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.

scholarly journals Using Deliberative Democracy for Better Urban Decision-Making through Integrative Thinking

Urban Science ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 3
Author(s):  
Janette Hartz-Karp ◽  
Dora Marinova
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Deliberative Democracy ◽  
Decision Makers ◽  
Capital City ◽  
City Region ◽  
Multiple Sources ◽  
Trade Offs ◽  
Urban Issues ◽  
Sustainability Challenges

This article expands the evidence about integrative thinking by analyzing two case studies that applied the collaborative decision-making method of deliberative democracy which encourages representative, deliberative and influential public participation. The four-year case studies took place in Western Australia, (1) in the capital city Perth and surrounds, and (2) in the city-region of Greater Geraldton. Both aimed at resolving complex and wicked urban sustainability challenges as they arose. The analysis suggests that a new way of thinking, namely integrative thinking, emerged during the deliberations to produce operative outcomes for decision-makers. Building on theory and research demonstrating that deliberative designs lead to improved reasoning about complex issues, the two case studies show that through discourse based on deliberative norms, participants developed different mindsets, remaining open-minded, intuitive and representative of ordinary people’s basic common sense. This spontaneous appearance of integrative thinking enabled sound decision-making about complex and wicked sustainability-related urban issues. In both case studies, the participants exhibited all characteristics of integrative thinking to produce outcomes for decision-makers: salience—grasping the problems’ multiple aspects; causality—identifying multiple sources of impacts; sequencing—keeping the whole in view while focusing on specific aspects; and resolution—discovering novel ways that avoided bad choice trade-offs.

scholarly journals Model-driven development platform selection: four industry case studies

2021 ◽  
Author(s):  
Siamak Farshidi ◽  
Slinger Jansen ◽  
Sven Fortuin
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Decision Makers ◽  
Development Activity ◽  
Model Driven Development ◽  
Domain Experts ◽  
Theoretical Contribution ◽  
Model Driven ◽  
Development Platform ◽  
Software Production

AbstractModel-driven development platforms shift the focus of software development activity from coding to modeling for enterprises. A significant number of such platforms are available in the market. Selecting the best fitting platform is challenging, as domain experts are not typically model-driven deployment platform experts and have limited time for acquiring the needed knowledge. We model the problem as a multi-criteria decision-making problem and capture knowledge systematically about the features and qualities of 30 alternative platforms. Through four industry case studies, we confirm that the model supports decision-makers with the selection problem by reducing the time and cost of the decision-making process and by providing a richer list of options than the enterprises considered initially. We show that having decision knowledge readily available supports decision-makers in making more rational, efficient, and effective decisions. The study’s theoretical contribution is the observation that the decision framework provides a reliable approach for creating decision models in software production.

scholarly journals Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

2014 ◽  
Vol 369 (1643) ◽  
pp. 20130194 ◽  
Author(s):  
Michael D. Madritch ◽  
Clayton C. Kingdon ◽  
Aditya Singh ◽  
Karen E. Mock ◽  
Richard L. Lindroth ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Populus Tremuloides ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Spectral Signature ◽  
Spectral Variation ◽  
Fine Scale ◽  
Below Ground ◽  
Landscape Scales ◽  
Spectrometer Data

Fine-scale biodiversity is increasingly recognized as important to ecosystem-level processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.

scholarly journals Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

2014 ◽  
Vol 11 (23) ◽  
pp. 6827-6840 ◽  
Author(s):  
M. Réjou-Méchain ◽  
H. C. Muller-Landau ◽  
M. Detto ◽  
S. C. Thomas ◽  
T. Le Toan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Spatial Scales ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Forest Degradation ◽  
Forest Carbon ◽  
Mitigation Strategies ◽  
Large Plot

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

scholarly journals RS & GIS-Based Spatialtemporal Analysis of Ecological Footprint and Biocapacity Pattern of Jinghe River Watershed in China: Does Supply Meet Demand?

2011 ◽  
Vol 356-360 ◽  
pp. 2820-2832
Author(s):  
Dong Xia Yue ◽  
Jin Hui Ma ◽  
Jian Jun Guo ◽  
Jia Jing Zhang ◽  
Jun Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Ecological Footprint ◽  
Spatial Scales ◽  
Supply And Demand ◽  
Remote Sensing Data ◽  
Spatiotemporal Dynamics ◽  
River Watershed ◽  
Per Capita ◽  
Jinghe River

The Ecological Footprint methodology is a framework that tracks Ecological Footprint (humanity’s demands on the biosphere) by comparing human demand against the regenerative capacity (Biocapacity) of the planet (WWF, 2010) to advance the science of sustainability. As such, the spatiotemporal dynamics of the Ecological Footprint (EF) and Biocapacity (BC) in a given watershed are important topics in the field of sustainability research based on remote sensing (RS) data and geographic information system (GIS) techniques．This paper reports on a case study of the Jinghe River Watershed using improved EF methodology with the help of GIS and high resolution remote sensing data, to quantitatively estimate the relationship between EF demand and BC supply and analyze their spatial distribution patterns at multiple spatial scales for four periods (1986, 1995, 2000 and 2008). We predict the future BC both overall, and of six categories of biological productivity area for the next four decades using the Markov Chain Method．The results showed that the spatial distribution of EF demand and BC supply were significantly uneven in the region, in which the per-capita EF of all counties located in the watershed increased continually from 1986 to 2008, and the EF per person of counties in the middle and lower reaches area was markedly greater than that in the upper reaches over time. On the supply side, the per-capita BC of all counties decreased gradually from 1986 to 2008, and the per-capita BC of counties in the upper reaches area was greater than that in the middle and lower reaches during the period, causing the uneven spatial distribution of Ecological budget-the gap between supply and demand, showed that the Jinghe River Watershed on the whole has begun to be unsustainable since 2008, with each county exhibiting differential temporal patterns. The prediction results showed that the total BC will increase continually from 2020 to 2050, and the BC of six categories will reduce, indicating that unsustainability in the region will escalate. As a whole, The EF demand has exceeded the BC supply, and the gap was widening in the Jinghe Watershed. This paper provided an in-depth portrait of the spatiotemporal dynamics of EF and BC, as well as their interactions with humanity and ecosystems.

Biophysical remote sensing of arctic environments

2003 ◽  
Vol 27 (1) ◽  
pp. 44-68 ◽  
Author(s):  
Gita J. Laidler ◽  
Paul Treitz
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Spatial Scales ◽  
Temporal Distribution ◽  
Remote Sensing Data ◽  
Vegetation Mapping ◽  
General Purpose ◽  
The Arctic ◽  
Natural Systems ◽  
Tundra Ecosystem

Various remote sensing studies have been conducted to investigate methods and applications of vegetation mapping and analysis in arctic environments. The general purpose of these studies is to extract information on the spatial and temporal distribution of vegetation as required for tundra ecosystem and climate change studies. Because of the recent emphasis on understanding natural systems at large spatial scales, there has been an increasing interest in deriving biophysical variables from satellite data. Satellite remote sensing offers potential for extrapolating, or ‘scaling up’ biophysical measures derived from local sites, to landscape and even regional scales. The most common investigations include mapping spatial vegetation patterns or assessing biophysical tundra characteristics, using medium resolution satellite data. For instance, Landsat TM data have been shown to be useful for broad vegetation mapping and analysis, but not accurately representative of smaller vegetation communities or local spatial variation. It is anticipated, that high spatial resolution remote sensing data, now available from commercial remote sensing satellites, will provide the necessary sampling scale to link field data to remotely sensed reflectance data. As a result, it is expected that these data will improve the representation of biophysical variables over sparsely vegetated regions of the Arctic.

scholarly journals Modelling sub-grid wetland in the ORCHIDEE global land surface model: evaluation against river discharges and remotely sensed data

2012 ◽  
Vol 5 (4) ◽  
pp. 941-962 ◽  
Author(s):  
B. Ringeval ◽  
B. Decharme ◽  
S. L. Piao ◽  
P. Ciais ◽  
F. Papa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
River Flow ◽  
Spatial Scales ◽  
Remote Sensing Data ◽  
Surface Model ◽  
Global Evaluation ◽  
Good Opportunity ◽  
Sensing Data ◽  
Basin Scale

Abstract. The quality of the global hydrological simulations performed by land surface models (LSMs) strongly depends on processes that occur at unresolved spatial scales. Approaches such as TOPMODEL have been developed, which allow soil moisture redistribution within each grid-cell, based upon sub-grid scale topography. Moreover, the coupling between TOPMODEL and a LSM appears as a potential way to simulate wetland extent dynamic and its sensitivity to climate, a recently identified research problem for biogeochemical modelling, including methane emissions. Global evaluation of the coupling between TOPMODEL and an LSM is difficult, and prior attempts have been indirect, based on the evaluation of the simulated river flow. This study presents a new way to evaluate this coupling, within the ORCHIDEE LSM, using remote sensing data of inundated areas. Because of differences in nature between the satellite derived information – inundation extent – and the variable diagnosed by TOPMODEL/ORCHIDEE – area at maximum soil water content, the evaluation focuses on the spatial distribution of these two quantities as well as on their temporal variation. Despite some difficulties in exactly matching observed localized inundated events, we obtain a rather good agreement in the distribution of these two quantities at a global scale. Floodplains are not accounted for in the model, and this is a major limitation. The difficulty of reproducing the year-to-year variability of the observed inundated area (for instance, the decreasing trend by the end of 90s) is also underlined. Classical indirect evaluation based on comparison between simulated and observed river flow is also performed and underlines difficulties to simulate river flow after coupling with TOPMODEL. The relationship between inundation and river flow at the basin scale in the model is analyzed, using both methods (evaluation against remote sensing data and river flow). Finally, we discuss the potential of the TOPMODEL/LSM coupling to simulate wetland areas. A major limitation of the coupling for this purpose is linked to its ability to simulate a global wetland coverage consistent with the commonly used datasets. However, it seems to be a good opportunity to account for the wetland areas sensitivity to the climate and thus to simulate its temporal variability.

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