Predicting Mammal Species Richness from Remotely Sensed Data at Different Spatial Scales

2009 ◽  
Vol 1 (1) ◽  
pp. 7-16
Author(s):  
Boniface Oluoch Oindo
Keyword(s):  
Species Richness ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Mammal Species

scholarly journals Comparison of MODIS and SWAT Evapotranspiration over a Complex Terrain at Different Spatial Scales

2017 ◽  
Author(s):  
Olanrewaju O. Abiodun ◽  
Huade Guan ◽  
Vincent E. A. Post ◽  
Okke Batelaan
Keyword(s):  
Complex Terrain ◽  
Spatial Scales ◽  
Swat Model ◽  
South Australia ◽  
Remotely Sensed ◽  
Distributed Model ◽  
Climate Data ◽  
Remotely Sensed Data ◽  
Catchment Scale ◽  
Distributed Hydrological Models

Abstract. In most hydrological systems, evapotranspiration (ET) and precipitation are the largest components of the water balance, which are difficult to estimate, particularly over complex terrain. In recent decades, the advent of remotely-sensed data based ET algorithms and distributed hydrological models has provided improved spatially-upscaled ET estimates. However, information on the performance of these methods at various spatial scales is limited. This study compares the ET from the MODIS remotely sensed ET dataset (MOD16) with the ET estimates from a SWAT hydrological model for the complex terrain of the Sixth Creek Catchment of the Western Mount Lofty Ranges, South Australia. The SWAT model analyses are performed on daily timescales with a 6-year calibration period (2000–2005) and 7-year validation period (2007–2013). Differences in ET estimation between the two methods of up to 48 %, 21 % and 16 % were observed at respectively 1 km2, 5 km2 and 10 km2 spatial resolutions. Land cover differences, mismatches between the two methods and catchment-scale averaging of input climate data in the SWAT semi-distributed model were identified as the principal sources of weaker correlations at higher spatial resolution.

Incorporating local habitat heterogeneity and productivity measures when modelling vertebrate richness

2019 ◽  
Vol 47 (1) ◽  
pp. 7-14 ◽  
Author(s):  
W Justin Cooper ◽  
William J McShea ◽  
David A Luther ◽  
Tavis Forrester
Keyword(s):  
Species Richness ◽  
Habitat Heterogeneity ◽  
Spatial Scales ◽  
Model Performance ◽  
Remotely Sensed ◽  
Habitat Characteristics ◽  
Measurement Scale ◽  
Terrestrial Mammals ◽  
Declining Species ◽  
Monitoring Technologies

SummaryDeclining species richness is a global concern; however, the coarse-scale metrics used at regional or landscape levels might not accurately represent the important habitat characteristics needed to estimate species richness. Currently, there exists a lack of knowledge with regard to the spatial extent necessary to correlate remotely sensed habitat metrics to species richness and animal surveys. We provide a protocol for determining the best scale to use when merging remotely sensed habitat and animal survey data as a step towards improving estimates of vertebrate species richness on broad scales. We test the relative importance of fine-resolution habitat heterogeneity and productivity metrics at multiple spatial scales as predictors of species richness for birds, frogs and mammals using a Bayesian approach and a combination of passive monitoring technologies. Model performance was different for each taxonomic group and dependent on the scale at which habitat heterogeneity and productivity were measured. Optimal scales included a 20-m radius for bats and frogs, an 80-m radius for birds and a 180-m radius for terrestrial mammals. Our results indicate that optimal scales do exist when merging remotely sensed habitat measures with ground-based surveys, but they differ between vertebrate groups. Additionally, the selection of a measurement scale is highly influential to our understanding of the relationships between species richness and habitat characteristics.

Human interfaces and urban change

2010 ◽  
Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector
Keyword(s):  
Species Richness ◽  
North America ◽  
Agricultural Land ◽  
Population Change ◽  
Human Impacts ◽  
Remotely Sensed ◽  
Conservation Priorities ◽  
Data Sets ◽  
Remotely Sensed Data ◽  
Political Boundaries

For the first time in human history, more people live in urban areas than in rural areas, and the patterns of suburbanization and urban sprawl once characteristic of North America are now present globally (Obaid 2007). As conservation biologists seek to prioritize conservation efforts worldwide, urbanization and agricultural development emerge as two of the most extensive processes that threaten biodiversity. Suburban and rural sprawl are significant drivers of forest fragmentation and biodiversity loss (e.g., Murphy 1988; Radeloff et al. 2005). Data on human impacts is often averaged across political boundaries rather than biogeographic boundaries, making it challenging to use existing data sets on human demography in ecological studies and relate human population change to the changes in populations of other species. Remotely sensed data can make major contributions to mapping human impacts in ecologically relevant ways. For example, Ricketts and Imhoff (2003) assigned conservation priorities (based on species richness and endemism) for the United States and Canada using several different types of remotely sensed data. For mapping urban cover, they used the map of “city lights at night” from the Defense Meteorological Satellite Program (Imhoff et al. 1997) to classify land as urbanized or not urbanized. For mapping agricultural cover, they used the USGS North America Seasonal Land Cover map (Loveland et al. 2000), derived from the Advanced Very High Resolution Radiometer (AVHRR), lumping five categories to create an agricultural land class. For ecological data, they used a compilation of ecoregion boundaries combined with range maps for over 20,000 species in eight taxa (birds, mammals, butterflies, amphibians, reptiles, land snails, tiger beetles, and vascular plants; Ricketts et al. 1999). Analyzing these data, Ricketts and Imhoff (2003) identified a strong correlation between species richness and urbanization. Of the 110 ecoregions studied, 18 ranked in the top third for both urbanization and biodiversity (species richness, endemism, or both); some of the ecoregions identified as priorities were not identified by a previous biodiversity assessment that did not include the remotely sensed mapping of urbanization (Ricketts et al. 1999).

scholarly journals Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure

2021 ◽  
Vol 13 (4) ◽  
pp. 2375
Author(s):  
Sangchul Lee ◽  
Junyu Qi ◽  
Hyunglok Kim ◽  
Gregory W. McCarty ◽  
Glenn E. Moglen ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Assessment Tool ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Hydrologic Models ◽  
Daily Streamflow ◽  
Model Predictions ◽  
Improved Model ◽  
Water Assessment

There is a certain level of predictive uncertainty when hydrologic models are applied for operational purposes. Whether structural improvements address uncertainty has not well been evaluated due to the lack of observational data. This study investigated the utility of remotely sensed evapotranspiration (RS-ET) products to quantitatively represent improvements in model predictions owing to structural improvements. Two versions of the Soil and Water Assessment Tool (SWAT), representative of original and improved versions, were calibrated against streamflow and RS-ET. The latter version contains a new soil moisture module, referred to as RSWAT. We compared outputs from these two versions with the best performance metrics (Kling–Gupta Efficiency [KGE], Nash-Sutcliffe Efficiency [NSE] and Percent-bias [P-bias]). Comparisons were conducted at two spatial scales by partitioning the RS-ET into two scales, while streamflow comparisons were only conducted at one scale. At the watershed level, SWAT and RSWAT produced similar metrics for daily streamflow (NSE of 0.29 and 0.37, P-bias of 1.7 and 15.9, and KGE of 0.47 and 0.49, respectively) and ET (KGE of 0.48 and 0.52, respectively). At the subwatershed level, the KGE of RSWAT (0.53) for daily ET was greater than that of SWAT (0.47). These findings demonstrated that RS-ET has the potential to increase prediction accuracy from model structural improvements and highlighted the utility of remotely sensed data in hydrologic modeling.

Remotely-Sensed Oil and Shoreline Interaction Modeling

2017 ◽  
Vol 2017 (1) ◽  
pp. 2600-2619
Author(s):  
Zachary Nixon ◽  
Jacqueline Michel ◽  
Scott Zengel
Keyword(s):  
Oil Spill ◽  
Spatial Scales ◽  
Information Service ◽  
Deepwater Horizon ◽  
Remotely Sensed ◽  
Water Levels ◽  
Remotely Sensed Data ◽  
Oil Distribution ◽  
Wide Range ◽  
Interaction Modeling

ABSTRACT No. 2017-233 The broad adoption of remotely sensed data and derivative products from satellite and aerial platforms available to describe the distribution of spilled oil on the water surface was an important factor during Deepwater Horizon (DWH) oil spill both for tactical response and damage assessment. The availability and utility of these data in describing on-water oil distribution provide strong temptation to make estimates about on-shoreline oil distribution. The mechanisms by which floating oil interact with the shoreline, however, are extremely complex, heterogeneous at fine spatial scales, and generally not well described or quantified beyond broad conceptual or spill-specific empirical models. In short, oil on water does not necessarily lead to oil on adjacent shorelines. We combine data derived from NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) using a variety of satellite platforms of opportunity describing the remotely-sensed, daily composite anomaly polygons representing oil on water over multiple months with ground observations made in the field, collocated in time and space extracted from a newly compiled database of ground survey data (SCAT, NRDA and others) from the northwestern Gulf of Mexico. Because this new compiled dataset is very large (100,000s of observations) and spans a wide range of habitats, geography, and time, it is particularly suitable for inference and predictive modeling. We use these combined datasets to make inference about the relative influence on shoreline oiling probability and loading of distance from on-water oil observation via multiple distance metrics, shoreline morphology, water levels and ranges, wind direction and speed, wave energy, shoreline aspect and geometry. We also construct predictive models using machine-learning modeling methods to make predictions about shoreline oiling probability given observed distributions of on-water oil. The importance of this work is three part: firstly, the relationships between these parameters can assist hind-cast modeling of shoreline oiling probability for the Deepwater Horizon oil spill. Secondly, these data and models can permit similar modeling for future spills. Lastly, we propose that this dataset serve as a nucleus that can be expanded using data from subsequent or future spills to allow iteratively improvements in shoreline oil probability modeling using remotely sensed data, as well as an improved understanding of oil-shoreline interactions more generally.

Relating mammal species richness to landscape patterns across multiple spatial scales

2021 ◽  
Vol 36 (4) ◽  
pp. 1003-1022
Author(s):  
Annalie Dorph ◽  
Matthew Swan ◽  
Julian Di Stefano ◽  
Trent D. Penman
Keyword(s):  
Species Richness ◽  
Spatial Scales ◽  
Landscape Patterns ◽  
Mammal Species ◽  
Multiple Spatial Scales

scholarly journals Comparison of MODIS and SWAT evapotranspiration over a complex terrain at different spatial scales

2018 ◽  
Vol 22 (5) ◽  
pp. 2775-2794 ◽  
Author(s):  
Olanrewaju O. Abiodun ◽  
Huade Guan ◽  
Vincent E. A. Post ◽  
Okke Batelaan
Keyword(s):  
Complex Terrain ◽  
Spatial Scales ◽  
Swat Model ◽  
South Australia ◽  
Remotely Sensed ◽  
Distributed Model ◽  
Climate Data ◽  
Remotely Sensed Data ◽  
Catchment Scale ◽  
Distributed Hydrological Models

Abstract. In most hydrological systems, evapotranspiration (ET) and precipitation are the largest components of the water balance, which are difficult to estimate, particularly over complex terrain. In recent decades, the advent of remotely sensed data based ET algorithms and distributed hydrological models has provided improved spatially upscaled ET estimates. However, information on the performance of these methods at various spatial scales is limited. This study compares the ET from the MODIS remotely sensed ET dataset (MOD16) with the ET estimates from a SWAT hydrological model on graduated spatial scales for the complex terrain of the Sixth Creek Catchment of the Western Mount Lofty Ranges, South Australia. ET from both models was further compared with the coarser-resolution AWRA-L model at catchment scale. The SWAT model analyses are performed on daily timescales with a 6-year calibration period (2000–2005) and 7-year validation period (2007–2013). Differences in ET estimation between the SWAT and MOD16 methods of up to 31, 19, 15, 11 and 9 % were observed at respectively 1, 4, 9, 16 and 25 km2 spatial resolutions. Based on the results of the study, a spatial scale of confidence of 4 km2 for catchment-scale evapotranspiration is suggested in complex terrain. Land cover differences, HRU parameterisation in AWRA-L and catchment-scale averaging of input climate data in the SWAT semi-distributed model were identified as the principal sources of weaker correlations at higher spatial resolution.

scholarly journals Towards a general framework for predicting threat status of data-deficient species from phylogenetic, spatial and environmental information

2015 ◽  
Vol 370 (1662) ◽  
pp. 20140016 ◽  
Author(s):  
Walter Jetz ◽  
Robert P. Freckleton
Keyword(s):  
Remotely Sensed ◽  
The Philippines ◽  
Remotely Sensed Data ◽  
Mammal Species ◽  
Statistical Framework ◽  
Threat Status ◽  
Small Set ◽  
Future Potential ◽  
Data Deficient ◽  
Conservation Concern

In taxon-wide assessments of threat status many species remain not included owing to lack of data. Here, we present a novel spatial-phylogenetic statistical framework that uses a small set of readily available or derivable characteristics, including phylogenetically imputed body mass and remotely sensed human encroachment, to provide initial baseline predictions of threat status for data-deficient species. Applied to assessed mammal species worldwide, the approach effectively identifies threatened species and predicts the geographical variation in threat. For the 483 data-deficient species, the models predict highly elevated threat, with 69% ‘at-risk’ species in this set, compared with 22% among assessed species. This results in 331 additional potentially threatened mammals, with elevated conservation importance in rodents, bats and shrews, and countries like Colombia, Sulawesi and the Philippines. These findings demonstrate the future potential for combining phylogenies and remotely sensed data with species distributions to identify species and regions of conservation concern.

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