Prioritization of Effective Factors on Zataria multiflora Habitat Suitability and its Spatial Modeling

Edalat, Mohsen; Jahangiri, Enayat; Dastras, Emran; Pourghasemi, Hamid Reza

doi:10.1016/b978-0-12-815226-3.00018-1

Cited by 9 publications

(7 citation statements)

References 41 publications

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“…Also, SVM and MaxEnt were comparatively used to spatially model landslide occurrence, and the results show that the highest areal percentage was allocated to the high susceptibility class by the SVM model, whereas the MaxEnt model allocated the lowest areal percentage to the high susceptibility class [45]. Therefore, this result highlights the superior performance of SVM in detecting the habitat suitability of Zataria multiflora Boiss [25]. Hence, the SVM is a useful tool for future planning regarding the conservation and management of plant species habitats.…”

Section: Discussionmentioning

confidence: 84%

“…Generally, the self-adaptability, rapid learning speed, and insensitivity to training size make the SVM a reliable method for the intelligent processing of remote sensing data [19][20][21]. Therefore, the SVM algorithm has the deterministic learning features of nonparametric data, and its high accuracy makes it an important and pleasant tool for habitat suitability mapping with an impressive predictive accuracy [22][23][24][25][26]. MaxEnt and SVMs yielded a good performance with the original data, indicating their sufficient regulation of multicollinearity in spatial distributions studies [25,27].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the SVM algorithm has the deterministic learning features of nonparametric data, and its high accuracy makes it an important and pleasant tool for habitat suitability mapping with an impressive predictive accuracy [22][23][24][25][26]. MaxEnt and SVMs yielded a good performance with the original data, indicating their sufficient regulation of multicollinearity in spatial distributions studies [25,27]. Their suitability for the assessment of species distribution and habitat suitability models has led them to become popular methods for evaluating habitat requirements in recent years.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

Boogar

Sher

Pourghasemi

et al. 2019

Water

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Support vector machine (SVM) and maximum entropy (MaxEnt) machine learning techniques are well suited to model the habitat suitability of species. In this study, SVM and MaxEnt models were developed to predict the habitat suitability of Juniperus spp. in the Southern Zagros Mountains of Iran. In recent decades, drought extension and climate alteration have led to extensive changes in the geographical occurrence of this species and its growth and regeneration are extremely limited in this area. This study evaluated the habitat suitability of Juniperus through spatial modeling and predicts appropriate regions for future cultivation and resource conservation. We modeled the natural habitat of Juniperus for an area of 700 ha in Sepidan Area in the Fars province using (1) data regarding the presence of the species (295 samples) collected through field surveys and GPS, (2) habitat soil information and indices derived from 60 soil samples collected in the study area, and (3) climatic and topographic datasets collected from various sources. In total, 15 conditioning factors were used for this spatial modeling approach. Receiver operator characteristic (ROC) curves were applied to estimate the accuracy of the habitat suitability models produced by the SVM and MaxEnt techniques. Results indicated logical and similar area under the curve (AUC)-ROC values for the SVM (0.735) and MaxEnt (0.728) models. Both the SVM and MaxEnt methods revealed a significant relationship between the Juniperus spp. distribution and conditioning factors. Environmental factors played a vital role in evaluating the presence of Juniperus sp. as Max and Min temperatures and annual mean rainfall were the three most important factors for habitat suitability in the study area. Finally, an area with high and very high suitability for the future cultivation of Juniperus sp. and for landscape conservation was suggested based on the SVM model.

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Section: Discussionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

Boogar

Sher

Pourghasemi

et al. 2019

Water

Self Cite

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“…IDW interpolation gives accurate results with a reasonable calculation based on the temporal and spatial structure (Maleika, 2020;Ryu et al, 2020;Yang et al, 2020). The spatial interpolation of the extreme rainfall data, using IDW algorithms, has given good results (Edalat et al, 2019;Tsangaratos et al, 2019). The IDW interpolation for estimating precipitation is given in Eqs.…”

Section: Spatial Analysis Of the Rainfall Datamentioning

confidence: 99%

Assessment of the performance of CORDEX regional climate models in simulating rainfall and air temperature over southwest Ethiopia

Demissie

Sime

2021

Heliyon

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This study analyzed the performance of four (REgional MOdel (REMO2009), High-Resolution Hamburg Climate Model 5 (HIRAM5), Climate Limited-Area Modeling Community (CCLM4-8) and Rossby Centre Regional Atmospheric Model (RCA4)) Regional Climate Models (RCMs) simulations from Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa program. The simulation period of 1985-2005 was evaluated considering how each RCM simulated the observed rainfall and air temperature over southwest Ethiopia. It was found that all the RCMs simulated the seasonal rainfall, but not the peak rainfall, with all models including their ensemble underestimating the peak rainfall. However, the ensemble was better than the individual RCMs in simulating both rainfall and air temperature. All models were slightly biased around a warm climate zone in simulating maximum air temperature when compared to the simulation of air minimum temperature. Of the four RCMs, REMO2009 performed well in simulating the maximum and minimum air temperatures. The interseasonal variation in rainfall was greater than the seasonal variation in air temperature. In terms of cumulative distribution, the HIRAM5 captured more extreme rainfall events and overestimated the return period. Overall, the differences in performance among the RCMs provided strong evidence for the use of regional-scale data at the local scale in climate impact assessments being controversial. In relation to the spatial pattern of the rainfall, most of the models simulated the observed minimum rainfall in the north and northeast, medium rainfall in the central region, and maximum rainfall in the south and southwest of the study area. The overall results indicate that choosing a reliable RCM is fundamentally necessary to delivering a strong basis for any climate-change impact study.

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“…• Support Vector Machine (SVM). This method was developed from the statistical learning theory, which reduces the error related to the size of the training or sample data [39,40]. It is a machine learning algorithm used in problems where inputoutput vector dependencies such as image classification and linear regression are unknown [41].…”

Section: Satellite Image Classification Algorithmsmentioning

confidence: 99%

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

Pech-May

Aquino-Santos

Ríos-Toledo

et al. 2022

Sensors

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Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.

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Prioritization of Effective Factors on Zataria multiflora Habitat Suitability and its Spatial Modeling

Cited by 9 publications

References 41 publications

Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

Assessment of the performance of CORDEX regional climate models in simulating rainfall and air temperature over southwest Ethiopia

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

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