Contribution of environmental factors to temperature distribution at different resolution levels on the forefield of the Loven Glaciers, Svalbard

ABSTRACTThe climate and its components (temperature and precipitation) are organised according to different spatial scales that are structured hierarchically. The aim of this paper is to explore the dependence between temperature and deterministic factors at different scales on a 10 km2 study area on the northwestern coast of Svalbard. A GIS was developed which contained three sources of information: temperature, remotely sensed imagery and digital elevation models (DEM), and derived raster data layers. The first layer, temperatures, was acquired at regularly observed temporal intervals from 53 stations. The second layer comprised remotely sensed images (aerial photography and SPOT imagery) and DEM data at 2 m and 20 m resolution, respectively. From these, a windowing procedure was applied to derive several spatial subsets of different spatial resolutions (6, 14, 30, 60, 140, and 300 m). The third layer comprised slope, aspect, and a theoretical solar radiation value derived from the DEM, and a vegetation index derived from the remotely sensed imagery. Linear regressions were then systematically conducted on the datasets, with temperature as the dependent variable, and each of the other data layers as the independent variables. By using graphical analysis, we link the correlation coefficients obtained for each factor, from the smallest spatial resolution (6 m) to the largest resolution (300 m). The results indicated that each explanatory variable and scale brings a specific contribution to changes in temperature. For example, the effect of elevation remains constant for all spatial resolutions, reflecting a quasi ‘non-scalar’ pattern of this variable. For other variables however, the effect of spatial scale can have a strong effect. In the case of solar radiation, a maximum of explanation was obtained for spatial resolutions of 14 m and 60 m; for vegetation index the optimum contribution was related to the 300 m resolution. Thus, different environment characteristics may have significant effects on changes in temperature when differences in spatial scale are taken into account.

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Multi-scale selection models predict breeding habitat for two Arctic-breeding raptor species

Arctic Science ◽

10.1139/as-2018-0026 ◽

2020 ◽

Vol 6 (1) ◽

pp. 24-40

Author(s):

Philippe Galipeau ◽

Alastair Franke ◽

Mathieu Leblond ◽

Joel Bêty

Keyword(s):

Habitat Selection ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spatial Scales ◽

Environmental Indicators ◽

Breeding Habitat ◽

Slope Aspect ◽

Peregrine Falcon ◽

Apex Predators ◽

Selection Models

Raptors are important environmental indicators because they are apex predators and can be sensitive to disturbance. Few studies have addressed habitat preferences of tundra-nesting raptors, and those that exist have focused on fine-scale characteristics. With increasing economic development predicted to occur throughout the Canadian Arctic, the investigation of raptor breeding habitat at broad spatial scales is required. We modeled breeding habitat selection for two raptor species on north Baffin Island, NU, Canada. During aerial surveys conducted over six breeding seasons, we documented 172 peregrine falcon (Falco peregrinus tundrius) and 160 rough-legged hawk (Buteo lagopus) nesting sites. We used these locations in conjunction with remote sensing data to build habitat selection models at three spatial scales. Topography, distance to water, and normalized difference vegetation index explained selection at all scales; slope aspect was also important at the finest scale. To validate landscape scale models, we conducted a validation survey that resulted in the detection of 45 new nests (peregrine falcon n = 21, rough-legged hawk n = 24). We did not detect any new nests in areas where model-predicted occurrence was expected to be low. Conversely, we found more than half of previously undetected nests in areas where model-predicted occurrence was expected to be high.

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Inter-Comparison of Normalized Difference Vegetation Index Measured from Different Footprint Sizes in Cropland

Remote Sensing ◽

10.3390/rs12182980 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2980

Author(s):

Jae-Hyun Ryu ◽

Sang-Il Na ◽

Jaeil Cho

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Spatial Scales ◽

Correlation Coefficients ◽

The Other ◽

Terrestrial Vegetation ◽

Pixel Size ◽

Spectrometer Data ◽

Sentinel 2

Remote sensing techniques using visible and near-infrared wavelengths are useful for monitoring terrestrial vegetation. The normalized difference vegetation index (NDVI) is a widely used proxy of vegetation conditions, and it has been measured at various footprint sizes using satellite, unmanned aerial vehicle (UAV), and ground-installed sensors. The goal of this study was to analyze the spatial characteristics of NDVI data by comparing the values obtained at different footprint sizes. In particular, the NDVI was evaluated in garlic and onion fields that featured ridges and furrows. The evaluation was performed using data from a leaf spectrometer, field spectrometers, ground-installed spectral reflectance sensors, a multispectral camera onboard a UAV, and Sentinel-2 satellites. The correlation coefficients between NDVIs evaluated from the various sensors (excluding the satellite-mounted sensors) ranged from 0.628 to 0.944. The UAV-based NDVI (NDVIUAV) exhibited the lowest root mean square error (RMSE = 0.088) when compared with field spectrometer data. On the other hand, the satellite-based NDVI data (NDVISentinel-2) were poorly correlated with those obtained from the other sensors as a result of the footprint mismatch. However, by upscaling the NDVIUAV data to the pixel size of Sentinel-2, the comparison was improved, and the following statistics were obtained: correlation coefficient: 0.504–0.785; absolute bias: 0.048–0.078; RMSE: 0.063–0.094. According to the aforementioned results, ground-based NDVI data can be used to validate NDVIUAV data without further processing and NDVIUAV data can be used to validate NDVISentinel-2 data after upscaling to the Sentinel-2 pixel size. Overall, the results presented in this study may be helpful to understand and integrate NDVI data at different spatial scales.

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Application of Remotely Sensed Imagery and Socioeconomic Surveys to Map Crop Choices in the Bekaa Valley (Lebanon)

Agriculture ◽

10.3390/agriculture9030057 ◽

2019 ◽

Vol 9 (3) ◽

pp. 57 ◽

Cited By ~ 3

Author(s):

Arnaud Caiserman ◽

Dominique Dumas ◽

Karine Bennafla ◽

Ghaleb Faour ◽

Farshad Amiraslani

Keyword(s):

Euclidean Distance ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remotely Sensed ◽

Strongly Correlated ◽

Remotely Sensed Imagery ◽

Crop Type ◽

Socioeconomic Data ◽

Sentinel 2 ◽

Reference Fields

Based on remotely sensed imagery and socioeconomic data, this research analyzes the reasons why farmers choose one crop over another in the Bekaa Valley in Lebanon. This study mapped the area of the cultivated crop in 2017 with Sentinel-2 images. An accurate and new method was developed to extract the field boundaries from the evolution of the normalized difference vegetation index (NDVI) profile throughout the season. We collected 386 GPS locations for fields that are used for crop cultivation, from which the NDVI profile was extracted. The 386 reference fields were separated into two groups: reference locations and test locations. The Euclidean distance (ED) was calculated between these two groups, and the classification was strongly correlated to the known crop type in the field (overall accuracy: 90%). Our study area cultivated wheat (32%), spring potatoes (25%), spring vegetables (27%), orchards (11%), vineyards (7%), and alfalfa (<1%). Socioeconomic surveys showed that farmers favored these crops over others on account of their profitability. Nonetheless, the surveys highlighted a paradox: despite the lack of a political frame for agriculture in Lebanon, farmers’ crop choices strongly depend on a few existing policies.

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The spatial dynamics of wheat yield and protein content at the field scale

10.31223/x5691r ◽

2021 ◽

Author(s):

Paul Stoy ◽

Anam Khan ◽

Aaron Wipf ◽

Nick Silverman ◽

Scott Powell

Keyword(s):

Protein Content ◽

Spatial Scale ◽

Information Content ◽

Vegetation Index ◽

Management Practices ◽

Spatial Scales ◽

Wheat Yield ◽

Future Research ◽

Field Scale ◽

Leibler Divergence

Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to predict their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA and tested the ability of normalized difference vegetation index (NDVI) measurements from an unpiloted aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to wheat measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was ‘averaged out’ at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact yield and GPC.

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The relationship between effective plant area index and Landsat spectral response across elevation, solar insolation, and spatial scales in a northern Idaho forest

Canadian Journal of Forest Research ◽

10.1139/x03-215 ◽

2004 ◽

Vol 34 (2) ◽

pp. 465-480 ◽

Cited By ~ 28

Author(s):

Amy L Pocewicz ◽

Paul Gessler ◽

Andrew P Robinson

Keyword(s):

Spatial Scale ◽

Complex Terrain ◽

Vegetation Index ◽

Remotely Sensed ◽

Solar Insolation ◽

Area Index ◽

Plant Area Index ◽

Plant Area ◽

Northern Idaho ◽

The Relationship

Leaf area index (LAI) is an important forest characteristic related to photosynthesis and carbon sequestration, and gains in efficiency for LAI measurements are possible using remotely sensed imagery. However, the potential effects of complex topography on this measurement system are not well understood. Our objective was to understand how complex terrain and measurement aggregation influence the relationship between LAI and remotely sensed vegetation indices across a mountainous conifer forest. We identified NDVIc, a middle-infrared (MIR) correction to NDVI (Normalized Difference Vegetation Index), as the vegetation index providing the best prediction of effective plant area index (PAIe), used to approximate LAI. We tested formal hypotheses to identify how elevation and solar insolation gradients and spatial scale of measurement aggregation affected the PAIeNDVIc relationship and found that it changed across elevation at one spatial scale. Comparisons of NDVIc with NDVI revealed that vegetation index choice is important in complex terrain, and we concluded that the MIR correction improves the PAIeNDVI relationship by explaining variation related to solar insolation. Our results suggest that NDVIc calculated from Landsat ETM+ provides a practical estimate of PAIe across our northern Idaho study area and potentially other conifer forests in complex terrain.

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Spatial Scale Interactions and Image Statistics

Perception ◽

10.1068/p261089 ◽

1997 ◽

Vol 26 (9) ◽

pp. 1089-1100 ◽

Cited By ~ 12

Author(s):

Nuala Brady

Keyword(s):

Visual System ◽

Spatial Scale ◽

Visual Processing ◽

Spatial Scales ◽

Power Spectra ◽

Visual Image ◽

Natural Scenes ◽

Sources Of Information ◽

Scale Invariant ◽

Scale Interactions

In natural scenes and other broadband images, spatial variations in luminance occur at a range of scales or frequencies. It is generally agreed that the visual image is initially represented by the activity of separate frequency-tuned channels, and this notion is supported by physiological evidence for a stage of multi-resolution filtering in early visual processing. The question whether these channels can be accessed as independent sources of information in the normal course of events is a more contentious one. In the psychophysical study of both motion and spatial vision, there are examples of tasks in which fine-scale structure dominates perception or performance and obscures information at coarser scales. It is argued here that one important factor determining the relative salience of information from different spatial scales in broadband images is the distribution of response activity across spatial channels. The special case of natural scenes that have characteristic ‘scale-invariant’ power spectra in which image contrast is roughly constant in equal octave frequency bands is considered. A review is presented of evidence which suggests that the sensitivity of frequency-tuned filters in the visual system is matched to this image statistic, so that, on average, different channels respond with equal activity to natural scenes. Under these conditions, the visual system does appear to have independent access to information at different spatial scales and spatial scale interactions are not apparent.

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Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China

Remote Sensing ◽

10.3390/rs11192223 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2223 ◽

Cited By ~ 1

Author(s):

Jiansheng Wu ◽

Jingtian Liang ◽

Liguo Zhou ◽

Fei Yao ◽

Jian Peng

Keyword(s):

High Resolution ◽

Spatial Scale ◽

Spatial Scales ◽

Correlation Coefficients ◽

Shenzhen City ◽

Medium Resolution ◽

Before And After ◽

Autumn And Winter ◽

The Relationship

Satellite-derived aerosol optical depth (AOD) is widely used to estimate surface PM2.5 concentrations. Most AOD products have relatively low spatial resolutions (i.e., ≥1 km). Consequently, insufficient research exists on the relationship between high-resolution (i.e., <1 km) AOD and PM2.5 concentrations. Taking Shenzhen City, China as the study area, we derived AOD at the 16-m spatial resolution for the period 2015–2017 based on Gaofen-1 (GF-1) satellite images and the Dark Target (DT) algorithm. Then, we extracted AOD at spatial scales ranging from 40 m to 5000 m and applied vertical and humidity corrections. We analyzed the correlation between AOD and PM2.5 concentrations, and the impacts of AOD correction and spatial scale on the correlation. It was found that the DT-derived GF-1 AOD at different spatial scales had statistically significant correlations with surface PM2.5 concentrations, and the AOD corrections strengthened the correlations. The correlation coefficients (R) between AOD at different spatial scales and PM2.5 concentrations were 0.234–0.329 and 0.340–0.423 before and after AOD corrections, respectively. In spring, summer, autumn, and winter, PM2.5 concentrations had the best correlations with humidity-corrected AOD, uncorrected AOD, vertical and humidity-corrected AOD, and uncorrected AOD, respectively, indicating a distinct seasonal variation of the aerosol characteristics. At spatial scales of 1–5 km, AOD at finer spatial scales generally had higher correlations with PM2.5 concentrations. However, at spatial scales <1 km, the correlations fluctuated irregularly, which could be attributed to scale mismatches between AOD and PM2.5 measurements. Thus, 1 km appears to be the optimum spatial scale for DT-derived AOD to maximize the correlation with PM2.5 concentrations. It is also recommended to aggregate very high-resolution DT-derived AOD to an appropriate medium resolution (e.g., 1 km) before matching them with in situ PM2.5 measurements in regional air pollution studies.

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Quantitative and Qualitative Approaches of Delineation in Detailed Mapping of Vineyard Landscape. Case Study: Vicinity of Pezinok (Slovakia)

Ekológia (Bratislava) ◽

10.1515/eko-2016-0019 ◽

2016 ◽

Vol 35 (3) ◽

pp. 240-252 ◽

Cited By ~ 3

Author(s):

Marián Gábor ◽

Vladimír Falťan ◽

František Petrovič

Keyword(s):

Solar Radiation ◽

Environmental Planning ◽

Vegetation Index ◽

Slope Aspect ◽

Qualitative And Quantitative ◽

Qualitative Approaches ◽

Quantitative Characteristics ◽

Landscape Units ◽

Base Elevation

AbstractThe main goal of this paper is the application of qualitative and quantitative free available data for geographical delineation based on reconnaissance research in vineyard landscape. The results of delineation are useful in agricultural management or environmental planning. Our delineation may serve as the basic information on site conditions of vineyards near Pezinok (Slovakia), with historical use from the beginning of 13th century. We have studied the actual land cover and classified physiotopes of the study area into a set of relatively homogenous and coherent landscape units. The landscape units defined in this work consist of homogenous physiotopes in terms of their structural and functional characteristics, which have been shaped by natural factors (land-forms, soil type and subtype, geological base, elevation, slope, aspect, solar radiation and normal different vegetation index (NDVI)). The characteristics were used to define 23 landscape units in qualitative delineation (based on both qualitative and quantitative data). Only quantitative characteristics – elevation, aspect, slope, solar radiation and NDVI, were used in a K-means cluster analysis to define the 17 landscape units. The number of landscape units was computed by WB-index, and standardisation of data was computed by factor analysis. The whole classification process was statistically significant. The strength of the grouping procedure was tested by using Discriminant Analysis, which found that 92.70% of objects in qualitative and 98.50% of objects in quantitative delineation were correctly classified.

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