Remote sensing variables as predictors of habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling mammal living in a desert environment

Campos, Valeria E.; Gatica, Gabriel; Bellis, Laura M.

doi:10.1007/s13364-015-0215-3

Cited by 10 publications

(10 citation statements)

References 40 publications

(62 reference statements)

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“…The mean texture of BI was lower on occurrence points than on absence points because low mean values indicate less bright areas, such as the rocky substratum (Campos et al. ), where this species occurs. Variance was also included in the best model; this measure is ecologically relevant since it captures textural heterogeneity (Wood et al.…”

Section: Discussionmentioning

confidence: 98%

Using remotely sensed data to model suitable habitats for tree species in a desert environment

Campos

Cappa

Viviana

et al. 2015

J Vegetation Science

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Questions Can the species–environment relationship be understood using current remote sensing techniques? Can the derived indicators of remotely sensed data serve as a proxy for variables that affect habitat suitability of plant species? Which remote sensing predictors are best associated with woody species occurrence in a desert environment? How well do models with derived indicators of remotely sensed data predict the occurrence of these species? What are the potential distributions of Ramorinoa girolae, Prosopis spp. and Bulnesia retama in the study area? Location Ischigualasto Provincial Park, San Juan province, Argentina. Methods We selected random field points from a Landsat 8 OLI to determine presence/absence of trees species. We calculated Brightness index (BI) using the same image and used this index to calculate texture measures on a 3 × 3 moving window size. We used the following subset of texture measures: (1) first‐order: range, (2) second‐order: mean, variance, contrast, entropy, second moment and correlation. We also calculated Topographic Wetness Index (TWI), slope angle and slope aspect from Global Digital Elevation Model. Results and Conclusion Second‐order mean of BI had an important effect on the occurrence of target trees species. TWI was an important variable for Prosopis spp. and B. retama, whereas slope angle was important for R. girolae and B. retama. In addition, the occurrence of R. girolae was affected by second‐order variance of BI and slope aspect; and the presence of B. retama was affected by second‐order contrast of BI. All the variables that had important effects on the occurrence of tree species provide environmental information about their different habitat requirements; therefore, our findings indicate that the remote sensing data are reliable to derive indicators of tree species presence in our study area.

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Section: Discussionmentioning

confidence: 98%

Using remotely sensed data to model suitable habitats for tree species in a desert environment

Campos

Cappa

Viviana

et al. 2015

J Vegetation Science

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“…To potentially extend their habitat to neighboring areas, we proposed a habitat suitability prediction method based on noninvasive field observations from remote sensing imaginh using a UAS multispectral camera system and machine learning classification. Since the evaluated environmental conditions, including vegetation, soil, and topography criteria, impact the ecological gradient of both flora and fauna species [50][51][52], the proposed approach should be applicable to a wide variety of endangered species. Moreover, the application of the UAS mounted multispectral camera system ensured that flora and fauna species would be minimally disturbed in their natural habitat during the field observations and non-invasive population count [53].…”

Section: Discussionmentioning

confidence: 99%

A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species

et al. 2022

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In this study, we propose integrating unmanned aerial systems (UASs) and machine learning classification for suitability prediction of expanding habitats for endangered flora species to prevent further extinction. Remote sensing imaging of the protected steppe-like grassland in Bilje using the DJI P4 Multispectral UAS ensured non-invasive data collection. A total of 129 individual flora units of five endangered flora species, including small pasque flower (Pulsatilla pratensis (L.) Miller ssp. nigricans (Störck) Zämelis), green-winged orchid (Orchis morio (L.)), Hungarian false leopardbane (Doronicum hungaricum Rchb.f.), bloody cranesbill (Geranium sanguineum (L.)) and Hungarian iris (Iris variegate (L.)) were detected and georeferenced. Habitat suitability in the projected area, designated for the expansion of the current area of steppe-like grassland in Bilje, was predicted using the binomial machine learning classification algorithm based on three groups of environmental abiotic criteria: vegetation, soil, and topography. Four machine learning classification methods were evaluated: random forest, XGBoost, neural network, and generalized linear model. The random forest method outperformed the other classification methods for all five flora species and achieved the highest receiver operating characteristic (ROC) values, ranging from 0.809 to 0.999. Soil compaction was the least favorable criterion for the habitat suitability of all five flora species, indicating the need to perform soil tillage operations to potentially enable the expansion of their coverage in the projected area. However, potential habitat suitability was detected for the critically endangered flora species of Hungarian false leopardbane, indicating its habitat-related potential for expanding and preventing further extinction. In addition to the current methods of predicting current coverage and population count of endangered species using UASs, the proposed method could serve as a basis for decision making in nature conservation and land management.

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“…Little is known about the specific impact of forest structure on small mammal diversity and less still on how LiDAR measurements of forest structure relate to the life history requirements of multiples species of small mammals [12,62]. Thus, we analyzed the ability of few specific variables that encompass a wide range of forest structural attributes to predict small mammal diversity.…”

Section: Discussionmentioning

confidence: 99%

“…The few prior studies on remotely-sensed habitat suitability for small mammals have found LiDAR effectively predicts small mammal habitat use. However, these studies are focused on abundance and habitat use of single species, rather than overall small mammal community diversity [11][12][13]. Small mammals crucially contribute to ecosystem function through their roles in seed propagation, organic matter decomposition, and soil mixing [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Lidar Prediction of Small Mammal Diversity in Wisconsin, USA

Schooler

Zald

2019

Remote Sensing

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Vegetation structure is a crucial component of habitat selection for many taxa, and airborne LiDAR (Light Detection and Ranging) technology is increasingly used to measure forest structure. Many studies have examined the relationship between LiDAR-derived structural characteristics and wildlife, but few have examined those characteristics in relation to small mammals, specifically, small mammal diversity. The aim of this study was to determine if LiDAR could predict small mammal diversity in a temperate-mixed forest community in Northern Wisconsin, USA, and which LiDAR-derived structural variables best predict small mammal diversity. We calculated grid metrics from LiDAR point cloud data for 17 plots in three differently managed sites and related the metrics to small mammal diversity calculated from five months of small mammal trapping data. We created linear models, then used model selection and multi-model inference as well as model fit metrics to determine if LiDAR-derived structural variables could predict small mammal diversity. We found that small mammal diversity could be predicted by LiDAR-derived variables including structural diversity, cover, and canopy complexity as well as site (as a proxy for management). Structural diversity and canopy complexity were positively related with small mammal diversity, while cover was negatively related to small mammal diversity. Although this study was conducted in a single habitat type during a single season, it demonstrates that LiDAR can be used to predict small mammal diversity in this location and possibly can be expanded to predict small mammal diversity across larger spatial scales.

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Remote sensing variables as predictors of habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling mammal living in a desert environment

Cited by 10 publications

References 40 publications

Using remotely sensed data to model suitable habitats for tree species in a desert environment

Using remotely sensed data to model suitable habitats for tree species in a desert environment

A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species

Lidar Prediction of Small Mammal Diversity in Wisconsin, USA

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