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This paper applies different methods of map comparison to quantify the characteristics of three different land change models. The land change models used for simulation are termed as “Stochastic Markov (St_Markov)”, “Cellular Automata Markov (CA_Markov)” and “Multi Layer Perceptron Markov (MLP_Markov)” models. Various model validation techniques such as per category method, kappa statistics, components of agreement and disagreement, three map comparison and fuzzy methods have then been applied. A comparative analysis of the validation techniques has also been discussed. In all cases, it is found that “MLP_Markov” gives the best results among the three modeling techniques. Fuzzy set theory is the method that seems best able to distinguish areas of minor spatial errors from major spatial errors. Based on the outcome of this paper, it is recommended that scientists should try to use the Kappa, three map comparison and fuzzy methods for model validation. This paper facilitates communication among land change modelers, because it illustrates the range of results for a variety of model validation techniques and articulates priorities for future research.

The free availability of Sentinel-1 and 2 imageries enables the production of high resolution (10 m) global Land Use Land Cover (LULC) maps by a wide range of institutions, which often make them publicly available. This raises several issues: Which map should be used for each type of application? How accurate are these maps? What is the level of agreement between them? This motivated us to assess the thematic accuracy of six LULC maps for continental Portugal with 10 m spatial resolution with reference dates between 2017 and 2020, using the same method and the same reference database, in a bid to make the results comparable. The overall accuracy and the per class user’s and producer’s accuracy are compared with the ones reported by the map producers, at the national, European, or global level, according to their availability. The nomenclatures of the several maps were then analyzed and compared to generate a harmonized nomenclature to which all maps were converted into. The harmonized products were compared directly with a visual analysis and the proportion of regions equally classified was computed, as well as the area assigned per product to each class. The accuracy of these harmonized maps was also assessed considering the previously used reference database. The results show that there are significant differences in the overall accuracy of the original products, varying between 42% and 72%. The differences between the user’s and producer’s accuracy per class are very large for all maps. When comparing the obtained results with the ones reported by the map producers for Portugal, Europe or globally (depending on what is available) the results obtained in this study have lower accuracy metrics values for all maps. The comparison of the harmonized maps shows that they agree in 83% of the study area, but there are differences in terms of detail and area of the classes, mainly for the class “Built up” and “Bare land”.

We prove that a jointly conservative family of geometric functors between rigidly-compactly generated tensor triangulated categories induces a surjective map on Balmer spectra. From this we deduce a fiberwise criterion for Balmer’s comparison map to be a continuous bijection. This gives short alternative proofs of the Hopkins–Neeman theorem and its generalization, due to Lau, to the case of a finite group acting trivially on an affine scheme.

Aim: Species distribution modelling is commonly used to guide future conservation policies in the light of potential climate change. However, arbitrary decisions during the model-building process can affect predictions and contribute to uncertainty about where suitable climate space will exist. For many species, the key climatic factors limiting distributions are unknown. This paper assesses the uncertainty generated by using different climate predictor variable sets for modelling the impacts of climate change. Location: Europe, 10°W to 50° E and 30° N to 60 ° N. Methods: Using 1453 presence pixels at 30 arcsec resolution for the great bustard (Otis tarda), predictions of future distribution were made based on two emissions scenarios, three general climate models and 26 sets of predictor variables. Twentysix current models were created, and 156 for both 2050 and 2080. Map comparison techniques were used to compare predictions in terms of the quantity and the location of presences (map comparison kappa, MCK) and using a range change index (RCI). Generalized linear models (GLMs) were used to partition explained deviance in MCK and RCI among sources of uncertainty. Results: The 26 different variable sets achieved high values of AUC (area under the receiver operating characteristic curve) and yet introduced substantial variation into maps of current distribution. Differences between maps were even greater when distributions were projected into the future. Some 64-78% of the variation between future maps was attributable to choice of predictor variable set alone. Choice of general climate model and emissions scenario contributed a maximum of 15% variation and their order of importance differed for MCK and RCI. Main conclusions: Generalized variable sets produce an unmanageable level of uncertainty in species distribution models which cannot be ignored. The use of sound ecological theory and statistical methods to check predictor variables can reduce this uncertainty, but our knowledge of species may be too limited to make more than arbitrary choices. When all sources of modelling uncertainty are considered together, it is doubtful whether ensemble methods offer an adequate solution.Future studies should explicitly acknowledge uncertainty due to arbitrary choices in the model-building process and develop ways to convey the results to decision-makers.

Accurate modeling and simulation of forest land cover change resulting from epidemic insect outbreaks play a crucial role in equipping scientists and forest managers with essential insights. These insights enable proactive planning and the formulation of effective strategies to mitigate the impact of such disturbances. By employing advanced modeling techniques, researchers and managers can anticipate the evolving dynamics of forest ecosystems, thereby facilitating timely interventions and sustainable management practices. In this study, we applied sixteen machine-learning models, plus two ensemble averaging procedures, to Mountain Pine Beetle (Dendroctonus ponderosae) infestation data in British Columbia, to calculate projections of insect-induced deforestation. Model drivers included topographic, climatic and adjacency variables. We verified the results of the simulations by randomly splitting datasets between training and test subsets (aka Validation assessment), as well as by comparing future projections with observations (aka Prediction assessment). All calculations were carried out for different mountain pine beetle map sets and time differences, and we employed up to seven performance metrics (six threshold-dependent and one threshold-independent) and four error metrics to assess goodness of prediction. ANCOVA tests were then run on metric results to test differences between Validation and Prediction assessments. In addition, we computed Friedman rankings for all simulation and metrics. Our results showed that validation assessments were, most of the time, significantly more optimistic than prediction assessments. We also noted that different conclusions could be reached for different performance metrics. We conclude that, for prediction purposes, error metrics and components of the confusion table were most helpful in understanding the ability and limitations of Mountain Pine Beetle predictive maps. These results also suggest that, in general, care must be taken in assessing prediction performance of machine-learning models based solely on validation tests.

This work presents a comparison between a global and a national land cover map, namely the ESA WorldCover 2020 (WC20) and the Portuguese use/land cover map (Carta de Uso e Ocupação do Solo 2018) (COS18). Such a comparison is relevant given the current amount of publicly available LULC products (either national or global) where such comparative studies enable a better understanding regarding different sets of LULC information and their production, focus and characteristics, especially when comparing authoritative maps built by national mapping agencies and global land cover focused products. Moreover, this comparison is also aimed at complementing the global validation report released with the WC20 product, which focused on global and continental level accuracy assessments, with no additional information for specific countries. The maps were compared by following a framework composed by four steps: (1) class nomenclature harmonization, (2) computing cross-tabulation matrices between WC20 and the Portuguese map, (3) determining the area occupied by each harmonized class in each data source, and (4) visual comparison between the maps to illustrate their differences focusing on Portuguese landscape details. Some of the differences were due to the different minimum mapping unit ofCOS18 and WC20, different nomenclatures and focuses on either land use or land cover. Overall, the results show that while WC20 detail is able to distinguish small occurrences of artificial surfaces and grasslands within an urban environment, WC20 is often not able to distinguish sparse/individual trees from the neighboring cover, which is a common occurrence in the Portuguese landscape. While selecting a map, users should be aware that differences between maps can have a range of causes, such as scale, temporal reference, nomenclature and errors.

Useful germplasm for citrus breeding includes all sexually compatible species of the former genera , and , now merged in the single genus. An improved knowledge on the synteny/collinearity between the genome of these different species, and on their recombination landscapes, is essential to optimize interspecific breeding schemes. We have performed a large comparative genetic mapping study including several main clades of the genus. It concerns five species ( and ), two horticultural groups resulting from interspecific admixture (clementine and lemon) and two recent interspecific hybrids ( ). The nine individual genetic maps were established from GBS data of 1,216 hybrids. The number of SNPs mapped for each parent varies from 760 for to 4,436 for the hybrid, with an average of 2,162.3 markers by map. Their comparison with v1.0 assembly and inter-map comparisons revealed a high synteny and collinearity between the nine genetic maps. Non-Mendelian segregation was frequent and specific for each parental combination. The recombination landscape was similar for the nine mapped parents, and large genomic regions with very low recombination were identified. A consensus genetic map was successfully established. It encompasses 10,756 loci, including 7,915 gene-based markers and 2,841 non-genic SNPs. The anchoring of the consensus map on 15 published citrus chromosome-scale genome assemblies revealed a high synteny and collinearity for the most recent assemblies, whereas discrepancies were observed for some older ones. Large structural variations do not seem to have played a major role in the differentiation of the main species of the Citrus genus. The consensus genetic map is a useful tool to check the accuracy of genome assemblies, identify large structural variation and focus on analyzing potential relationships with phenotypic variations. It should also be a reference framework to integrate the positions of QTLs and useful genes identified in different analyses.

The construction of high-speed railways (HSRs) is the core engine for promoting the economic integration and spatial structure optimization of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Changes in land use along HSR corridors are inextricably linked to the efficacy of regional coordinated development and ecological protection initiatives, as well as the realization of regional sustainable development. Nevertheless, past relevant studies exhibit prominent limitations. First, the lack of effective methodologies for the intuitive comparison of multiple research subjects makes it difficult to accurately portray the differential characteristics of land use across various HSR routes. Second, the insufficient comprehensive analysis of the dynamic evolution of landscape patterns along routes, coupled with the absence of intuitive spatial visualization expressions, fails to explicitly reveal the spatiotemporal differentiation of landscape fragmentation, which hinders sustainable land resource utilization and ecological protection. To address these gaps, this study introduces the intensity comparison map and the comprehensive index map of landscape fragmentation and takes six typical HSRs in the GBA to conduct an intuitive comparative analysis of land use changes along multiple routes. Results show that land use evolution along HSRs presents distinct phased characteristics, with construction land acting as the core driving factor. Its proportion increases continuously, while the proportions of cultivated land and water bodies decline dramatically. Significant disparities exist in land use evolution across different HSR routes, which are closely associated with the natural and economic conditions of the traversed regions, reflecting the heterogeneous adaptability between individual routes and regional development dynamics. High landscape fragmentation areas are predominantly distributed in the transition zones between construction land and natural landscapes; fragmentation intensifies during the planning and construction phases and stabilizes or even diminishes along certain routes during the operation phase, with human activities identified as the pivotal influencing factor. This research deepens the understanding of the interaction mechanism between transportation infrastructure and land use changes in the GBA and provides a scientific basis for sustainable HSR construction planning, the rational utilization of land resources, and the coordinated advancement of ecological protection in the GBA and other similar regions worldwide, thus facilitating the sustainable development of high-density urban agglomerations globally.

Land-use/land-cover change is the most important factor in causing biodiversity loss. The Mediterranean region has been affected by antropic disturbance for thousands of years, and is, nowadays, one of the most significantly altered hotspots in the world. However, in the last years a significant increase in forest cover has been measured. These new patterns are independent from planned conservation strategies and appear to have a substantial impact on landscapes and biodiversity. We used three land-use/land-cover maps (from 1960 to 2000) covering the Italian peninsula to analyze the pattern of land-use/land-cover change. We measured an increase in forests, especially in mountains, an increase in artificial areas, especially in coastal zones, and a decrease in pastures. Intensively cultivated areas showed a limited decrease while extensively cultivated ones showed a marked decrease. In the same period mammal and bird species followed a similar pattern, with forest birds, ungulates and carnivores increasing, and typically Mediterranean species decreasing. We suggest that our results may provide important information, which could be useful for conservation planning in the entire Mediterranean hotspot. We suggest that an increasing conservation effort should be made to protect the Mediterranean-type forests and scrublands, as well as traditional agricultural practices. Moreover, future conservation efforts should consider the broad socio-political and ecological processes that are most likely to occur across the whole hotspot, especially along coastal areas, and the network of protected areas should be functionally integrated in a conservation strategy that includes the human-dominated landscape.

Variations in climatic pattern due to boundary layer processes at the topoclimatic scale are critical for ecosystems and human activity, including agriculture, fruit harvesting, and animal husbandry. Here, a new method for topoclimate mapping based on land surface temperature (LST) computed from the brightness temperature of Landsat ETM+ thermal bands (band6) is presented. The study was conducted in a coastal lowland area with glacial landforms (Wolin Island). The method presented is universal for various areas, and is based on freely available remote sensing data. The topoclimatic typology obtained was compared to the classical one based on meteorological data. It was proven to show a good sensitivity to changes in topoclimatic conditions (demonstrated by changes in LST distribution) even in flat, agricultural areas with only small variations in topography. The technique will hopefully prove to be a convenient and relatively fast tool that can improve the topoclimatic classification of other regions. It could be applied by local authorities and farmer associations for optimizing agricultural production.