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Colorblindness is a genetic condition that affects a person's ability to accurately perceive colors. Several papers still exist making use of rainbow colors palette to show output. In such cases, for colorblind people such graphs are meaningless. In this paper, we propose good practices and coding solutions developed in the R Free and Open Source Software to (i) simulate colorblindness, (ii) develop colorblind friendly color palettes and (iii) provide the tools for converting a noncolorblind friendly graph into a new image with improved colors. [ABSTRACT FROM AUTHOR] more...

Spatial patterns are a crucial aspect of geographical studies and play a significant role in various fields, such as remote sensing, ecology, and geology. Comparing spatial patterns is a common task in these fields, and using raster data is a popular approach. This work aims to provide a comprehensive overview of existing R packages for comparing spatial patterns in rasters. First, we discuss various methods for comparing spatial patterns, including visual inspection, correlation coefficient between focal regions of two rasters, spatial autocorrelation analysis, structural similarity index, and comparison of spatial signatures. These methods can be applied to continuous or categorical raster data, and the choice of method depends on the type of data and the specific research question. Next, we present several R packages that implement these methods, including terra, SSIMmap, and spatialEco, and show how they can be applied. These packages provide a wide range of tools for comparing spatial patterns in rasters, offering researchers a powerful means to study change, similarity, association, and model assessment. However, we also highlight issues with the current software, such as the absence of standardization and inadequate documentation. Importantly, there is still a lack of studies that systematically compare different methods and suggest good practices in their use. The growing number of FOSS tools for spatial raster comparison offers a promising avenue for testing various methods and their application to real-life scenarios. Further research is needed to evaluate the performance of these methods and establish best practices for their use. [ABSTRACT FROM AUTHOR] more...

Thermokarst lakes are the main components of the vast Arctic and subarctic landscapes. These lakes can serve as geoindicators of permafrost degradation; therefore, proper lake distribution assessment methods are necessary. In this study, we compared four machine learning methods to improve existing lake detection systems. The northern part of Yakutia was selected as the study area owing to its complex environment. We used data from Landsat 8 and spectral indices to take into account the spectral characteristics of the lakes, and MERIT DEM data to take into account the topography. The lowest accuracy was found for the classification and regression trees (CART) method (overall accuracy = 81%). On the other hand, the random forests (RF) classification provided the best results (overall accuracy = 92%), and only this classification coped well in all problematic areas, such as shaded and humid areas, near steep slopes, burn scars, and rivers. The altitude and bands SWIR1 (Short wave infrared 1), SWIR2 (Short wave infrared 2), and Green were the most important. Spectral indices did not have significant impact on the classification results in the specific conditions of the thermokarst lakes environment. 17,700 lakes were identified with the total area of 271.43 km2. [ABSTRACT FROM AUTHOR] more...

Ionic strength (I; mol·L−1) acts as one of the most important parameters of natural waters. It is indispensable for obtaining ion activities and thus is crucial for describing chemical processes in water solutions. Limnology, I, has many applications, but calculating the partial pressure of CO2 (pCO2) and the carbonate saturation index (SI) are among the most important examples. The determination of I requires the full ion composition of water to be recognized, and when the concentration of some major ion(s) is/are missing altogether, the I value remains unknown. Because historical and monitoring data are often incomplete, it seems useful to provide a method for the indirect assessment of I. In this paper, we developed and tested an empirical model to estimate I on the basis of electric conductivity at 25 °C (EC). Our model consists of two linear equations: (i) Imod = 15.231 × 10−6·EC − 79.191 × 10−6 and (ii) Imod = 10.647 × 10−6·EC + 26.373 × 10−4 for EC < 592.6 μS·cm−1 and for EC > 592.6 μS·cm−1, respectively. We showed that model performance was better than the hitherto used EC–I relationships. We also demonstrated that the model provided an effective tool for limnological monitoring with special emphasis on the assessment of CO2 emissions from lakes. [ABSTRACT FROM AUTHOR] more...

The purpose of the study was to evaluate the possibility of parameterizing the state of soil surface roughness (SSR) based on proximal measurements of spectral reflectance in the VIS‐NIR range, which is important for the needs of monitoring the state of soil surfaces. SSR should be constantly monitored as it provides an insight into a range of hydrological and erosive soil processes and improves the interpretation of remote sensing data. SSR and the spectral reflectance of three texturally different soils were measured under simulated rainfall in laboratory condition. The relationship between the SSR parameters and soil spectra was determined using regression random forest models. Various spectral data processing methods were tested and the best wavelengths for SSR description after rainfall were found. Two roughness indices were used to describe the SSR: Height Standard Deviation (HSD) and T3D (Tortuosity index). Although both shared a significant correlation with SSR, the T3D index demonstrated a more pronounced rainfall effect and a closer correlation with spectral data than HSD. The best determination of T3D was obtained with the raw spectra (RAW) (R2 = 0.71), as well as with spectra transformed with the baseline alignment first derivative (BA1d) method (R2 = 0.71) or the Savitzky‐Golay (SG) method (R2 = 0.69). Different wavelengths were the best SSR predictors depending on the spectral transformation method (VIPs ‐ Variable Importance in Projection). For both roughness indices, the NIR wavelengths (725–820 nm) yielded the highest VIP Score in models based on RAW spectra, while those in the VIS region (450–772 nm) were most important in models based on transformed spectra. Plain Language Summary: The intensity of agricultural land use is increasing globally, contributing to soil degradation. Such degradation requires constant monitoring using various soil physical characteristics, including soil surface roughness (SSR). The SSR changes over time under the influence of natural and anthropogenic factors, such as rain and farming practices in agricultural fields. SSR can be quantified using remote sensing methods based on the interpretation of airborne and satellite images. However, to do so, it is necessary to understand the relationship between the SSR and the amount of light reflected from the soil surface, first in controlled laboratory condition using hyperspectral data and statistical models. Our study presents results of the reflectance measurements from soil surfaces, which were gradually smoothed by simulated rainfall in the laboratory condition. Based on these measurements, and machine learning methods, models were developed that can predict the SSR from the VNIR‐SWIR spectra with a reliability of up to 71%. Key Points: The impact of simulated rainfall on the soil surface roughness and their spectral characteristics was investigatedThe changes in the soil surface roughness were closely related to the changes in spectral reflectance from the soilsThe spectral data was more closely related to the T3D index than the height standard deviation index [ABSTRACT FROM AUTHOR] more...

The quantitative description of the soil surface roughness is necessary for effective monitoring of wind, water and tillage erosion, hydrological processes or greenhouse gas emissions. The aim of this work was to build soil roughness predictive models based on the type of tillage tool, the roughness indices and soil properties. The roughness formed by five tillage tools was determined. Two surface roughness indices: Height Standard Deviation (HSD) and T3D (Tortuosity index) were calculated from Digital Elevation Model. The both roughness indices demonstrated a significant correlation, however, they provided different information about soil roughness. The HSD describes roughness on the "macro," while T3D refers to the "micro" scale. Hence, our findings show that a single index is not sufficient to describe the roughness of post‐treatment surface. The linear and random forest models were built to describe the relationships between the roughness indices, type of tillage tool and soil properties. The HSD analysis indicated that the type of tillage tools had the greatest impact on post‐treatment roughness. In contrast, T3D analysis found soil texture to have a significant effect, together with tillage tools. In all modeling scenarios, T3D was more accurately predicted than HSD by both the linear (R2 = 0.62 vs. R2 = 0.60) and random forest models (R2 = 0.58 vs. R2 = 0.55). Predictive soil surface roughness models can be applied effectively for estimating water retention in soil, the intensity and speed of surface water flow, soil erosion, the level of reflected shortwave solar radiation or soil properties by remote sensing techniques. Plain Language Summary: The acreage of arable soils decreases along with the intensification of agricultural production. Intensive use of farmlands can lead to soil degradation, manifested by increased susceptibility to erosion, loss of organic matter, decline in fertility and structural soil condition and reduced crop yields. Thus, soil degradation is a very important in views of world food security and environmental quality. In order to effectively limit the deterioration of soil quality as a result of agricultural activity, it is necessary to develop methods of soil condition monitoring that would enable the assessment of the risk of erosion. One of the important physical soil characteristics that can be used to assess soil condition is its surface roughness. In this study the relationships between the roughness indices, type of tillage treatment and soil properties were described using the linear and random forest models. Information about the tillage tool type is needed to predict soil surface roughness however, when soil properties are included in the modeling, the reliability of the prediction increased significantly. Predictive soil surface roughness models can be applied effectively for a range of tasks, such as estimating water retention in soil and the intensity and speed of surface water flow. Key Points: Soil surface roughness formed by five selected tillage tools was analyzedRoughness in terms of Height Standard Deviation was mainly formed by the tillage tool when in terms of T3D was influenced by both tillage tools and soil texturePredictive models for soil surface roughness were created [ABSTRACT FROM AUTHOR] more...

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...

Categorical maps of landscape types (LTs) are useful abstractions that simplify spatial and thematic complexity of natural landscapes, thus facilitating land resources management. A local landscape arises from a fusion of patterns of natural themes (such as land cover, landforms, etc.), which makes an unsupervised identification and mapping of LTs difficult. This paper introduces the integrated co-occurrence matrix (INCOMA) – a signature for numerical representation of multi-thematic categorical patterns. INCOMA enables an unsupervised identification and mapping of LTs. The region is tessellated into a large number of local landscapes – patterns of themes over small square-shaped neighborhoods. With local landscapes represented by INCOMA signatures and with dissimilarities between local landscapes calculated using the Jensen-Shannon Divergence ( J S D), LTs can be identified and mapped using standard clustering or segmentation techniques. Resultant LTs are typically heterogeneous with respect to categories of contributing themes reflecting the human perception of a landscape. LTs calculated by INCOMA are more faithful abstractions of actual landscapes than LTs obtained by the current method of choice – the map overlay. The concept of INCOMA is described, and its application is demonstrated by an unsupervised mapping of LT zones in Europe based on combined patterns of land cover and landforms. [ABSTRACT FROM AUTHOR] more...

Context: Pattern-based spatial analysis provides methods to describe and quantitatively compare spatial patterns for categorical raster datasets. It allows for spatial search, change detection, and clustering of areas with similar patterns. Objectives: We developed an R package motif as a set of open-source tools for pattern-based spatial analysis. Methods: This package provides most of the functionality of existing software (except spatial segmentation), but also extends the existing ideas through support for multi-layer raster datasets. It accepts larger-than-RAM datasets and works across all of the major operating systems. Results: In this study, we describe the software design of the tool, its capabilities, and present four case studies. They include calculation of spatial signatures based on land cover data for regular and irregular areas, search for regions with similar patterns of geomorphons, detection of changes in land cover patterns, and clustering of areas with similar spatial patterns of land cover and landforms. Conclusions: The methods implemented in motif should be useful in a wide range of applications, including land management, sustainable development, environmental protection, forest cover change and urban growth monitoring, and agriculture expansion studies. The motif package homepage is https://nowosad.github.io/motif. [ABSTRACT FROM AUTHOR] more...

Quantifying landscape characteristics and linking them to ecological processes is one of the central goals of landscape ecology. Landscape metrics are a widely used tool for the analysis of patch‐based, discrete land‐cover classes. Existing software to calculate landscape metrics has several constraints, such as being limited to a single platform, not being open‐source or involving a complicated integration into large workflows. We present landscapemetrics, an open‐source R package that overcomes many constraints of existing landscape metric software. The package includes an extensive collection of commonly used landscape metrics in a tidy workflow. To facilitate the integration into large workflows, landscapemetrics is based on a well‐established spatial framework in R. This allows pre‐processing of land‐cover maps or further statistical analysis without importing and exporting the data from and to different software environments. Additionally, the package provides many utility functions to visualize, extract, and sample landscape metrics. Lastly, we provide building‐blocks to motivate the development and integration of new metrics in the future. We demonstrate the usage and advantages of landscapemetrics by analysing the influence of different sampling schemes on the estimation of landscape metrics. In so doing, we demonstrate the many advantages of the package, especially its easy integration into large workflows. These new developments should help with the integration of landscape analysis in ecological research, given that ecologists are increasingly using R for the statistical analysis, modelling and visualization of spatial data. [ABSTRACT FROM AUTHOR] more...

The basic aim of this study was to find relations between the dates of occurrence and characteristics of extensive extreme daily (24-h) precipitation totals (EEDPTs) and pressure systems. The analysis was conducted on the basis of precipitation data from the multi-year period 1956-1980 and the Grosswetterlagen classification of circulation situations. EEDPTs were taken to embrace those cases of maximum annual daily precipitation totals that were registered on the same day at a minimum of 75 precipitation stations. In the years 1956-1980 there were 209 such events. The hypothesis about the effect of a circulation situation on the probability of occurrence of an EEDPT was verified in quantitative terms, the reference being both the entire multi-year period and the seasonal variation in the occurrence of precipitation of this type. Next, circulation situations were compared in terms of amount-related parameters of EEDPTs (mean precipitation, coefficient of variation), their spatial features (perimeter, area), and precipitation volume. The analyses performed show a statistically significant dependence between the atmospheric circulation and extensive extreme precipitation. It was demonstrated that there were circulation situations during which EEDPTs occurred much more often or much more rarely than over the entire multi-year period under study. Also identified was the connection of an atmospheric circulation with the mean amount, coefficient of variation and volume of extensive extreme precipitation. [ABSTRACT FROM AUTHOR] more...

Entropy is a fundamental concept in thermodynamics that is important in many fields, including image processing, neurobiology, urban planning, and sustainability. As of recently, the application of Boltzmann entropy for landscape patterns was mostly limited to the conceptual discussion. However, in the last several years, a number of methods for calculating Boltzmann entropy for landscape mosaics and gradients were proposed. We developed an R package belg as an open source tool for calculating Boltzmann entropy of landscape gradients. The package contains functions to calculate relative and absolute Boltzmann entropy using the hierarchy-based and the aggregation-based methods. It also supports input raster with missing (NA) values, allowing for calculations on real data. In this study, we explain ideas behind implemented methods, describe the core functionality of the software, and present three examples of its use. The examples show the basic functions in this package, how to adjust Boltzmann entropy values for data with missing values, and how to use the belg package in larger workflows. We expect that the belg package will be a useful tool in the discussion of using entropy for a description of landscape patterns and facilitate a thermodynamic understanding of landscape dynamics. [ABSTRACT FROM AUTHOR] more...

Freely available and reliable meteorological datasets are highly demanded in many scientific and business applications. However, the structure of publicly available databases is often difficult to follow, especially for users who only deal with this kind of dataset on occasion. The "climate" R package aims to fill this gap with an easy-to-use interface for downloading global meteorological data in a fast and consistent way. The package provides access to different sources of in-situ meteorological data, including the Ogimet website, atmospheric vertical sounding gathered at the University of Wyoming's webpage, and hydrological and meteorological measurements collected by the Institute of Meteorology and Water Management—National Research Institute (i.e., Polish Met Office). This article also provides a quick overview of the key functionalities available within the climate R package, and gives examples of an efficient and tidy workflow of meteorological data within the R based environment. The automation procedures included in the packages allow one to download data in a user-defined time resolution (from hourly to annual), for a user-defined time span, and for a specified group of stations or countries. The package also contains metadata, including a list of available stations, their geospatial information, and measurement descriptions with their units. Finally, the obtained datasets can be processed in R or exported to external tools (e.g., spreadsheets or GIS software). [ABSTRACT FROM AUTHOR] more...