Class Map Research Articles

Шкала природной пожарной опасности лесных экосистем И.С. Мелехова. Обзор современных российских методических подходов

The article is devoted to the review of various methodological approaches to the estimation of natural fire danger (NFD), as well as to the creation, updation and application of the NFD maps, which are offered by modern Russian scholars. The scale of natural fire danger assessment recommended for use and developed by I. S. Melekhov is presented and analyzed. The methodological drawbacks of this scale as stated by modern researchers are indicated. The paper reviews the development of a new methodological approach to compilation of regional scales for assessing the natural fire danger of forests, by taking into account the links between forest growth conditions as well as seasonal and climatic conditions in the regions of the Russian Federation. The method for mapping of natural fire danger on the basis of maps of plant combustible materials, proposed by the scientific group of the V. N. Sukachev Institute of Forest SB RAS, is studied. We consider studies of the Mytischi Branch of Bauman Moscow State Technical University related to investigation if the possibility for applying mathematical modeling methods for long-term forecasting of changes in NFD under different scenarios of forest management. The method for annual mapping of NFD classes, proposed in the CEPF RAS, is presented. An example of the use of NFD maps in assessing the probability of forest fires in the ICARP FEB RAS is considered. Future research areas are identified, namely, a cartographic representation of the created regional scales of NFD and the results of mathematical modeling of long-term changes in NFD.

Universal singular exponents in catalytic variable equations

Catalytic equations appear in several combinatorial applications, most notably in the enumeration of lattice paths and in the enumeration of planar maps. The main purpose of this paper is to show that the asymptotic estimate for the coefficients of the solutions of (so-called) positive catalytic equations has a universal asymptotic behavior. In particular, this provides a rationale why the number of maps of size n in various planar map classes grows asymptotically like c⋅n−5/2γn, for suitable positive constants c and γ. Essentially we have to distinguish between linear catalytic equations (where the subexponential growth is n−3/2) and non-linear catalytic equations (where we have n−5/2 as in planar maps). Furthermore we provide a quite general central limit theorem for parameters that can be encoded by catalytic functional equations, even when they are not positive.

The real cycle class map

The classical cycle class map for a smooth complex variety sends cycles in the Chow ring to cycles in the singular cohomology ring. We study two cycle class maps for smooth real varieties: the map from the I-cohomology ring to singular cohomology induced by the signature, and a new cycle class map defined on the Chow-Witt ring. For both maps, we establish basic compatibility results like compatibility with pullbacks, pushforwards and cup products. As a first application of these general results, we show that both cycle class maps are isomorphisms for cellular varieties.

Digital mapping of soil classes using spatial extrapolation with imbalanced data

Digital mapping of soil classes using the extrapolation approach is timesaving, economically cheap, and helps collecting soil data from areas with difficult sampling. However, it has not been explored widely enough for digital mapping of soil classes. This study seeks to evaluate and compare several machine learning and regression algorithms for the extrapolation of soil sub-groups. Also the issue of imbalanced number of observations was addressed and oversampling technique was applied on the minority soil class to improve the models performance. The study area is located in central north Iran with 84 and 72 soil profiles sampled in the donor and recipient areas, respectively. A set of various environmental covariates including remotely sensed data, digital elevation model derivatives and geomorphology map were used as explanatory variables for predicting soil classes. Results showed that among eleven investigated models, C5.0 decision tree (DT), random forest (RF) and multinomial logistic regression (MNL) had the highest overall accuracy of 46%, 42% and 38%, respectively, for the extrapolation of soil classes. Also the Kappa statistic values for these models were 0.30, 0.24 and 0.22, respectively. Oversampling of the minority soil class led to an increase in overall accuracy for some of the models with the highest ones being DT = 53% and RF = 50%. Also, the Kappa value for DT and RF models increased to 0.39 and 0.35, respectively. In addition, oversampling of the minority soil class led to the prevention of losing this class in the final map.

Importance of individual sample of training data in modified possibilistic c-means classifier for handling heterogeneity within a specific crop

In remote sensing images, pixel-based classifiers use means or variance-covariance statistical parameters generated from training sample data sets. These parameters do not represent in totality about variations within the class. This research work enlightens each training sample’s role in handling heterogeneity within the class instead of using statistical parameters (mean). Modified possibilistic c-means fuzzy algorithm, capable of single class mapping, has been experimented to handle heterogeneity within the class. The mapping of mustard, wheat, and grass classes has been conducted using multispectral temporal images of Sentinel-2A/2B of Banasthali, Rajasthan region. It has been observed that while using individual samples in place of statistical parameters in fuzzy-based classifiers, the individual class identified has been least affected due to heterogeneity within the class. Mean membership difference for favorable and non-favorable classes as well as F-score, kappa, and overall accuracy have been calculated. Through all these parameters, individual samples as mean outperformed other training approaches.

Geographic object-based image analysis and artificial neural networks for digital soil mapping

The use of techniques to calibrate soil prediction models based on legacy maps is restricted to areas where conventional survey and soil mapping were performed. It is necessary to seek alternatives to calibrate soil predictive models without a legacy soil map. The problem is the difficulty to delineate polygons of soil classes based on soil sampling points. This paper presents a novel digital soil mapping strategy using only georeferenced points of soil profiles to delineate detailed polygons of soil classes by the Geographic Object-Based Image Analysis (GEOBIA) approach and Artificial Neural Networks (ANN) models. The main objective was to evaluate the integration of the GEOBIA approach at different segmentation levels with ANN’s models and sampling points to produce a digital soil map of the Vale dos Vinhedos region, in Rio Grande do Sul, Brazil. From a Digital Terrain Model, 10 predictive variables were extracted. From the RapidEye remote sensing image the following spectral variables were extracted: Normalized Difference Vegetation Index and Normalized Difference Water Index. A new set of variables was produced using principal component analysis after standardization of the original variables. The multiresolution segmentation algorithm was used to create image objects at different segmentation levels. Four segmentation levels were tested with scale parameter (SP) 1, 2, 5 and 10; and the Cheesboard segmentation (CS) was used to transform the original pixels into polygons. Information on types of soil was obtained from 163 georeferenced soil profiles. Five ANN’s structures were implemented: four for the segmented data (SP01, SP02, SP05 and SP10) and one for the per-pixel approach (CS); 16 repetitions of each ANN were performed. With the best models for each segmentation level, extrapolations of soil classes were performed for the entire study area and validated with a detailed legacy soil map. The per-pixel approach model obtained the worst result. The best result in the extrapolation of soil types found was with the object-based approach (SP1). In our understanding, the scientific community can profit from the proposed methodology particularly when it only has georeferenced points of soil classes and not a legacy map, i.e., immediately after a field survey without the need for photointerpretation for delineation of the soil mapping units. The use of GEOBIA for digital mapping of soil classes seems to be a promising approach for future research.

Spatio - Temporal Analysis of Land Use / Land Cover Changes in Cheha District, South - Central Ethiopia

Land use and land cover (LULC) dynamics pose negative impacts to land resources. Evaluation of the rate and extent of change is, therefore, indispensable for managing the environment and its implications on sustainable land resource management of the study area. The study was aimed to analyze the spatial and temporal dynamics of LULC of Cheha district during 1973 to 2017 periods and assess the implications in LULC changes. All acquired imageries of the study area were geo-referenced and re-projected. A supervised classification with maximum likelihood technique was employed to extract multispectral satellite data from Landsat using GIS and ERDAS imagine software. The Landsat imageries were monitored for (1973, 1987, 2000 and 2017) period. Seven thematic maps of land classes were identified and generated using remote sensing techniques and field verification. The study results showed the areal extent of shrub, bare and forest lands decreased by (29.6%), (19.4%) and (7.5%), respectively during 1973 to 1987 interval. However, built-up area was increased considerably by (57.3%) and agriculture land (18.9%) in the same span. The area coverage of built-up, agriculture and forest lands, during the entire study period (1973 - 2017) increased by (67.1%), (31.6%) and (59.7%), respectively. Conversely, the respective yearly change of grazing, shrub and bare land classes over the study period was (-194.4 ha), (-114.7 ha) and (-60.3 ha). The LULC dynamics pretended a serious threat to land resources in the study area over the past 44 years. Therefore, better understanding of its underlying causes and associated driving forces could support in developing sustainable land resource management in the area. Keywords: land use/land cover change, Image classification, GIS, remote sensing DOI: 10.7176/JEES/11-7-01 Publication date: July 31 st 2021

Erosion prediction of several land utilization in Dadakitan Village, Baolan sub-district, Toli-Toli Regency

This study aims to determine the amount of soil erosion and the danger index of erosion in several lands uses in Dadakitan Village, Baolan Sub-District, Toli-Toli Regency. In this study, slope class maps and land use maps were overlaid, in order to obtain six land uses include the use of paddy fields, coconut plantation, cocoa plantation, cloves plantation, and forests. Erosion prediction was carried out for all land uses. The survey was then carried out on six land uses to observe the slope and the dominant vegetation. Soil samples were taken deliberately for each land use; three samples for each sample of intact soil and disturbed soil. Erosion prediction analysis was using the USLE (Universal Soil Loss Equation). The results showed that the erosion hazard index occurred in Dadakitan Village was classified as low, high, and very high. Low erosion occurred in paddy fields, coconut plantation, and cocoa plantation. In contrast, high levels of erosion occurred on clove plantation, and very high erosion level occurred on forest lands. The dominant factors affecting the occurrence of erosion in this area were soil erodibility, topography, plant management, and conservation measures.

Geostatistics as a tool to reduce the sampling effort in forest inventories

Geostatistics is one of the tools applied to investigate the spatial variability of forests to reduce costs and recognize the best productivity areas for planning. This study aimed to test the performance of geostatistical techniques in reducing the sampling effort in forest inventories. For this purpose, we used the height of dominant trees as a discriminator of the homogeneous strata to obtain a better representation of the productivity within the forest stands. We carried out the study in Pinus taeda L. stands in the Center-South of Paraná, Brazil, by using plots from a forest inventory allocated with the systematic process. Then, we tested three models to determine the site curves (Schumacher, Chapman-Richards 2, and 3 coefficients) with the thirty-seventh year being the reference age. To model the spatial patterns of the dominant height, we used the ordinary kriging, and, after that, we generated the thematic maps of the site classes. Similarly, we used the indicator kriging which allowed obtaining the probabilities of high, medium, and low productivity sites. The processing of the stratified sampling, with the support of the visual interpretation of the images, allowed us to define five strata according to productivity. Results showed that ordinary kriging is effective in defining the productivity classes. Along with geostatistical techniques, it produces more homogeneous strata and reduces the errors of the forest inventory. Moreover, the best-selected model was the Chapman-Richards (3 coefficients) for the site curves. The exponential model was the best model to identify the best areas of the probability of occurrence of sites with higher productivity. The efficiency of indicative kriging generated thematic maps to delimit the likely locations of the most promising sites. Overall, geostatistics proved to be efficient concerning error when compared to simple random sampling.

Geological and terrain attributes for predicting soil classes using pixel- and geographic object-based image analysis in the Brazilian Cerrado

One of the great challenges in the geosciences is mapping targets and phenomena that are not fully in direct range of imaging instruments such as remote sensors. Thus, soil maps are still scarce, especially at the detail level for large regions, with the few official maps being restricted to small spatial cutouts, resulting in an increasing demand for pedological information. The aim of this work was to analyze the use of covariates related to geological and terrain attributes on the performance of soil class predictive models for digital soil mapping with pixel- and geographic object-based image analysis (GEOBIA) approaches in the context of sub-humid tropical environments of the Cerrado biome, in central-western Brazil, to reproduce a conventional map. A reference soil map at the scale 1:50,000 with twelve soil classes was used. Randomly stratified samples and the Random Forest (RF) classifier was performed with seventeen digital elevation model derivatives and the geology categorical data (lithostratigraphic units) as covariates for the prediction of soil classes in both approaches. The main results demonstrated that GEOBIA is a promising method for mapping soil classes, with overall accuracy of 0.70, which is 5% higher than pixel-based. The most important covariates for soil class mapping were related to Geology and terrain attributes: Altitude, Vertical Distance to Channel Network, Vector Terrain Ruggedness, Relative Slope Position, Topographic Height, Profile Curvature, Roughness, Slope, and Valley Depth. The integration of the geological covariate improved the models in both approaches. Integrated evaluation of the geological units and terrain attributes along with the use of machine learning techniques, such as the RF algorithm, is a promising and relevant strategy for digital predictive mapping of soil classes that can contribute toward the greater reproducibility of conventional soil maps.

Class Maps for Visualizing Classification Results

Classification is a major tool of statistics and machine learning. A classification method first processes a training set of objects with given classes (labels), with the goal of afterward assigning new objects to one of these classes. When running the resulting prediction method on the training data or on test data, it can happen that an object is predicted to lie in a class that differs from its given label. This is sometimes called label bias, and raises the question whether the object was mislabeled. The proposed class map reflects the probability that an object belongs to an alternative class, how far it is from the other objects in its given class, and whether some objects lie far from all classes. The goal is to visualize aspects of the classification results to obtain insight in the data. The display is constructed for discriminant analysis, the k-nearest neighbor classifier, support vector machines, logistic regression, and coupling pairwise classifications. It is illustrated on several benchmark datasets, including some about images and texts.

MIND MAPPING MATA PELAJARAN MATEMATIKA UNTUK KELAS IX SEMESTER GANJIL

The study s a qualitative descriptive approach, and his research type is the study of library studies. Researchers aim to project sub-sub materials through the mathematical mind mapping of major semester ix classes, researchers are trying to collect data by reading, studying understanding and analyzing books as well as research journals, accessing Internet sites, or documents related to mind mapping. Mind mapping is the most effective and efficient way to input, store and extract data from the brain or into the brain. It works according to the natural workings of our brain, thus optimizing all the human brain's potential and capacity. The manufacturing of the mind mapping process requires that a number of basic characteristics are attained. There is also the basic characteristic; Paper, colour, line, letter, keyword, key image, and structure. Thus basically the mind mapping process is based on the development of the mind mapping process, resulting in a mind mapping that can help teachers and students in the mathematical process during the difficult part of the semester, and can make learning more meaningful and enjoyable because the learning material is visible to the whole so that students can readily understand the material given.

Comparison of Methodological Approaches for the Evaluation of Ecosystem Services for Options to Develop Sustainable Tourism Forms (Ecotourism) (Example: Bratislava IV)

Abstract Ecosystem services are the benefits provided to human society by natural ecosystems, which are generally understood as the ecosystem processes that sustain human life. In particular, an ecosystem’s cultural services include intangible benefits derived from aesthetic and other experiences, recreation, cognition and spiritual enrichment, and the ability to discern values. The study focuses on the comparison of two methodological approaches to the evaluation of landscape and the benefits of ecosystems for the development of ecological forms of tourism, using Bratislava IV as an example. The basis for database information is the processing of secondary landscape structure. The first methodological approach is based on a detailed mapping of land cover classes (Land Cover) in accordance with the legend of the Technical Guide CORINE Land Cover – supplement at the fourth and fifth levels of mapping in 2014‒2018 and determining the degree of landscape-ecological significance. The second approach is based on creating a set of relevant indicators and their evaluation using the scaling and weighting method. We reached the conclusion that the first methodological approach is more suitable for smaller territories at the cadastral level, while the second is better suited for regional-level territories.

Predicting the spatial distribution of soil mineral particles using OLI sensor in northwest of Iran.

Texture is one of the most important soil properties that knowledge of the spatial distribution is essential for land-use planning and other activities related to agriculture and environment protection. So, this study was performed to supply the soil texture spatial distribution using standardized spectral reflectance (ZPC1) index of Landsat 8 satellite images in the northwest of Iran. The soil sampling was performed using a random method in 145 points. Mineral soil particles including clay, silt, and sand were determined, and soil texture was calculated. In this study, Landsat 8 satellite images were used to interpolate the soil texture spatial distribution. In the first step, the principal component analysis (PCA) was obtained. Then, PCA1 was standardized using a z-score (ZPC1), and regression techniques were used to create proper relationships between ZPC1 and the primary soil particles. Then, spatial distribution of soil particles was used to create a spatially distributed map of the soil textural classes. The results showed that the standardization of the first component reduced the standard deviation of PCA1 from 23.6 to 10.8. The results of comparing ZPC1 with soil mineral components showed that with increasing the amounts of soil clay and sand, the ZPC1 value decreases and increases, respectively. The results showed that the ranges of the spatial distribution of clay and sand were similar to the laboratory-measured amounts. The results of texture class prediction using the soil texture triangle showed that the amount of similarity between the measured and predicted classes was 53.79%.

Digital soil maps can perform as well as large-scale conventional soil maps for the prediction of catchment baseflows

The rapid advance of digital soil mapping (DSM) has resulted in the generation of fine resolution soils spatial datasets with associated uncertainty that can be as accurate but more cost-effective and faster to produce than conventional soil maps. There is documented and increasing interest by policy makers and end-users in moving from conventional soil mapping to DSM; however, the wider operational use of DSM depends on demonstrating the effectiveness of DSM in fulfilling user needs and requirements along with providing DSM products that can be easily used by non-specialists. In this study we used the Hydrology of Soil Types (HOST) classification scheme, which was devised to predict flows in ungauged catchments and is used by both the research and policy maker communities in the UK, as the exemplar for comparing the efficiency of HOST class maps produced using both DSM and conventional soil map approaches for predicting catchment hydrological response. The performance for hydrological predictions of a detailed (1:25,000 scale) polygon HOST class map and a HOST-DSM class map produced via spatial disaggregation was assessed by comparing Base Flow Index (BFI) calculated using HOST class proportions from both maps with BFI calculated using flow data from gauges in 39 selected catchments. Results showed that the disaggregated HOST-DSM class map gave similar or even better BFI predictions than the conventional polygon-based HOST class map, while also providing a better insight on spatial soil variability within map units and its effect on hydrological predictions. This study demonstrates the potential of DSM to produce soil hydrological maps that provide comparable or better baseflow predictions than maps produced using detailed and intensive soil surveying. The study results also suggest that translating DSM of classes to more easily interpretable mapping datasets and incorporating prediction uncertainty in the final DSM product, as in the case of BFI maps based on HOST-DSM classes, could help with facilitating the transition towards a wider operational use of DSM.

Assessment of land-use and land-cover changes in Pangari watershed area (MS), India, based on the remote sensing and GIS techniques

In this paper, we focus on the assessment of land-use and land-cover change detection mapping to the effective planning and management policies of environment, land-use policy and hydrological system in the study area. In this study the soil and water conservation project has been applied during the five years and after five years what changes have been found in the land-use and land-cover classes and vegetation. In this view, this land-use and land-cover mapping is a more important role to decide the policy for watershed planning and management project in the semiarid region. In an emerging countries, fast industrialization and urbanization impose a significant threat to the natural atmosphere. The remote sensing and GIS techniques are crucial roles in the study of land-use and land-cover mapping during the years of 2007, 2014, and 2017. The main objective of this is to prepare the land-use and NDVI maps in the years of 2008, 2014 and 2017; these maps have prepared from satellite data using the supervised classification method. A normalized difference vegetation index map (NDVI) was done by using Landsat 8 and LISS-III satellite data. NDVI values play a major role in monitoring the vegetation and variation in land-use and land-cover classes. In these maps, four types of land are divided into four classes as agriculture, built-up, wasteland, and water body. The results of study show that agriculture land of 18.71% (158.24 Ha), built-up land of 0.62% (5.31 Ha), wasteland of 40.33% (341.02 Ha), and water body land of 17.39% (147 Ha) are increased. Land-use and land-cover maps and NDVI values show that agriculture land of 22.97% (194.29 Ha), 5.46% (14.59 Ha), and 0.08% (0.22 Ha) decreases during the years of 2008, 2014, and 2017. The results directly indicate that the supervised classification method has been the accurate identified feature in the land-use map classes. This classification method has been given the better accuracy (95%) from spatiotemporal satellite data. The accuracy was also tally with ground-truth and Google earth information. These results can be a very useful for the land-use policy, watershed planning, and management with natural resources, animals, and ecological systems.

Quality control and class noise reduction of satellite image time series

The extensive amount of Earth observation satellite images available brings opportunities and challenges for land mapping in global and regional scales. These large datasets have motivated the use of satellite image time series analysis coupled with machine learning techniques to produce land use and cover class maps. To be successful, these methods need good quality training samples, which are the most important factor for determining the accuracy of the results. For this reason, training samples need methods for quality control of class noise. In this paper, we propose a method to assess and improve the quality of satellite image time series training data. The method uses self-organizing maps (SOM) to produce clusters of time series and Bayesian inference to assess intra-cluster and inter-cluster similarity. Consistent samples of a class will be part of a neighborhood of clusters in the SOM map. Noisy samples will appear as outliers in the SOM. Using Bayesian inference in the SOM neighborhoods, we can infer which samples are noisy. To illustrate the methods, we present a case study in a large training set of land use and cover classes in the Cerrado biome, Brazil. The results prove that the method is efficient to reduce class noise and to assess the spatio-temporal variation of satellite image time series training samples.

4D-STEM of Beam-Sensitive Materials

ConspectusScanning electron nanobeam diffraction, or 4D-STEM (four-dimensional scanning transmission electron microscopy), is a flexible and powerful approach to elucidate structure from "soft" materials that are challenging to image in the transmission electron microscope because their structure is easily damaged by the electron beam. In a 4D-STEM experiment, a converged electron beam is scanned across the sample, and a pixelated camera records a diffraction pattern at each scan position. This four-dimensional data set can be mined for various analyses, producing maps of local crystal orientation, structural distortions, crystallinity, or different structural classes. Holding the sample at cryogenic temperatures minimizes diffusion of radicals and the resulting damage and disorder caused by the electron beam. The total fluence of incident electrons can easily be controlled during 4D-STEM experiments by careful use of the beam blanker, steering of the localized electron dose, and by minimizing the fluence in the convergent beam thus minimizing beam damage. This technique can be applied to both organic and inorganic materials that are known to be beam-sensitive; they can be highly crystalline, semicrystalline, mixed phase, or amorphous.One common example is the case for many organic materials that have a π-π stacking of polymer chains or rings on the order of 3.4-4.2 Å separation. If these chains or rings are aligned in some regions, they will produce distinct diffraction spots (as would other crystalline spacings in this range), though they may be weak or diffuse for disordered or weakly scattering materials. We can reconstruct the orientation of the π-π stacking, the degree of π-π stacking in the sample, and the domain size of the aligned regions. This Account summarizes illumination conditions and experimental parameters for 4D-STEM experiments with the goal of producing images of structural features for materials that are beam-sensitive. We will discuss experimental parameters including sample cooling, probe size and shape, fluence, and cameras. 4D-STEM has been applied to a variety of materials, not only as an advanced technique for model systems, but as a technique for the beginning microscopist to answer materials science questions. It is noteworthy that the experimental data acquisition does not require an aberration-corrected TEM but can be produced on a variety of instruments with the right attention to experimental parameters.

Remote sensing‐based biological and nonbiological indices for evaluating desertification in Iran: Image versus field indices

AbstractMapping and monitoring of the complex process of desertification based on ground data in broad arid and semiarid areas faces basic limitations. Therefore, the purpose of the present study was to propose a new method for mapping this phenomenon in central Iran using biological and nonbiological (BNB) indices of remote sensing products from 2003 to 2016. For this purpose, BNB indices including normalized difference vegetation index, land surface temperature, temperature vegetation dryness index, precipitation, evapotranspiration, net primary production, rain use efficiency (RUE), aridity index, and slope were extracted using MOD13A2, MOD11A2, PERSIANN‐CDR, and SRTM products. After calibration and normalization of indices, they were combined using fuzzy logic and gamma operator and the combined 2003 map was validated by MEDALUS model map prepared based on ground data in 2003 using Pearson correlation and error matrix. Results showed more than 70% correlation (p < .001) as well as overall accuracy and kappa coefficient of more than 70% and 0.5 between remote sensing‐based and MEDALUS‐based desertification maps. According to the 2003 and 2016 maps, desertification classes including low, moderate, severe, and very severe changed from 11.9, 49.8, 34, and 4.1% to 11.11, 43.21, 40.43, and 5.24%, respectively, which indicate increasing trend of desertification in the region. The findings demonstrate the high capability of proposed method to map and monitor desertification classes. Therefore, it can be used to update existing desertification models and to report desertification condition and its positive and negative trends at local, national, and international levels.

Assessing map accuracy from a suite of site-specific, non-site specific, and spatial distribution approaches.

Assessing a map's accuracy and efficacy requires careful consideration and calculation of a comprehensive set of measures, particularly when characteristics in geospatial datasets may be used for addressing multiple objectives. We describe a map accuracy assessment framework for categorical geospatial data over large areas that utilizes site-specific (plot-pixel), non-site specific (area estimates), and comparative accuracy (spatial and statistical distributions) methods. This comprehensive framework spans the range of approaches applied in most other studies, maximizing opportunities for both within- and between-study comparisons. The analysis framework incorporated recommended good practices for sampling design, response design, analysis protocols, and design-based inference for providing rigorous estimates of accuracy and precision. Further partitioning of disagreement allows users to assess the relative importance of spatial location versus total abundance of map classes to map error; we introduced a ratio of allocation disagreement to quantity disagreement for comparing relative importance of each across datasets. Assessment of fuzzy membership with linguistic scales may have minimal effect on overall accuracy, but can provide substantial improvement in user's accuracy and producer's accuracy for uncommon classes. We adapted non-site specific approaches to provide metrics and insights into spatial variability of map accuracy of thematic classes. Comparing estimates based on reference data and co-located samples of map data may reveal potential issues with reference data. We demonstrated example applications all components of the assessment framework for thematic maps of early successional forest and other land cover classes across the western Great Lakes region, USA.