Using patch and landscape variables to model bird abundance in a naturally heterogeneous landscape

2003 ◽  
Vol 81 (3) ◽  
pp. 441-452 ◽  
Author(s):  
Gaea E Crozier ◽  
Gerald J Niemi
Keyword(s):  
Spatial Scales ◽  
Remote Sensing Data ◽  
Landscape Composition ◽  
Training Data ◽  
Aerial Photographs ◽  
Habitat Patch ◽  
Bird Abundance ◽  
Landscape Characteristics ◽  
Heterogeneous Landscape ◽  
Study Sites

Regression models were developed to predict relative bird abundance in a naturally heterogeneous landscape using patch and landscape spatial scales. Breeding birds were surveyed with point counts on 140 study sites in 1997 and 1998. Aerial photographs were digitized to obtain habitat patch information, such as area, shape, and edge contrast. Classified remote-sensing data were gathered to provide information on landscape composition and configuration within a 1-km2 area around the study sites. Stepwise multiple linear regression was used to develop 40 species-specific models within specific habitat types using patch and landscape characteristics. In 38 out of the 40 models, area of the habitat patch was first selected as the most important predictor of relative bird abundance. Variables related to the landscape were retained in 6 of the 40 models. In this naturally heterogeneous region, the landscape surrounding the patch contributed little to explaining relative bird abundance. The models were evaluated by examining how well they predicted relative bird abundance in a test set not included in the original analyses. The results of the test data were reasonable: >79% of the test observations were within the prediction intervals established by the training data.

Evaluating landscape characteristics of predicted hotspots for plant invasions

2020 ◽  
Vol 13 (3) ◽  
pp. 163-175
Author(s):  
Adrián Lázaro-Lobo ◽  
Kristine O. Evans ◽  
Gary N. Ervin
Keyword(s):  
Land Use ◽  
Invasive Species ◽  
Propagule Pressure ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Plant Invasions ◽  
Landscape Composition ◽  
Environmental Predictors ◽  
Distribution Models ◽  
Landscape Characteristics

AbstractInvasive species are widely recognized as a major threat to global diversity and an important factor associated with global change. Species distribution models (SDMs) have been widely applied to determine the range that invasive species could potentially occupy, but most examples focus on predictive variables at a single spatial scale. In this study, we simultaneously considered a broad range of variables related to climate, topography, land cover, land use, and propagule pressure to predict what areas in the southeastern United States are more susceptible to invasion by 45 invasive terrestrial plant species. Using expert-verified occurrence points from EDDMapS, we modeled invasion susceptibility at 30-m resolution for each species using a maximum entropy (MaxEnt) modeling approach. We then analyzed how environmental predictors affected susceptibility to invasion at different spatial scales. Climatic and land-use variables, especially minimum temperature of coldest month and distance to developed areas, were good predictors of landscape susceptibility to invasion. For most of the species tested, human-disturbed systems such as developed areas and barren lands were more prone to be invaded than areas that experienced minimal human interference. As expected, we found that landscape heterogeneity and the presence of corridors for propagule dispersal significantly increased landscape susceptibility to invasion for most species. However, we also found a number of species for which the susceptibility to invasion increased in landscapes with large core areas and/or less-aggregated patches. These exceptions suggest that even though we found the expected general patterns for susceptibility to invasion among most species, the influence of landscape composition and configuration on invasion risk is species specific.

scholarly journals Imaging Spectroscopy for Conservation Applications

2021 ◽  
Vol 13 (2) ◽  
pp. 292
Author(s):  
Megan Seeley ◽  
Gregory P. Asner
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Case Studies ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Remote Sensing Data ◽  
Decision Makers ◽  
High Spectral Resolution ◽  
Conservation Areas ◽  
Broad Array

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

Western white pine development in relation to biophysical characteristics across different spatial scales in the Coeur d'Alene River basin in northern Idaho, U.S.A.

2002 ◽  
Vol 32 (7) ◽  
pp. 1109-1125 ◽  
Author(s):  
Theresa B Jain ◽  
Russell T Graham ◽  
Penelope Morgan
Keyword(s):  
River Basin ◽  
Spatial Scales ◽  
Pinus Monticola ◽  
White Pine ◽  
Basal Diameter ◽  
Landscape Characteristics ◽  
Coeur D'alene ◽  
Biophysical Factors ◽  
Western White Pine ◽  
Northern Idaho

Many studies have assessed tree development beneath canopies in forest ecosystems, but results are seldom placed within the context of broad-scale biophysical factors. Mapped landscape characteristics for three watersheds, located within the Coeur d'Alene River basin in northern Idaho, were integrated to create a spatial hierarchy reflecting biophysical factors that influence western white pine (Pinus monticola Dougl. ex D. Don) development under a range of canopy openings. The hierarchy included canopy opening, landtype, geological feature, and weathering. Interactions and individual-scale contributions were identified using stepwise log–linear regression. The resulting models explained 68% of the variation for estimating western white pine basal diameter and 64% for estimating height. Interactions among spatial scales explained up to 13% of this variation and better described vegetation response than any single spatial scale. A hierarchical approach based on biophysical attributes is an excellent method for studying plant and environment interactions.

scholarly journals Remotely Sensed Single Tree Data Enable the Determination of Habitat Thresholds for the Three-Toed Woodpecker (Picoides tridactylus)

2018 ◽  
Vol 10 (12) ◽  
pp. 1972 ◽  
Author(s):  
Katarzyna Zielewska-Büttner ◽  
Marco Heurich ◽  
Jörg Müller ◽  
Veronika Braunisch
Keyword(s):  
Bark Beetles ◽  
Laser Scanning ◽  
Positive Relationship ◽  
Spatial Scales ◽  
Keystone Species ◽  
Remote Sensing Data ◽  
Individual Tree ◽  
Substrate Quality ◽  
Dead Trees ◽  
Negative Effect

Forest biodiversity conservation requires precise, area-wide information on the abundance and distribution of key habitat structures at multiple spatial scales. We combined airborne laser scanning (ALS) data with color-infrared (CIR) aerial imagery for identifying individual tree characteristics and quantifying multi-scale habitat requirements using the example of the three-toed woodpecker (Picoides tridactylus) (TTW) in the Bavarian Forest National Park (Germany). This bird, a keystone species of boreal and mountainous forests, is highly reliant on bark beetles dwelling in dead or dying trees. While previous studies showed a positive relationship between the TTW presence and the amount of deadwood as a limiting resource, we hypothesized a unimodal response with a negative effect of very high deadwood amounts and tested for effects of substrate quality. Based on 104 woodpecker presence or absence locations, habitat selection was modelled at four spatial scales reflecting different woodpecker home range sizes. The abundance of standing dead trees was the most important predictor, with an increase in the probability of TTW occurrence up to a threshold of 44–50 dead trees per hectare, followed by a decrease in the probability of occurrence. A positive relationship with the deadwood crown size indicated the importance of fresh deadwood. Remote sensing data allowed both an area-wide prediction of species occurrence and the derivation of ecological threshold values for deadwood quality and quantity for more informed conservation management.

scholarly journals Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

2019 ◽  
Vol 11 (22) ◽  
pp. 2603
Author(s):  
George Xian ◽  
Hua Shi ◽  
Cody Anderson ◽  
Zhuoting Wu
Keyword(s):  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Regression Tree ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Landsat 8 ◽  
Signal To Noise ◽  
Study Results ◽  
The Impact

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

scholarly journals MODEL FOR LAND COVER ESTIMATION USING UNSUPERVISED MACHINE LEARNING ON GOOGLE MAPS COLOR IMAGES

2018 ◽  
Vol XLII-5 ◽  
pp. 147-154
Author(s):  
R. S. Bhowmick ◽  
A. Kumar ◽  
G. D. Singh ◽  
S. Kumar
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Pixel Classification ◽  
Unsupervised Machine Learning ◽  
Landscape Characteristics ◽  
Area Of Interest ◽  
Proposed Model ◽  
Agricultural Areas ◽  
Hyper Text Transfer Protocol

<p><strong>Abstract.</strong> Remote sensing data and satellite images are broadly used for land cover information. There are so many challenges to classify pixels on the basis of features and characteristics. Generally it is pixel classification that required the count of pixels for certain area of interest. In the proposed model, we are applying unsupervised machine learning to classify the content of the input images on the basis of pixels intensity. The study aims to compare classification accuracy of different landscape characteristics like water, forest, urban, agricultural areas, transport network and other classes adapted from CORINE (Coordination of information on the environment) nomenclature. To fulfil the aim of the model, accessing data from Google map using Google static API service which creates a map based on URL parameters sent through a standard HTTP (Hyper Text Transfer Protocol) request and returns the map as an image which can be display on any graphical user interface platform. The Google Static Maps API returns an image either in GIF, PNG or JPEG format in response to an HTTP request. To identify different land cover/use classes using k-means clustering. The model is dynamic in nature that describes the clustering as well formulate the area of the concerned class or clustered fields.</p>

scholarly journals Ungulates rely less on visual cues, but more on adapting movement behaviour, when searching for forage

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3178 ◽  
Author(s):  
Jan A. Venter ◽  
Herbert H.T. Prins ◽  
Alla Mashanova ◽  
Rob Slotow
Keyword(s):  
Visual Cues ◽  
A Priori ◽  
Step Length ◽  
Habitat Patch ◽  
Movement Behaviour ◽  
Large Herbivores ◽  
Heterogeneous Landscape ◽  
Equus Burchelli ◽  
Scale Step ◽  
Patch Scale

Finding suitable forage patches in a heterogeneous landscape, where patches change dynamically both spatially and temporally could be challenging to large herbivores, especially if they have noa prioriknowledge of the location of the patches. We tested whether three large grazing herbivores with a variety of different traits improve their efficiency when foraging at a heterogeneous habitat patch scale by using visual cues to gaina prioriknowledge about potential higher value foraging patches. For each species (zebra (Equus burchelli), red hartebeest (Alcelaphus buselaphussubspeciescamaa) and eland (Tragelaphus oryx)), we used step lengths and directionality of movement to infer whether they were using visual cues to find suitable forage patches at a habitat patch scale. Step lengths were significantly longer for all species when moving to non-visible patches than to visible patches, but all movements showed little directionality. Of the three species, zebra movements were the most directional. Red hartebeest had the shortest step lengths and zebra the longest. We conclude that these large grazing herbivores may not exclusively use visual cues when foraging at a habitat patch scale, but would rather adapt their movement behaviour, mainly step length, to the heterogeneity of the specific landscape.

scholarly journals Assessing volumetric change distributions and scaling relations of retrogressive thaw slumps across the Arctic

2022 ◽  
Vol 16 (1) ◽  
pp. 1-15
Author(s):  
Philipp Bernhard ◽  
Simon Zwieback ◽  
Nora Bergner ◽  
Irena Hajnsek
Keyword(s):  
Regional Differences ◽  
Scaling Laws ◽  
Remote Sensing Data ◽  
High Arctic ◽  
The Arctic ◽  
Scaling Relations ◽  
Volumetric Change ◽  
Inverse Gamma ◽  
Time Period ◽  
Study Sites

Abstract. Arctic ice-rich permafrost is becoming increasingly vulnerable to terrain-altering thermokarst, and among the most rapid and dramatic of these changes are retrogressive thaw slumps (RTSs). They initiate when ice-rich soils are exposed and thaw, leading to the formation of a steep headwall which retreats during the summer months. The impacts and the distribution and scaling laws governing RTS changes within and between regions are unknown. Using TanDEM-X-derived digital elevation models, we estimated RTS volume and area changes over a 5-year time period from winter 2011/12 to winter 2016/17 and used for the first time probability density functions to describe their distributions. We found that over this time period all 1853 RTSs mobilized a combined volume of 17×106 m3 yr−1, corresponding to a volumetric change density of 77 m3 yr−1 km−2. Our remote sensing data reveal inter-regional differences in mobilized volumes, scaling laws, and terrain controls. The distributions of RTS area and volumetric change rates follow an inverse gamma function with a distinct peak and an exponential decrease for the largest RTSs. We found that the distributions in the high Arctic are shifted towards larger values than at other study sites We observed that the area-to-volume scaling was well described by a power law with an exponent of 1.15 across all study sites; however the individual sites had scaling exponents ranging from 1.05 to 1.37, indicating that regional characteristics need to be taken into account when estimating RTS volumetric changes from area changes. Among the terrain controls on RTS distributions that we examined, which included slope, adjacency to waterbodies, and aspect, the latter showed the greatest but regionally variable association with RTS occurrence. Accounting for the observed regional differences in volumetric change distributions, scaling relations, and terrain controls may enhance the modelling and monitoring of Arctic carbon, nutrient, and sediment cycles.

scholarly journals MODIFIED OPTIMIZATION WATER INDEX (MOWI) FOR LANDSAT-8 OLI/TIRS

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 185-190 ◽  
Author(s):  
M. Moradi ◽  
M. Sahebi ◽  
M. Shokri
Keyword(s):  
Water Resources ◽  
Spatial Scales ◽  
Landsat Imagery ◽  
Human Life ◽  
Spectral Band ◽  
Remote Sensing Data ◽  
Particle Swarm Algorithm ◽  
Landsat 8 ◽  
Water Index ◽  
Water Detection

Water is one of the most important resources that essential need for human life. Due to population growth and increasing need of human to water, proper management of water resources will be one of the serious challenges of next decades. Remote sensing data is the best way to the management of water resources due time and cost effectiveness over a greater range of temporal and spatial scales. Between many kinds of satellite data, from SAR to optic or from high resolution to low resolution, Landsat imagery is more interesting data for water detection and management of earth surface water. Landsat8 OLI/TIRS is the newest version of Landsat satellite series. In this paper, we investigated the full spectral potential of Landsat8 for water detection. It is developed many kinds of methods for this purpose that index based methods have some advantages than other methods. Pervious indices just use a limited number of spectral band. In this paper, Modified Optimization Water Index (MOWI) defined by consideration of a linear combination of bands that each coefficient of bands calculated by particle swarm algorithm. The result shows that modified optimization water index (MOWI) has a proper performance on different condition like cloud, cloud shadow and mountain shadow.

scholarly journals The effect of local land-use changes on floristic diversity during the past 1000 years in southern Sweden

The Holocene ◽  
2016 ◽  
Vol 27 (5) ◽  
pp. 694-711 ◽  
Author(s):  
Daniel Fredh ◽  
Florence Mazier ◽  
Petra Bragée ◽  
Per Lagerås ◽  
Mats Rundgren ◽  
...  
Keyword(s):  
Land Use ◽  
Time Windows ◽  
Land Use Changes ◽  
Management Strategies ◽  
Floristic Diversity ◽  
Southern Sweden ◽  
13Th Century ◽  
Land Use Pattern ◽  
Landscape Characteristics ◽  
Heterogeneous Landscape

The relationship between land-use and floristic diversity in the landscape, for the last millennia, is analysed from two small lakes in southern Sweden. Pollen analysis and the Local Vegetation Estimates (LOVE) model are used to quantify land-cover at local scales with 100-year time windows. Floristic richness is estimated using palynological richness, and we introduce LOVE-based evenness as a proxy for floristic evenness on a local scale based on the LOVE output. The results reveal a dynamic land-use pattern, with agricultural expansion during the 13th century, a partly abandoned landscape around AD 1400, re-establishment during the 15th–17th centuries and a transition from traditional to modern land-use during the 20th century. We suggest that the more heterogeneous landscape and the more dynamic land-use during the 13th–19th centuries were of substantial importance for achieving the high floristic diversity that characterises the traditional landscape. Pollen-based studies of this type are helpful in identifying landscape characteristics and land-use practices that are important for floristic diversity and may therefore guide the development of ecosystem management strategies aiming at mitigating the on-going loss of species seen in the landscape of southern Sweden and many other regions worldwide.

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