmd b/R-package/hasa/vignettes/Habitat_Sampler_Usage.Rmd deleted file mode 100644 index 45d2837..0000000 --- a/R-package/hasa/vignettes/Habitat_Sampler_Usage.Rmd +++ /dev/null @@ -1,86 +0,0 @@ ---- -title: "Habitat_Sampler_Usage" -author: "Carsten Neumann" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{Habitat_Sampler_Usage} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- -## Workflow of Habitat Sampling and Probability Mapping -```{r setup} -library(HaSa) -``` - -```R -multi_Class_Sampling(...) -``` -## step 1 - -**A)** an interactive map is plotted in a web browser (firefox for linux), containing: -a) background map -b) RGB image -c) selected habitat type map -d) probaility threshold on mouse hover -e) predictive distance - -**B)** the user has to decide to extract this habitat type on the basis of a threshold **(B.1)** or to sample again **(B.2)** - -### B.1 -``` -enter threshold in R console -``` -6 files are saved to disk for the selected habitat type -a) HabitatSampler object (Run) - R Binary -b) probability map - *.kml, *.png, geocoded *.tif -c) threshold list - R Binary -d) leaflet interactive web interface - *.html - -after habitat extraction is done the user have to decide to adjust starting number of samples and number of models or proceed automaticlay to the next step -``` -enter sample/model adjsutement (../..) or auto (0) in R console -``` -## step 2 ... proceed with A) - -### B.2 -``` -enter 0 in R console -``` -the user have to decide to adjust starting number of samples and number of models or proceed automaticlay to new sampling -``` -enter sample/model adjsutement (../..) or auto (0) in R console -``` -...proceed with A until decision (B.1) has made - -## step 2 ... proceed with A) - --------- -### if convergence fails / no models can be selected / num_samples are to little / or another error occurs, restart next step with: -```R -multi_Class_Sampling(in_raster = out.raster, reference = out.reference, class_names = out.names, ... ) -``` -step = specify next step number - --------- -## remarks -1) the results from previous steps are reproducable when using the same seed value and int.seed=Run@seeds (e.g. Run02@seeds) in consequence, init.sample for regular sampling determines an invariant sample distribution, use random sampling or vary init.sample to get varying sample distributions -2) regular sampling is faster -3) last = T can be set when only one class should be separated from the background pixels -4) The R object Run holds slots of: -models = selected classifiers -ref_samples = spatial points of selected samples (see ?write_Out_Samples.r) -switch = the target class is [2] if switch is not NA then the target class must be changed from [1] to [2] (see write_Out_Samples.r) -layer = raster layer of habitat type probability -mod_all = all classifiers from num_models -class_ind = predictive distance metric for all classes -seeds = seeds to reproduce respecitve step/habitat type sampling -5) if multi_test > 1 the user will get multiple maps and will be ask to enter the number of the probability distribution that is apropriate - -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>" -) -``` - - -- GitLab From 8afab9f4786f61cf56c70a390e6874170948c700 Mon Sep 17 00:00:00 2001 From: Romulo Pereira Goncalves Date: Mon, 28 Mar 2022 14:59:49 +0200 Subject: [PATCH 05/15] We moved the documentation to R-package/hasa/vignettes --- .gitlab-ci.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index d1c9f52..d1219a1 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -23,8 +23,8 @@ pages: # this job must be called 'pages' to advise GitLab to upload content to - mkdir -p public/images/ # Copy over the docs - - cp -r docs/*.html public/index.html - - cp -r docs/images/* public/images/ + - cp -r R-package/hasa/vignettes/*.html public/index.html + - cp -r R-package/hasa/vignettes/images/* public/images/ # Check if everything is working great - ls -al public @@ -36,5 +36,5 @@ pages: # this job must be called 'pages' to advise GitLab to upload content to expire_in: 30 days only: - master - - documentation + - improve_documentation -- GitLab From 0786769cb590a7a77c93f2876f81e9b17557a89b Mon Sep 17 00:00:00 2001 From: Romulo Pereira Goncalves Date: Mon, 28 Mar 2022 15:03:36 +0200 Subject: [PATCH 06/15] Update and fix some URLs --- README.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.rst b/README.rst index b34fb8a..37b6d5b 100644 --- a/README.rst +++ b/README.rst @@ -12,7 +12,7 @@ How to use 1. R package ----------------------- * You need R to install the **HaSa package** that includes all functions and demo data. -* The installation steps for R version **4.1.0** are defined in `Section 2.0 of the documentation `_. +* The installation steps for R version **4.1.0** are defined in `Section 2.0 of the documentation `_. * For Ubuntu systems the following system packages dependencies need to be installed: .. code-block:: @@ -21,14 +21,14 @@ How to use * For Windows operating systems the `Rtools `_ are needed * library(HaSa) and list datasets: data(package="HaSa") and functions: lsf.str("package:HaSa") or use library(help="HaSa") -* Information about program execution and function behavior is available in Rmarkdown: `HabitatSampler_Usage `_ +* Information about program execution and function behavior is available in Rmarkdown: `HabitatSampler_Usage `_ 2. Stepwise Procedure ---------------------------------- * The **demo** directory provides a step-wised procedure via an R script: **HabitatSampler.r**, but also via a Jupyter notebook **HabitatSampler.ipynb**. -* All necessary data and information is available under the directory: `demo `_ -* For documentation please check the `docs `_ directory. +* All necessary data and information is available under the directory: `demo `_ +* For documentation please check the `HaSa vignettes `_ directory. Input ---------------- @@ -72,4 +72,4 @@ Credits HaSa was developed by Carsten Neumann (Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences) within the context of the `NaTec - KRH `__ project funded by the German Federal Ministry of Education and Research (BMBF) (grant number: 01 LC 1602A). -The test data represent pre-processed Copernicus Sentinel-2 satellite imagery (ESA 2018). Pre-processing was done using `GTS2 `__ and `AROSICS `__. +The test data represent pre-processed Copernicus Sentinel-2 satellite imagery (ESA 2018). Pre-processing was done using `GTS2 `__ and `AROSICS `__. -- GitLab From f7d1429dc87dd3c423d2ac8a41989dc724a643d7 Mon Sep 17 00:00:00 2001 From: Romulo Pereira Goncalves Date: Mon, 28 Mar 2022 15:05:06 +0200 Subject: [PATCH 07/15] Fix more paths --- README.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index 37b6d5b..eddead4 100644 --- a/README.rst +++ b/README.rst @@ -1,4 +1,4 @@ -.. figure:: docs/images/Logo.png +.. figure:: R-package/hasa/vignettes/images/Logo.png :target: https://github.com/carstennh/HabitatSampler/tree/master/demo :align: center @@ -21,14 +21,14 @@ How to use * For Windows operating systems the `Rtools `_ are needed * library(HaSa) and list datasets: data(package="HaSa") and functions: lsf.str("package:HaSa") or use library(help="HaSa") -* Information about program execution and function behavior is available in Rmarkdown: `HabitatSampler_Usage `_ +* Information about program execution and function behavior is available in Rmarkdown: `HabitatSampler_Usage `_ 2. Stepwise Procedure ---------------------------------- * The **demo** directory provides a step-wised procedure via an R script: **HabitatSampler.r**, but also via a Jupyter notebook **HabitatSampler.ipynb**. * All necessary data and information is available under the directory: `demo `_ -* For documentation please check the `HaSa vignettes `_ directory. +* For documentation please check the `HaSa vignettes `_ directory. Input ---------------- -- GitLab From bd23dc5dbf291525fa6de553a3ba601c0236b571 Mon Sep 17 00:00:00 2001 From: Romulo Pereira Goncalves Date: Mon, 28 Mar 2022 15:06:13 +0200 Subject: [PATCH 08/15] Fix more paths for the images. --- README.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.rst b/README.rst index eddead4..11b2c67 100644 --- a/README.rst +++ b/README.rst @@ -40,7 +40,7 @@ Output ---------------- * **Interactive Maps** of class type probabilities -.. image:: docs/images/figure_1.jpg +.. image:: R-package/hasa/vignettes/images/figure_1.jpg :width: 700px @@ -50,7 +50,7 @@ Output * the classes are referred to as class types -.. image:: docs/images/figure_2.jpg +.. image:: R-package/hasa/vignettes/images/figure_2.jpg :width: 450px Key Features -- GitLab From 57a6c6f494e901cfa8ef8ef4eefb0a0cb1d45ee9 Mon Sep 17 00:00:00 2001 From: Romulo Goncalves Date: Tue, 19 Apr 2022 15:15:00 +0200 Subject: [PATCH 09/15] Fix path and double section. --- R-package/hasa/vignettes/HabitatSampler.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R-package/hasa/vignettes/HabitatSampler.Rmd b/R-package/hasa/vignettes/HabitatSampler.Rmd index 58b3688..25dd2bd 100644 --- a/R-package/hasa/vignettes/HabitatSampler.Rmd +++ b/R-package/hasa/vignettes/HabitatSampler.Rmd @@ -48,7 +48,7 @@ The Sentinel-2 data are downloaded and processed using the German Centre for Geo library(tools) #install.packages("kableExtra") library(kableExtra) -wd <- paste(tools::file_path_as_absolute("./"), "/../demo/", sep = "") +wd <- paste(tools::file_path_as_absolute("./"), "/../../../demo/", sep = "") metadat <- read.csv(paste(wd, "Data/S2_stack_metadata.csv", sep = ""), header = T, sep = ",") colnames(metadat) <- c("", "Band 2", "Band 3", "Band 4", "Band 5", "Band 6", "Band 7", "Band 8", "Band 11", "Band 12") metadat[1,] <- c("Date","Blue", "Green", "Red", "Red Edge 1", "Red Edge 2", "Red Edge 3", "NIR", "SWIR 1", "SWIR 2") @@ -272,7 +272,7 @@ Habitat sampling output. R object that contains: seeds # seeds to reproduce respective step/habitat type sampling ``` -### 4.1.2 Interactive probability maps and downloading output +### 4.1.4 Interactive probability maps and downloading output An interactive map is plotted in a web browser (e.g., Firefox for Linux) containing a selected habitat type. The number of models predicting this habitat type can be viewed by hovering the mouse over the map. ![](./images/inter_map_ex_new.png){width=65%} -- GitLab From 1b0833aeeb4532dcb230f5ad57a670481adb4cd0 Mon Sep 17 00:00:00 2001 From: Romulo Goncalves Date: Tue, 19 Apr 2022 15:21:31 +0200 Subject: [PATCH 10/15] Fix path. --- R-package/hasa/vignettes/HabitatSampler.Rmd | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/R-package/hasa/vignettes/HabitatSampler.Rmd b/R-package/hasa/vignettes/HabitatSampler.Rmd index 25dd2bd..39d4d47 100644 --- a/R-package/hasa/vignettes/HabitatSampler.Rmd +++ b/R-package/hasa/vignettes/HabitatSampler.Rmd @@ -6,10 +6,6 @@ output: pdf_document: toc: true toc_depth: 2 - md_document: - toc: true - toc_depth: 2 - variant: gfm html_document: theme: united highlight: tango @@ -19,6 +15,10 @@ output: collapsed: false smooth_scroll: false df_print: paged + md_document: + toc: true + toc_depth: 2 + variant: gfm header-includes: - \usepackage{caption} - \captionsetup[figure]{labelformat=empty} @@ -98,7 +98,7 @@ An important step preceding classification is to load the Sentinel-2 satellite t ## 3.1 Data directories Before loading the input data and using `HaSa`, the user needs to define a series of directory paths. They are from where `HaSa` will read input data, and store intermediates and final results. These directory paths are relative to the working directory path, i.e., `wd`. The following code sets all the paths assuming that the root path is the current directory, i.e., the `demo` directory. ```{r set wd, eval = TRUE} -wd <- paste(tools::file_path_as_absolute("./"), "/../demo/", sep = "") +wd <- paste(tools::file_path_as_absolute("./"), "/../../../demo/", sep = "") #demo data stored here dataPath <- paste(wd,"Data/", sep = "") -- GitLab From 34538a011a3252dfa973b364679a593ef503506f Mon Sep 17 00:00:00 2001 From: Romulo Goncalves Date: Tue, 19 Apr 2022 15:24:43 +0200 Subject: [PATCH 11/15] Fix sub-section number. --- R-package/hasa/vignettes/HabitatSampler.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R-package/hasa/vignettes/HabitatSampler.html b/R-package/hasa/vignettes/HabitatSampler.html index e3a732a..84b71e3 100644 --- a/R-package/hasa/vignettes/HabitatSampler.html +++ b/R-package/hasa/vignettes/HabitatSampler.html @@ -3019,7 +3019,7 @@ div.tocify {

3.1 Data directories

Before loading the input data and using HaSa, the user needs to define a series of directory paths. They are from where HaSa will read input data, and store intermediates and final results. These directory paths are relative to the working directory path, i.e., wd. The following code sets all the paths assuming that the root path is the current directory, i.e., the demo directory.

-
wd <- paste(tools::file_path_as_absolute("./"), "/../demo/", sep = "")
+
wd <- paste(tools::file_path_as_absolute("./"), "/../../../demo/", sep = "")
 
 # demo data stored here
 dataPath <- paste(wd, "Data/", sep = "")
@@ -3242,7 +3242,7 @@ div.tocify {
         seeds           # seeds to reproduce respective step/habitat type sampling
-

4.1.2 Interactive probability maps and downloading output

+

4.1.4 Interactive probability maps and downloading output

An interactive map is plotted in a web browser (e.g., Firefox for Linux) containing a selected habitat type. The number of models predicting this habitat type can be viewed by hovering the mouse over the map.

From this interactive map, the user has two choices:

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"`r Sys.Date()`" output: + md_document: + toc: true + toc_depth: 2 + variant: gfm pdf_document: toc: true toc_depth: 2 @@ -15,10 +19,6 @@ output: collapsed: false smooth_scroll: false df_print: paged - md_document: - toc: true - toc_depth: 2 - variant: gfm header-includes: - \usepackage{caption} - \captionsetup[figure]{labelformat=empty} @@ -32,9 +32,9 @@ knitr::opts_chunk$set(tidy.opts = list(width.cutoff = 75), tidy = TRUE, fig.pos \newpage # 1 Introduction -This manual introduces the Habitat Sampler (HaSa), an innovative tool that autonomously generates representative reference samples for predictive modelling of surface class probabilities. The tool can be applied to any image data that displays surface structures and dynamics of any kind at multiple spatial and temporal scales. HaSa was initially developed to classify habitat dynamics in semi-natural ecosystems but the procedure can theoretically be applied to any surface. The main innovation of the tool is that it reduces reliance on comprehensive in situ ground truth data or comprehensive training datasets which constrain accurate image classification particularly in complex scenes. +This manual introduces the Habitat Sampler (HaSa), an innovative tool that autonomously generates representative reference samples for predictive modeling of surface class probabilities. The tool can be applied to any image data that displays surface structures and dynamics of any kind at multiple spatial and temporal scales. HaSa was initially developed to classify habitat dynamics in semi-natural ecosystems but the procedure can theoretically be applied to any surface. The main innovation of the tool is that it reduces reliance on comprehensive in situ ground truth data or comprehensive training data sets which constrain accurate image classification particularly in complex scenes. -Though development of HaSa has prioritized ease of use, this documentation assume a familiarity with the R software. The document is built successively and is intended to lead you step-by-step through the HaSa procedure of generating probability and classification maps. HaSa is still in development and any suggestions or improvements are welcomed and encouraged in our [GitHub Community Version](https://git.gfz-potsdam.de/habitat-sampler/HabitatSampler.git). If questions remain please don't hesitate to contact the authors of the package. For a detailed description of the Habitat Sampler and its applications, see [Neumann et al., (2020)](https://doi.org/10.1111/ddi.13165). +Though development of HaSa has prioritized ease of use, this documentation assume a familiarity with the R software. The document is built successively and is intended to lead you step-by-step through the HaSa procedure of generating probability and classification maps. HaSa is still in development and any suggestions or improvements are welcomed and encouraged in our [GitLab Community Version](https://git.gfz-potsdam.de/habitat-sampler/HabitatSampler.git). If questions remain please don't hesitate to contact the authors of the package. For a detailed description of the Habitat Sampler and its applications, see [Neumann et al., (2020)](https://doi.org/10.1111/ddi.13165). ## 1.1 Usage The tool is implemented in R and uses Leaflet [(Cheng et al., 2019)](https://rdrr.io/cran/leaflet/) to generate interactive maps in a web browser. There are no assumptions about the input image data and there are no constraints for the spectral-temporal-spatial domain in which the image is sampled. The tool requires the input of a priori expert user knowledge to generate reference data about expected surface classes which are delineated in the imagery or extracted from an external spectral library. The user has the choice between image classifiers [random forest](https://doi.org/10.1023/A:1010933404324) (RF) and [support vector](https://doi.org/10.1145/130385.130401) (SV). @@ -84,7 +84,7 @@ remotes::install_git("https://git.gfz-potsdam.de/habitat-sampler/HabitatSampler. ``` ## 2.3 Load HaSa -Before the user starts using `HaSa` it is necessary to load the library and some of its dependencies. In the following example, the libraries to be loaded are passed as a list. The option `options("rgdal_show_exportToProj4_warnings"="none")` is to supress the warning messages related with the latest changes in `gdal` and `PROJ6`. +Before the user starts using `HaSa` it is necessary to load the library and some of its dependencies. In the following example, the libraries to be loaded are passed as a list. The option `options("rgdal_show_exportToProj4_warnings"="none")` is to suppress the warning messages related with the latest changes in `gdal` and `PROJ6`. ```{r load libraries, echo = TRUE, results = 'hide', message = FALSE, warning = FALSE} options("rgdal_show_exportToProj4_warnings" = "none") libraries <- c("rgdal","raster","maptools","spatialEco","randomForest","e1071", @@ -206,10 +206,11 @@ Class sampling input HaSa::multi_Class_Sampling( in_raster = raster_stk, # clipped satellite time series stack [raster brick] num_samples = 75, # starting number of spatial samples (recommended value: 75) + # for more info *See note 1 sample_type = "regular_raster", # distribution of spatial samples # ("random_raster","regular_raster", "matrix_random") - # recommended: "regular_raster" *See note 1 + # recommended: "regular_raster" *See note 2 num_models = 200, # number of models to collect (recommended value: 200) num_iterations = 10, # number of iterations for model accuracy # (recommended value:10) @@ -221,23 +222,24 @@ HaSa::multi_Class_Sampling( # recommended input: rf) num_trees = 500, # if the model is "rf" set the number of trees for the # random forest algorithm, otherwise, the parameter is - # ignored *See note 2 + # ignored *See note 3 mtry = 10, # splitting nodes (recommended: mtry < number of predictors) mod_error = 0.02, # threshold for model error until which iteration is being last = FALSE, # only TRUE for one class classifier - # recommended input: FALSE *See note 3 + # recommended input: FALSE *See note 4 last_ref_val = 1000, # default reference value for the last step (default: 1000) + # for more info *See note 5 seed = - as.integer(Sys.time()), # set seed for reproducible results *See note 4 + as.integer(Sys.time()), # set seed for reproducible results *See note 6 init_seed = "sample", # "sample" for new or use Run@seeds to reproduce previous - # steps *See note 5 + # steps *See note 7 out_path = out_path, # output path for saving results step = 1, # at which step should the procedure start, e.g. use # step = 2 if the first habitat is already extracted class_names = class_names,# vector with class names in the order of reference spectra n_classes = 7, # total number of classes to be separated multi_test = 1, # number of test runs to compare different probability - # output *See note 6 + # output *See note 8 RGB = c(19,20,21), # pallette colors for the interactive plots color = c("lightgrey", "orange", "yellow", "limegreen", "forestgreen") @@ -253,12 +255,14 @@ HaSa::multi_Class_Sampling( ) ``` -* **Note 1**: There are threes sampling strategies: `random_raster` (it uses `raster::sampleRandom`), `raster_regular` (it uses `raster::sampleRegular`), and `random_matrix` (it uses matrices and the `stats::sample` function over only existent non NaN pixels). -* **Note 2**: The default value is 500, for small number of trees (at least 1/3 of the number of predictors) use an odd number for precise prediction results. -* **Note 3**: The argument `last = T` can be set when only one class should be separated from the background pixels -* **Note 4**: For different results the `seed` should have a different value on each run (use `seed = as.numeric(Sys.time())`). To repeat a specific run the user just needs to restart R, run again the `HaSa::multi_Class_Sampling` with the same arguments, this is, keep `seed` constant, and it will get the same results for all the steps. -* **Note 5**: The results from previous steps are reproducible when using the same seed value and `int.seed=Run@seeds` (e.g. Run02@seeds) in consequence, `init.sample` for regular sampling determines an invariant sample distribution, use `random` sampling or vary `init.sample` to get varying sample distributions. -* **Note 6**: If `multi_test > 1` the user will get multiple maps and will be asked to enter the number of the probability distribution that is appropriate. +* **Note 1**: In case it is not possible to find models, increasing the number of `num_samples` and `num_models` is not always the solution. The user should also try to re-sample with a different `seed` value. +* **Note 2**: There are threes sampling strategies: `random_raster` (it uses `raster::sampleRandom`), `raster_regular` (it uses `raster::sampleRegular`), and `random_matrix` (it uses matrices and the `stats::sample` function over only existent non `NaN` pixels). The `regular_raster` -> fast: preferable at the beginning of sampling procedure. The `random_raster` -> slow: it only samples pixels with information and it is preferable to use at the final steps with few and irregular distributed pixels. The `random_matrix` -> fast: it only samples pixels with information and it is preferable to use at the final steps with few and irregular distributed pixels. +* **Note 3**: The default value is 500, for small number of trees (at least 1/3 of the number of predictors) use an odd number for precise prediction results. +* **Note 4**: The argument `last = T` can be set when only one class should be separated from the background pixels +* **Note 5**: The reference data for the pseudo class in the last step is built using the value of `last_ref_val`. In case the user gets NA as last class, the user should adjust the value of `last_ref_val` and re-sample again. +* **Note 6**: For different results the `seed` should have a different value on each run (use `seed = as.numeric(Sys.time())`). To repeat a specific run the user just needs to restart R, run again the `HaSa::multi_Class_Sampling` with the same arguments, this is, keep `seed` constant, and it will get the same results for all the steps. +* **Note 7**: The results from previous steps are reproducible when using the same seed value and `int.seed=Run@seeds` (e.g. Run02@seeds) in consequence, `init.sample` for regular sampling determines an invariant sample distribution, use `random` sampling or vary `init.sample` to get varying sample distributions. +* **Note 8**: If `multi_test > 1` the user will get multiple maps and will be asked to enter the number of the probability distribution that is appropriate. Habitat sampling output. R object that contains: ```r diff --git a/R-package/hasa/vignettes/HabitatSampler.html b/R-package/hasa/vignettes/HabitatSampler.html index 84b71e3..767ca6e 100644 --- a/R-package/hasa/vignettes/HabitatSampler.html +++ b/R-package/hasa/vignettes/HabitatSampler.html @@ -11,7 +11,7 @@ - + An Introduction to Habitat Sampler @@ -2863,7 +2863,7 @@ div.tocify {

An Introduction to Habitat Sampler

Carsten Neumann, Alison Beamish, Romulo Goncalves

-

01/06/2021

+

2022-05-05

@@ -2871,8 +2871,8 @@ div.tocify {

1 Introduction

-

This manual introduces the Habitat Sampler (HaSa), an innovative tool that autonomously generates representative reference samples for predictive modelling of surface class probabilities. The tool can be applied to any image data that displays surface structures and dynamics of any kind at multiple spatial and temporal scales. HaSa was initially developed to classify habitat dynamics in semi-natural ecosystems but the procedure can theoretically be applied to any surface. The main innovation of the tool is that it reduces reliance on comprehensive in situ ground truth data or comprehensive training datasets which constrain accurate image classification particularly in complex scenes.

-

Though development of HaSa has prioritized ease of use, this documentation assume a familiarity with the R software. The document is built successively and is intended to lead you step-by-step through the HaSa procedure of generating probability and classification maps. HaSa is still in development and any suggestions or improvements are welcomed and encouraged in our GitHub Community Version. If questions remain please don’t hesitate to contact the authors of the package. For a detailed description of the Habitat Sampler and its applications, see Neumann et al., (2020).

+

This manual introduces the Habitat Sampler (HaSa), an innovative tool that autonomously generates representative reference samples for predictive modeling of surface class probabilities. The tool can be applied to any image data that displays surface structures and dynamics of any kind at multiple spatial and temporal scales. HaSa was initially developed to classify habitat dynamics in semi-natural ecosystems but the procedure can theoretically be applied to any surface. The main innovation of the tool is that it reduces reliance on comprehensive in situ ground truth data or comprehensive training data sets which constrain accurate image classification particularly in complex scenes.

+

Though development of HaSa has prioritized ease of use, this documentation assume a familiarity with the R software. The document is built successively and is intended to lead you step-by-step through the HaSa procedure of generating probability and classification maps. HaSa is still in development and any suggestions or improvements are welcomed and encouraged in our GitLab Community Version. If questions remain please don’t hesitate to contact the authors of the package. For a detailed description of the Habitat Sampler and its applications, see Neumann et al., (2020).

1.1 Usage

The tool is implemented in R and uses Leaflet (Cheng et al., 2019) to generate interactive maps in a web browser. There are no assumptions about the input image data and there are no constraints for the spectral-temporal-spatial domain in which the image is sampled. The tool requires the input of a priori expert user knowledge to generate reference data about expected surface classes which are delineated in the imagery or extracted from an external spectral library. The user has the choice between image classifiers random forest (RF) and support vector (SV).

@@ -3005,7 +3005,7 @@ div.tocify {

2.3 Load HaSa

-

Before the user starts using HaSa it is necessary to load the library and some of its dependencies. In the following example, the libraries to be loaded are passed as a list. The option options("rgdal_show_exportToProj4_warnings"="none") is to supress the warning messages related with the latest changes in gdal and PROJ6.

+

Before the user starts using HaSa it is necessary to load the library and some of its dependencies. In the following example, the libraries to be loaded are passed as a list. The option options("rgdal_show_exportToProj4_warnings"="none") is to suppress the warning messages related with the latest changes in gdal and PROJ6.

options(rgdal_show_exportToProj4_warnings = "none")
 libraries <- c("rgdal", "raster", "maptools", "spatialEco", "randomForest", 
     "e1071", "devtools", "fasterize", "rgeos", "leaflet", "htmlwidgets", "IRdisplay", 
@@ -3178,58 +3178,62 @@ div.tocify {
 
HaSa::multi_Class_Sampling(
     in_raster = raster_stk,   # clipped satellite time series stack [raster brick]
     num_samples = 75,         # starting number of spatial samples (recommended value: 75)
-    sample_type = 
-        "regular_raster",     # distribution of spatial samples 
-                              # ("random_raster","regular_raster", "matrix_random")
-                              # recommended: "regular_raster" *See note 1
-    num_models = 200,         # number of models to collect (recommended value: 200)
-    num_iterations = 10,      # number of iterations for model accuracy
-                              # (recommended value:10)
-    buffer = 10,              # distance (in m) for new sample collection around initial
-                              # samples (depends on pixel size and image resolution)
-    reference = ref,          # table of reference spectra [data.frame]
-    model = "rf",             # which machine learning algorithm to use ("rf" random
-                              # forest or "svm" support vector machine;
-                              # recommended input: rf)
-    num_trees = 500,          # if the model is "rf" set the number of trees for the
-                              # random forest algorithm, otherwise, the parameter is
-                              # ignored *See note 2
-    mtry = 10,                # splitting nodes (recommended: mtry < number of predictors)
-    mod_error = 0.02,         # threshold for model error until which iteration is being
-    last = FALSE,             # only TRUE for one class classifier
-                              # recommended input: FALSE *See note 3
-    last_ref_val = 1000,      # default reference value for the last step (default: 1000)
-    seed = 
-      as.integer(Sys.time()), # set seed for reproducible results *See note 4
-    init_seed = "sample",     # "sample" for new or use Run@seeds to reproduce previous
-                              # steps *See note 5
-    out_path = out_path,      # output path for saving results
-    step = 1,                 # at which step should the procedure start, e.g. use 
-                              # step = 2 if the first habitat is already extracted
-    class_names = class_names,# vector with class names in the order of reference spectra
-    n_classes = 7,            # total number of classes to be separated
-    multi_test = 1,           # number of test runs to compare different probability
-                              # output *See note 6
-    RGB = c(19,20,21),        # pallette colors for the interactive plots
-    color = 
-        c("lightgrey", "orange", "yellow", "limegreen", "forestgreen")
-                              #  single colors for continuous color palette interpolation
-    in_memory = TRUE,         # boolean for raster processing (memory = "TRUE", 
-                              # from disk = "FALSE")
-    optimized_mode = TRUE     # use the optimized mode (run in_memory if possible
-                              # and use matrices instead of rasters)
-    overwrite = TRUE,         # overwrite the KML and raster files from previous runs
-    save_runs = TRUE,         # an class object is saved into disk for each run 
-                              # (default TRUE)
-    plot_on_browser = FALSE   # plot on the browser or inline in a notebook (default TRUE)
-    )

+                              # for more info *See note 1
+    sample_type = 
+        "regular_raster",     # distribution of spatial samples 
+                              # ("random_raster","regular_raster", "matrix_random")
+                              # recommended: "regular_raster" *See note 2
+    num_models = 200,         # number of models to collect (recommended value: 200)
+    num_iterations = 10,      # number of iterations for model accuracy
+                              # (recommended value:10)
+    buffer = 10,              # distance (in m) for new sample collection around initial
+                              # samples (depends on pixel size and image resolution)
+    reference = ref,          # table of reference spectra [data.frame]
+    model = "rf",             # which machine learning algorithm to use ("rf" random
+                              # forest or "svm" support vector machine;
+                              # recommended input: rf)
+    num_trees = 500,          # if the model is "rf" set the number of trees for the
+                              # random forest algorithm, otherwise, the parameter is
+                              # ignored *See note 3
+    mtry = 10,                # splitting nodes (recommended: mtry < number of predictors)
+    mod_error = 0.02,         # threshold for model error until which iteration is being
+    last = FALSE,             # only TRUE for one class classifier
+                              # recommended input: FALSE *See note 4
+    last_ref_val = 1000,      # default reference value for the last step (default: 1000)
+                              # for more info *See note 5
+    seed = 
+      as.integer(Sys.time()), # set seed for reproducible results *See note 6
+    init_seed = "sample",     # "sample" for new or use Run@seeds to reproduce previous
+                              # steps *See note 7
+    out_path = out_path,      # output path for saving results
+    step = 1,                 # at which step should the procedure start, e.g. use 
+                              # step = 2 if the first habitat is already extracted
+    class_names = class_names,# vector with class names in the order of reference spectra
+    n_classes = 7,            # total number of classes to be separated
+    multi_test = 1,           # number of test runs to compare different probability
+                              # output *See note 8
+    RGB = c(19,20,21),        # pallette colors for the interactive plots
+    color = 
+        c("lightgrey", "orange", "yellow", "limegreen", "forestgreen")
+                              #  single colors for continuous color palette interpolation
+    in_memory = TRUE,         # boolean for raster processing (memory = "TRUE", 
+                              # from disk = "FALSE")
+    optimized_mode = TRUE     # use the optimized mode (run in_memory if possible
+                              # and use matrices instead of rasters)
+    overwrite = TRUE,         # overwrite the KML and raster files from previous runs
+    save_runs = TRUE,         # an class object is saved into disk for each run 
+                              # (default TRUE)
+    plot_on_browser = FALSE   # plot on the browser or inline in a notebook (default TRUE)
+    )
    -
  • Note 1: There are threes sampling strategies: random_raster (it uses raster::sampleRandom), raster_regular (it uses raster::sampleRegular), and random_matrix (it uses matrices and the stats::sample function over only existent non NaN pixels).
    • -
  • Note 2: The default value is 500, for small number of trees (at least 1/3 of the number of predictors) use an odd number for precise prediction results.
    • -
  • Note 3: The argument last = T can be set when only one class should be separated from the background pixels
    • -
  • Note 4: For different results the seed should have a different value on each run (use seed = as.numeric(Sys.time())). To repeat a specific run the user just needs to restart R, run again the HaSa::multi_Class_Sampling with the same arguments, this is, keep seed constant, and it will get the same results for all the steps.
    • -
  • Note 5: The results from previous steps are reproducible when using the same seed value and int.seed=Run@seeds (e.g. ) in consequence, init.sample for regular sampling determines an invariant sample distribution, use random sampling or vary init.sample to get varying sample distributions.
    • -
  • Note 6: If multi_test > 1 the user will get multiple maps and will be asked to enter the number of the probability distribution that is appropriate.
    • +
  • Note 1: In case it is not possible to find models, increasing the number of num_samples and num_models is not always the solution. The user should also try to re-sample with a different seed value.
    • +
  • Note 2: There are threes sampling strategies: random_raster (it uses raster::sampleRandom), raster_regular (it uses raster::sampleRegular), and random_matrix (it uses matrices and the stats::sample function over only existent non NaN pixels). The regular_raster -> fast: preferable at the beginning of sampling procedure. The random_raster -> slow: it only samples pixels with information and it is preferable to use at the final steps with few and irregular distributed pixels. The random_matrix -> fast: it only samples pixels with information and it is preferable to use at the final steps with few and irregular distributed pixels.
    • +
  • Note 3: The default value is 500, for small number of trees (at least 1/3 of the number of predictors) use an odd number for precise prediction results.
    • +
  • Note 4: The argument last = T can be set when only one class should be separated from the background pixels
    • +
  • Note 5: The reference data for the pseudo class in the last step is built using the value of last_ref_val. In case the user gets NA as last class, the user should adjust the value of last_ref_val and re-sample again.
    • +
  • Note 6: For different results the seed should have a different value on each run (use seed = as.numeric(Sys.time())). To repeat a specific run the user just needs to restart R, run again the HaSa::multi_Class_Sampling with the same arguments, this is, keep seed constant, and it will get the same results for all the steps.
    • +
  • Note 7: The results from previous steps are reproducible when using the same seed value and int.seed=Run@seeds (e.g. ) in consequence, init.sample for regular sampling determines an invariant sample distribution, use random sampling or vary init.sample to get varying sample distributions.
    • +
  • Note 8: If multi_test > 1 the user will get multiple maps and will be asked to enter the number of the probability distribution that is appropriate.

Habitat sampling output. R object that contains:

        models          # selected classifiers
diff --git a/R-package/hasa/vignettes/HabitatSampler.md b/R-package/hasa/vignettes/HabitatSampler.md
index 5557f40..2354631 100644
--- a/R-package/hasa/vignettes/HabitatSampler.md
+++ b/R-package/hasa/vignettes/HabitatSampler.md
@@ -19,18 +19,17 @@
       - [4.2 Generating classification map and summary
         statistics](#generating-classification-map-and-summary-statistics)
 
-
 # 1 Introduction
 
 This manual introduces the Habitat Sampler (HaSa), an innovative tool
 that autonomously generates representative reference samples for
-predictive modelling of surface class probabilities. The tool can be
+predictive modeling of surface class probabilities. The tool can be
 applied to any image data that displays surface structures and dynamics
 of any kind at multiple spatial and temporal scales. HaSa was initially
 developed to classify habitat dynamics in semi-natural ecosystems but
 the procedure can theoretically be applied to any surface. The main
 innovation of the tool is that it reduces reliance on comprehensive in
-situ ground truth data or comprehensive training datasets which
+situ ground truth data or comprehensive training data sets which
 constrain accurate image classification particularly in complex scenes.
 
 Though development of HaSa has prioritized ease of use, this
@@ -38,7 +37,7 @@ documentation assume a familiarity with the R software. The document is
 built successively and is intended to lead you step-by-step through the
 HaSa procedure of generating probability and classification maps. HaSa
 is still in development and any suggestions or improvements are welcomed
-and encouraged in our [GitHub Community
+and encouraged in our [GitLab Community
 Version](https://git.gfz-potsdam.de/habitat-sampler/HabitatSampler.git).
 If questions remain please don’t hesitate to contact the authors of the
 package. For a detailed description of the Habitat Sampler and its
@@ -134,7 +133,7 @@ remotes::install_git("https://git.gfz-potsdam.de/habitat-sampler/HabitatSampler.
 Before the user starts using `HaSa` it is necessary to load the library
 and some of its dependencies. In the following example, the libraries to
 be loaded are passed as a list. The option
-`options("rgdal_show_exportToProj4_warnings"="none")` is to supress the
+`options("rgdal_show_exportToProj4_warnings"="none")` is to suppress the
 warning messages related with the latest changes in `gdal` and `PROJ6`.
 
 ``` r
@@ -162,7 +161,7 @@ code sets all the paths assuming that the root path is the current
 directory, i.e., the `demo` directory.
 
 ``` r
-wd <- paste(tools::file_path_as_absolute("./"), "/../demo/", sep = "")
+wd <- paste(tools::file_path_as_absolute("./"), "/../../../demo/", sep = "")
 
 # demo data stored here
 dataPath <- paste(wd, "Data/", sep = "")
@@ -322,10 +321,11 @@ Class sampling input
 HaSa::multi_Class_Sampling(
     in_raster = raster_stk,   # clipped satellite time series stack [raster brick]
     num_samples = 75,         # starting number of spatial samples (recommended value: 75)
+                              # for more info *See note 1
     sample_type = 
         "regular_raster",     # distribution of spatial samples 
                               # ("random_raster","regular_raster", "matrix_random")
-                              # recommended: "regular_raster" *See note 1
+                              # recommended: "regular_raster" *See note 2
     num_models = 200,         # number of models to collect (recommended value: 200)
     num_iterations = 10,      # number of iterations for model accuracy
                               # (recommended value:10)
@@ -337,23 +337,24 @@ HaSa::multi_Class_Sampling(
                               # recommended input: rf)
     num_trees = 500,          # if the model is "rf" set the number of trees for the
                               # random forest algorithm, otherwise, the parameter is
-                              # ignored *See note 2
+                              # ignored *See note 3
     mtry = 10,                # splitting nodes (recommended: mtry < number of predictors)
     mod_error = 0.02,         # threshold for model error until which iteration is being
     last = FALSE,             # only TRUE for one class classifier
-                              # recommended input: FALSE *See note 3
+                              # recommended input: FALSE *See note 4
     last_ref_val = 1000,      # default reference value for the last step (default: 1000)
+                              # for more info *See note 5
     seed = 
-      as.integer(Sys.time()), # set seed for reproducible results *See note 4
+      as.integer(Sys.time()), # set seed for reproducible results *See note 6
     init_seed = "sample",     # "sample" for new or use Run@seeds to reproduce previous
-                              # steps *See note 5
+                              # steps *See note 7
     out_path = out_path,      # output path for saving results
     step = 1,                 # at which step should the procedure start, e.g. use 
                               # step = 2 if the first habitat is already extracted
     class_names = class_names,# vector with class names in the order of reference spectra
     n_classes = 7,            # total number of classes to be separated
     multi_test = 1,           # number of test runs to compare different probability
-                              # output *See note 6
+                              # output *See note 8
     RGB = c(19,20,21),        # pallette colors for the interactive plots
     color = 
         c("lightgrey", "orange", "yellow", "limegreen", "forestgreen")
@@ -369,26 +370,41 @@ HaSa::multi_Class_Sampling(
     )
 ```
 
-  - **Note 1**: There are threes sampling strategies: `random_raster`
+  - **Note 1**: In case it is not possible to find models, increasing
+    the number of `num_samples` and `num_models` is not always the
+    solution. The user should also try to re-sample with a different
+    `seed` value.
+  - **Note 2**: There are threes sampling strategies: `random_raster`
     (it uses `raster::sampleRandom`), `raster_regular` (it uses
     `raster::sampleRegular`), and `random_matrix` (it uses matrices and
-    the `stats::sample` function over only existent non NaN pixels).
-  - **Note 2**: The default value is 500, for small number of trees (at
+    the `stats::sample` function over only existent non `NaN` pixels).
+    The `regular_raster` -\> fast: preferable at the beginning of
+    sampling procedure. The `random_raster` -\> slow: it only samples
+    pixels with information and it is preferable to use at the final
+    steps with few and irregular distributed pixels. The `random_matrix`
+    -\> fast: it only samples pixels with information and it is
+    preferable to use at the final steps with few and irregular
+    distributed pixels.
+  - **Note 3**: The default value is 500, for small number of trees (at
     least 1/3 of the number of predictors) use an odd number for precise
     prediction results.
-  - **Note 3**: The argument `last = T` can be set when only one class
+  - **Note 4**: The argument `last = T` can be set when only one class
     should be separated from the background pixels
-  - **Note 4**: For different results the `seed` should have a different
+  - **Note 5**: The reference data for the pseudo class in the last step
+    is built using the value of `last_ref_val`. In case the user gets NA
+    as last class, the user should adjust the value of `last_ref_val`
+    and re-sample again.
+  - **Note 6**: For different results the `seed` should have a different
     value on each run (use `seed = as.numeric(Sys.time())`). To repeat a
     specific run the user just needs to restart R, run again the
     `HaSa::multi_Class_Sampling` with the same arguments, this is, keep
     `seed` constant, and it will get the same results for all the steps.
-  - **Note 5**: The results from previous steps are reproducible when
+  - **Note 7**: The results from previous steps are reproducible when
     using the same seed value and `int.seed=Run@seeds`
     (e.g. ) in consequence, `init.sample` for regular
     sampling determines an invariant sample distribution, use `random`
     sampling or vary `init.sample` to get varying sample distributions.
-  - **Note 6**: If `multi_test > 1` the user will get multiple maps and
+  - **Note 8**: If `multi_test > 1` the user will get multiple maps and
     will be asked to enter the number of the probability distribution
     that is appropriate.
 
@@ -405,7 +421,7 @@ Habitat sampling output. R object that contains:
         seeds           # seeds to reproduce respective step/habitat type sampling
 ```
 
-### 4.1.2 Interactive probability maps and downloading output
+### 4.1.4 Interactive probability maps and downloading output
 
 An interactive map is plotted in a web browser (e.g., Firefox for Linux)
 containing a selected habitat type. The number of models predicting this
diff --git a/R-package/hasa/vignettes/HabitatSampler.pdf b/R-package/hasa/vignettes/HabitatSampler.pdf
index 2fa089edc1648ecc35d47f7cb660160bcae8d39e..e06dcb76884250044d0ab2ce62f3f3c2806277da 100644
GIT binary patch
delta 124418
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-- 
GitLab


From b397d76381f30b4a1cd6e42a05cb66fa5aa3a9d9 Mon Sep 17 00:00:00 2001
From: Romulo Pereira Goncalves 
Date: Thu, 5 May 2022 10:42:42 +0200
Subject: [PATCH 14/15] Fix link

---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 11b2c67..0ef1a23 100644
--- a/README.rst
+++ b/README.rst
@@ -21,7 +21,7 @@ How to use
 * For Windows operating systems the `Rtools `_ are needed
 
 * library(HaSa) and list datasets: data(package="HaSa") and functions: lsf.str("package:HaSa") or use library(help="HaSa")
-* Information about program execution and function behavior is available in Rmarkdown: `HabitatSampler_Usage `_
+* Information about program execution and function behavior is available in Rmarkdown: `HabitatSampler.Rmd `_
 
 
 2. Stepwise Procedure
-- 
GitLab


From a7eab3baa92f04d3148a9007c20a38d2d64f8aae Mon Sep 17 00:00:00 2001
From: Daniela Rabe 
Date: Fri, 6 May 2022 14:24:30 +0200
Subject: [PATCH 15/15] fix typos

---
 R-package/hasa/vignettes/HabitatSampler.Rmd | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/R-package/hasa/vignettes/HabitatSampler.Rmd b/R-package/hasa/vignettes/HabitatSampler.Rmd
index 1453363..1d69e59 100644
--- a/R-package/hasa/vignettes/HabitatSampler.Rmd
+++ b/R-package/hasa/vignettes/HabitatSampler.Rmd
@@ -32,17 +32,17 @@ knitr::opts_chunk$set(tidy.opts = list(width.cutoff = 75), tidy = TRUE, fig.pos
 
 \newpage
 # 1 Introduction
-This manual introduces the Habitat Sampler (HaSa), an innovative tool that autonomously generates representative reference samples for predictive modeling of surface class probabilities. The tool can be applied to any image data that displays surface structures and dynamics of any kind at multiple spatial and temporal scales. HaSa was initially developed to classify habitat dynamics in semi-natural ecosystems but the procedure can theoretically be applied to any surface. The main innovation of the tool is that it reduces reliance on comprehensive in situ ground truth data or comprehensive training data sets which constrain accurate image classification particularly in complex scenes.
+This manual introduces the Habitat Sampler (HaSa), an innovative tool that autonomously generates representative reference samples for predictive modeling of surface class probabilities. The tool can be applied to any image data that displays surface structures and dynamics of any kind at multiple spatial and temporal scales. HaSa was initially developed to classify habitat dynamics in semi-natural ecosystems, but the procedure can theoretically be applied to any surface. The main innovation of the tool is that it reduces reliance on comprehensive in situ ground truth data or comprehensive training data sets which constrain accurate image classification particularly in complex scenes.
 
-Though development of HaSa has prioritized ease of use, this documentation assume a familiarity with the R software. The document is built successively and is intended to lead you step-by-step through the HaSa procedure of generating probability and classification maps. HaSa is still in development and any suggestions or improvements are welcomed and encouraged in our [GitLab Community Version](https://git.gfz-potsdam.de/habitat-sampler/HabitatSampler.git). If questions remain please don't hesitate to contact the authors of the package. For a detailed description of the Habitat Sampler and its applications, see [Neumann et al., (2020)](https://doi.org/10.1111/ddi.13165).
+Though development of HaSa has prioritized ease of use, this documentation assumes a familiarity with the R software. The document is built successively and is intended to lead you step-by-step through the HaSa procedure of generating probability and classification maps. HaSa is still in development and any suggestions or improvements are welcomed and encouraged in our [GitLab Community Version](https://git.gfz-potsdam.de/habitat-sampler/HabitatSampler.git). If questions remain please don't hesitate to contact the authors of the package. For a detailed description of the Habitat Sampler and its applications, see [Neumann et al., (2020)](https://doi.org/10.1111/ddi.13165).
 
 ## 1.1 Usage
 The tool is implemented in R and uses Leaflet [(Cheng et al., 2019)](https://rdrr.io/cran/leaflet/) to generate interactive maps in a web browser. There are no assumptions about the input image data and there are no constraints for the spectral-temporal-spatial domain in which the image is sampled. The tool requires the input of a priori expert user knowledge to generate reference data about expected surface classes which are delineated in the imagery or extracted from an external spectral library. The user has the choice between image classifiers [random forest](https://doi.org/10.1023/A:1010933404324) (RF) and [support vector](https://doi.org/10.1145/130385.130401) (SV).
 
 ## 1.2 Sample datasets
-The examples in this documentation use an L2A Sentinel-2 timeseries stack from 2018 (6 days, 9 bands each) and reference points that are included in the HaSa package. This Sentinel-2 data are from the Kyritz-Ruppiner Heide a former military training area north east of Berlin, Germany. The open heathlands in the former military training area are designated protected areas under the European Natura 2000 network and are subject to management activities including tree removal, controlled burning and machine mowing. The reference data include 7 classes identified with a priori expert knowledge.
+The examples in this documentation use an L2A Sentinel-2 timeseries stack from 2018 (6 days, 9 bands each) and reference points that are included in the HaSa package. This Sentinel-2 data are from the Kyritz-Ruppiner Heide a former military training area northeast of Berlin, Germany. The open heathlands in the former military training area are designated protected areas under the European Natura 2000 network and are subject to management activities including tree removal, controlled burning and machine mowing. The reference data include 7 classes identified with a priori expert knowledge.
 
-The Sentinel-2 data are downloaded and processed using the German Centre for Geosciences (GFZ) Time Series System for Sentinel-2 (GTS2). Data are made available and atmospherically corrected via a simple web API. Detailed information on the GTS2 system can be found [here](https://www.gfz-potsdam.de/gts2/). The metadata of the Senitnel-2 data including the band ID in the timeseries stack are provided below (Table 1).
+The Sentinel-2 data are downloaded and processed using the German Centre for Geosciences (GFZ) Time Series System for Sentinel-2 (GTS2). Data are made available and atmospherically corrected via a simple web API. Detailed information on the GTS2 system can be found [here](https://www.gfz-potsdam.de/gts2/). The metadata of the Sentinel-2 data including the band ID in the timeseries stack are provided below (Table 1).
 
 ```{r S2 metadata, eval = TRUE, echo=FALSE, message = FALSE, warning = FALSE}
 library(tools)
@@ -111,7 +111,7 @@ raster::rasterOptions(tmpdir = "./RasterTmp/")
 ```
 
 ## 3.2 Satellite timeseries stack
-The satellite time series is either passed as a **3.2.1** stack of images already clipped or **3.2.2** a stack of image to be clipped. In both cases, the input Satellite images needs to either have a valid projection or the projection be passed as parameter, i.e., `sat_crs_str = '+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs'`, otherwise, the function will report error. Satellite time series data are available in `dataPath`.
+The satellite time series is either passed as a **3.2.1** stack of images already clipped or **3.2.2** a stack of image to be clipped. In both cases, the input satellite images needs to either have a valid projection or the projection be passed as parameter, i.e., `sat_crs_str = '+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs'`, otherwise, the function will report error. Satellite time series data are available in `dataPath`.
 
 ```{r handling image data clipped, eval = TRUE, results= "hide", message = FALSE}
 satellite_series_path <- paste(dataPath,"SentinelStack_2018.tif",sep = "")
@@ -256,7 +256,7 @@ HaSa::multi_Class_Sampling(
 ```
 
 * **Note 1**: In case it is not possible to find models, increasing the number of `num_samples` and `num_models` is not always the solution. The user should also try to re-sample with a different `seed` value.
-* **Note 2**: There are threes sampling strategies: `random_raster` (it uses `raster::sampleRandom`), `raster_regular` (it uses `raster::sampleRegular`), and `random_matrix` (it uses matrices and the `stats::sample` function over only existent non `NaN` pixels). The `regular_raster` -> fast: preferable at the beginning of sampling procedure. The `random_raster` -> slow: it only samples pixels with information and it is preferable to use at the final steps with few and irregular distributed pixels. The `random_matrix` -> fast: it only samples pixels with information and it is preferable to use at the final steps with few and irregular distributed pixels.
+* **Note 2**: There are three sampling strategies: `random_raster` (it uses `raster::sampleRandom`), `raster_regular` (it uses `raster::sampleRegular`), and `random_matrix` (it uses matrices and the `stats::sample` function over only existent non `NaN` pixels). The `regular_raster` -> fast: preferable at the beginning of sampling procedure. The `random_raster` -> slow: it only samples pixels with information and it is preferable to use at the final steps with few and irregular distributed pixels. The `random_matrix` -> fast: it only samples pixels with information and it is preferable to use at the final steps with few and irregular distributed pixels.
 * **Note 3**: The default value is 500, for small number of trees (at least 1/3 of the number of predictors) use an odd number for precise prediction results.
 * **Note 4**: The argument `last = T` can be set when only one class should be separated from the background pixels
 * **Note 5**: The reference data for the pseudo class in the last step is built using the value of `last_ref_val`. In case the user gets NA as last class, the user should adjust the value of `last_ref_val` and re-sample again.
-- 
GitLab