Mapping key browse resources in a heterogeneous agricultural landscape (original) (raw)
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Predicting habitat distribution and frequency from plant species co-occurrence data
Journal of Biogeography, 2007
Aim Species frequency data have been widely used in nature conservation to aid management decisions. To determine species frequencies, information on habitat occurrence is important: a species with a low frequency is not necessarily rare if it occupies all suitable habitats. Often, information on habitat distribution is available for small geographic areas only. We aim to predict grid-based habitat occurrence from grid-based plant species distribution data in a meso-scale analysis.
Determination of the Potential Habitat of Range Plant Species Using Maximum Entropy Method
Journal of Rangeland Science, 2020
This study aimed to identify the most important physical variables affecting the distribution of four range plants species (Tamarix aphylla, Calligonum comosum, Prosopis spicigera and Salsola rigida) habitats and to prepare potential habitat map of the species using Maximum Entropy (MaxEnt) method in rangelands of Jiroft city, Kerman province, located in SE Iran. To this end, sampling of vegetation including species type and percent cover was conducted with randomized-systematic method in 2015. Sample size was determined as 60 plots with a quadrat size of 25-100 m2. For soil sampling, eight profiles were dug in each habitat and samples were taken at two depths, i.e., 0–30 and 30–60 cm. Results indicated that the classification accuracy of the model was acceptable and soil variables including EC, percentage of lime, organic matter, moisture content and texture had the greatest effect on the distribution of the studied plant species habitats. Correlations between the actual and predic...
Frontiers in remote sensing, 2024
Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%-91.7%) across all the zones and 89.1% (50%-100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%-97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%-98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is
Determination of Potential Habitat of Range Plant Species Using Maximum Entropy Method
This study aimed to identify the most important physical variables affecting the distribution of four range plant species (Tamarix aphylla, Calligonum comosum, Prosopis spicigera and Salsola rigida) habitats and to prepare potential habitat map of the species using Maximum Entropy (MaxEnt) method in rangelands of Jiroft city, Kerman province, Iran. To this end, sampling of vegetation including species type and percent cover was conducted with a randomized-systematic method in 2015. Sample size was determined as 60 plots with a quadrat size of 25-100 m 2. For soil sampling, eight profiles were dug in each habitat and samples were taken at two depths, i.e., 0-30 and 30-60 cm. Results indicated that the classification accuracy of the model was acceptable and soil variables including EC, percentage of lime, organic matter, moisture content and texture had the greatest effect on the distribution of the studied plant species habitats. Correlations between the actual and predicted maps for Tamarix aphylla and Calligonum comosum habitats were at a very good level, Kappa= 0.81 and 0.79, respectively; for Prosopis spicigera habitat, it was at a good level, Kappa= 0.75, and finally for Salsola rigida, it was at a moderate level, Kappa = 0.53. These results indicate that the MaxEnt method can provide valuable information about the physical conditions of plant habitats in arid rangeland. Knowledge on physical characteristics of plant habitats can be useful in determination of potential habitats and rangeland improvement projects.
Mountainous rangelands play a pivotal role in providing forage resources for livestock, particularly in summer, and maintaining ecological balance. This study aimed to identify environmental variables affecting range plant species distribution, ecological analysis of the relationship between these variables and the distribution of plants, and to model and map the plant habitats suitability by the Random Forest Method (RFM) in rangelands of the Taftan Mountain, Sistan and Baluchestan Province, southeastern Iran. In order to determine the environmental variables and estimate the potential distribution of plant species, the presence points of plants were recorded by using systematic random sampling method (90 points of presence) and soils were sampled in 5 habitats by random method in 0-30 and 30-60 cm depths. The layers of environmental variables were prepared using the Kriging interpolation method and Geographic Information System facilities. The distribution of the plant habitats was finally modelled and mapped by the RFM. Continuous maps of the habitat suitability were converted to binary maps using Youden Index () in order to evaluate the accuracy of the RFM in estimation of the distribution of species potential habitat. Based on the values of the area under curve (AUC) statistics, accuracy of predictive models of all habitats was in good level. Investigating the agreement between the predicted map, generated by each model, and actual maps, generated from fieldmeasured data, of the plant habitats, was at a high level for all habitats, except for Amygdalus scoparia habitat. This study concluded that the RFM is a robust model to analyze the relationships between the distribution of plant species and environmental variables as well as to prepare potential distribution maps of plant habitats that are of higher priority for conservation on the local scale in arid mountainous rangelands.
Remote Sensing, 2016
Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced dataset with many samples for common species and few samples for less common species. Despite the high prevalence of imbalanced datasets in multiclass species predictions, the effect on species prediction accuracy and landscape species abundance has not yet been quantified. First, we trained and assessed the accuracy of a support vector machine (SVM) model with a highly imbalanced dataset of 20 tropical species and one mixed-species class of 24 species identified in a hyperspectral image mosaic (350-2500 nm) of Panamanian farmland and secondary forest fragments. The model, with an overall accuracy of 62%˘2.3% and F-score of 59%˘2.7%, was applied to the full image mosaic (23,000 ha at a 2-m resolution) to produce a species prediction map, which suggested that this tropical agricultural landscape is more diverse than what has been presented in field-based studies. Second, we quantified the effect of class imbalance on model accuracy. Model assessment showed a trend where species with more samples were consistently over predicted while species with fewer samples were under predicted. Standardizing sample size reduced model accuracy, but also reduced the level of species over-and under-prediction. This study advances operational species mapping of diverse tropical landscapes by detailing the effect of imbalanced data on classification accuracy and providing estimates of tree species abundance in an agricultural landscape. Species maps using data and methods presented here can be used in landscape analyses of species distributions to understand human or environmental effects, in addition to focusing conservation efforts in areas with high tree cover and diversity.
Land unit approach for biodiversity mapping
Landscape Ecology Applied in Land …, 2001
Conservation of most wild species relies on habitat maintenance, which can rely on vegetation and terrain features. In land ecology, these terrain features are used to delineate land units that describe structure and function of geographic entities in space and time. The aim of this paper is to depict diagnostic species and link them into land units to define habitats. In the present study, 6500 records of vertebrate and vascular plant were gathered into a database. Diagnostic species selected on the basis of similar distribution patterns along ordination axes were represented into land units. From the 1162 species recorded, 122 were identified as diagnostic species (12 amphibians, 42 reptiles, 37 birds, 11 mammals and 20 vascular plants). Nine land units were recognised from the combination of terrain and vegetation clusters. Three species clusters were detected according to the number of species comprised. Volcanic bodies and Holocene lava flows harbour the most diagnostic species but a smaller number of species, whereas mixed forest, meadows and crops on foot-slopes and accumulation plains harbour fewer diagnostic species but larger number of species. These patterns were spatially displayed and discussed in the light of their role in conservation and management.
Sustainability, 2019
The identification of geographical distribution of a plant species is crucial for understanding the importance of environmental variables affecting plant habitat. In the present study, the spatial potential distribution of Astragalus fasciculifolius Boiss. as a key specie was mapped using maximum entropy (Maxent) as data mining technique and bivariate statistical model (FR: frequency ratio) in marl soils of southern Zagros, Iran. The A. fasciculifolius locations were identified and recorded by intensive field campaigns. Then, localities points were randomly split into a 70% training dataset and 30% for validation. Two climatic, four topographic, and eight edaphic variables were used to model the A. fasciculifolius distribution and its habitat potential. Maps of environmental variables were generated using Geographic Information System (GIS). Next, the habitat suitability index (HSI) maps were produced and classified by means of Maxent and FR approaches. Finally, the area under the receiver operating characteristic (AUC-ROC) curve was used to compare the performance of maps produced by Maxent and FR models. The interpretation of environmental variables revealed that the climatic and topographic parameters had less impact compared to edaphic variables in habitat distribution of A. fasciculifolius. The results showed that bulk density, nitrogen, acidity (pH), sand, and electrical conductivity (EC) of soil are the most significant variables that affect distribution of A. fasciculifolius. The validation of results showed that AUC values of Maxent and FR models are 0.83 and 0.76, respectively. The habitat suitability map by the better model (Maxent) showed that areas with high and very high suitable classes cover approximately 22% of the study area. Generally, the habitat suitability map produced using Maxent model could provide important information for conservation planning and a reclamation project of the degraded habitat of intended plant species. The distribution of the plants identifies the water, soil, and nutrient resources and affects the fauna distribution, and this is why it is relevant to research and to understand the plant distribution to properly improve the management and to achieve a sustainable management.
Ecological Informatics, 2019
Rangelands with more than 8000 plant species occupy nearly 54.6% of the land area of Iran and thus are accounted for a rich plant genetic storage. Mazandaran province has 378,000 ha of rangelands with high plant species richness and diversity due to its climate conditions but plants distribution is at risk because of nonprinciple management, land use change and as a result changing environmental factors. Vegetation management strategies can be guided by models that predict plant species distribution based on governing environmental variables. This is especially useful for the dominant species that determine ecosystem processes. In fact, modelling algorithm in each SDM determines its suitability for different ecosystems. Our aim was to compare the predictive power of a number of SDMs and to evaluate the importance of a range of environmental variables as predictors in the context of semi-arid rangeland vegetation. The selected study area, the Sarkhas rangelands (northern Iran, 36°10′ 42˝N-51°19′ 11˝E), covers approximately 4358.9 ha of Mazandaran province. The efficacy of four different modelling techniques as well as Ensemble model was evaluated to predict the distribution of five dominant forage plant species (Vicia villosa, Stachys lavandulifolia, Coronilla balansae, Sanguisorba minor and Alopecurus textilis). The used models included artificial neural network (ANN), boosted regression trees (BRT), classification and regression trees (CART), and random forest (RF). Ensemble, RF and CART had the highest area under curve. The AUC obtained for Vicia villosa, Stachys lavandulifolia, Coronilla balansae, Sanguisorba minor and Alopecurus textilis, were 0.90, 0.72, 0.76, 0.69 and 0.75 respectively. Ensemble model was the model that most consistently demonstrated high predictive power across species in the rangeland context investigated here. BRT exhibited the least predictive power. An importance analysis of variables showed that soil organic C according to the CART model (0.396) and K according to the RF model (0.396) were the most important environmental variables.
Landscape and urban …, 2010
Mismatches in boundaries between natural ecosystems and land governance units often complicate an ecosystem approach to management and conservation. For example, information used to guide management, such as vegetation maps, may not be available or consistent across entire ecosystems. This study was undertaken within a single biogeographic region (the Murray Mallee) spanning three Australian states. Existing vegetation maps could not be used as vegetation classifications differed between states. Our aim was to describe and map 'tree mallee' vegetation consistently across a 104 000 km 2 area of this region. Hierarchical cluster analyses, incorporating floristic data from 713 sites, were employed to identify distinct vegetation types. Neural network classification models were used to map these vegetation types across the region, with additional data from 634 validation sites providing a measure of map accuracy. Four distinct vegetation types were recognised: Triodia Mallee, Heathy Mallee, Chenopod Mallee and Shrubby Mallee. Neural network models predicted the occurrence of three of them with 79% accuracy. Validation results identified that map accuracy was 67% (kappa = 0.42) when using independent data. The framework employed provides a simple approach to describing and mapping vegetation consistently across broad spatial extents. Specific outcomes include: (1) a system of vegetation classification suitable for use across this biogeographic region; (2) a consistent vegetation map to inform land-use planning and biodiversity management at local and regional scales; and (3) a quantification of map accuracy using independent data. This approach is applicable to other regions facing similar challenges associated with integrating vegetation data across jurisdictional boundaries.