Spatial analysis of ground-based sun induced fluorescence data and canopy pigment content in a dry grassland

2020 ◽  
Author(s):  
Szilvia Fóti ◽  
János Balogh ◽  
Krisztina Pintér ◽  
Zoltán Nagy
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Precision Agriculture ◽  
Light Emission ◽  
Driving Forces ◽  
Spatial Scales ◽  
Photosynthetic Pigment ◽  
Field Work ◽  
Stress Conditions ◽  
Fluorescent Light

<p>Monitoring of canopy photosynthetic performance in optimal and stress conditions has major importance in carbon budget estimates or in precision agriculture. Photosynthesis responds very rapidly to the environmental conditions balancing photochemical processes with different other processes through which excitation energy is lost from the system, including photo-protective heat loss and fluorescent light emission. Although the ratio of photosynthesis to fluorescence in optimal and stress conditions differ, it is not an easy task to assess their actual share, because of the quick adjustment of the pigment-protein complexes or the changing intensity of light re-absorption by chlorophylls.</p><p>Sun induced fluorescence (SIF) measured by ground-based instrument provided direct data of the photosynthetic capacity of the canopy. The O<sub>2</sub> absorptions bands filled with fluorescence served to calculate actual fluorescence intensity within the total upwelling signal. Furthermore, field leaf samples were collected and laboratory analysis was performed to determine photosynthetic pigment contents (both chlorophylls and carotenoids).</p><p>The sampling, both for SIF and pigment data collection followed spatial grid arrangements with different resolutions, 10 × 10 m and 30 × 30 m. Spatial analysis lays on a relatively large number of samples, collected within a very short time period. Our aim was to link the spatial distribution of one target phenomenon to the distribution or intensity of different driving forces, such as terrain features, soil moisture content, soil temperature etc., which were also simultaneously collected in the field work. One measuring occasion at both spatial scales were selected for detailed spatial data processing with geostatistics and kriging.</p>

scholarly journals REVISITING THE CURRENT UAV REGULATIONS IN NEPAL: A STEP TOWARDS LEGAL DIMENSION FOR UAVS EFFICIENT APPLICATION

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 107-114 ◽  
Author(s):  
R. Shrestha ◽  
J. Zevenbergen ◽  
U. S. Panday ◽  
B. Awasthi ◽  
S. Karki
Keyword(s):  
Spatial Data ◽  
Precision Agriculture ◽  
Spatial Scales ◽  
Methodological Approach ◽  
Legal Framework ◽  
Geospatial Technology ◽  
Technical Requirements ◽  
Legal Provisions ◽  
Resource Requirements ◽  
Legal Frameworks

Abstract. UAVs-Unmanned Aerial Vehicles- also known as drones, are an emerging geospatial technology that can facilitate data acquisition at various temporal and spatial scales. Notwithstanding, the wide application of UAVs globally, its wider application is found to be growing in Nepal as well. For instance, precision agriculture, forestry, topographical surveying, etc. It seems that there is a correlation between efficient use of UAVs in these sectors and the legal frameworks that regulate the use of UAVs. Therefore, it seems necessary to obtain holistic national view of UAVs regulations. Aligning with this necessity, this paper provides insight on existing legal provisions for UAVs in Nepal by highlighting the importance, impact, and limitations of UAV regulations. The criteria used in the framework to capture the present holistic legal dimension from literature in the web of science database are a) applicability b) technical requirements c) operational requirements/ limitations d) administration procedure e) human resource requirements and f) implementation of ethical constraints. The adopted methodological approach consists of exploratory case studies, systematic reviews of the concerned literature on UAVs regulations and the workshop on “Flight 4 Purpose” in which various UAVs application were discussed. The results show that the existing legal framework has both strengths and weaknesses for its use to capture the spatial data. The way forward is to harmonize the soft and hard regulations so that such geospatial technology can be applied for overall development and ultimately for the societal benefits.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Mapping wildlife: integrating stakeholder knowledge with modelled patterns of deer abundance by using participatory GIS

2009 ◽  
Vol 36 (7) ◽  
pp. 553 ◽  
Author(s):  
Z. Austin ◽  
S. Cinderby ◽  
J. C. R. Smart ◽  
D. Raffaelli ◽  
P. C. L. White
Keyword(s):  
Community Involvement ◽  
Spatial Data ◽  
Spatial Scales ◽  
Stakeholder Participation ◽  
Landscape Scale ◽  
Scientific Data ◽  
Future Research ◽  
Economic Damage ◽  
Participatory Gis ◽  
Wild Deer

Context. Some species that are perceived by certain stakeholders as a valuable resource can also cause ecological or economic damage, leading to contrasting management objectives and subsequent conflict between stakeholder groups. There is increasing recognition that the integration of stakeholder knowledge with formal scientific data can enhance the information available for use in management. This is especially true where scientific understanding is incomplete, as is frequently the case for wide-ranging species, which can be difficult to monitor directly at the landscape scale. Aims. The aim of the research was to incorporate stakeholder knowledge with data derived from formal quantitative models to modify predictions of wildlife distribution and abundance, using wild deer in the UK as an example. Methods. We use selected predictor variables from a deer–vehicle collision model to estimate deer densities at the 10-km square level throughout the East of England. With these predictions as a baseline, we illustrate the use of participatory GIS as a methodological framework for enabling stakeholder participation in the refinement of landscape-scale deer abundance maps. Key results. Stakeholder participation resulted in modifications to modelled abundance patterns for all wild deer species present in the East of England, although the modifications were minor and there was a high degree of consistency among stakeholders in the adjustments made. For muntjac, roe and fallow deer, the majority of stakeholder changes represented an increase in density, suggesting that populations of these species are increasing in the region. Conclusions. Our results show that participatory GIS is a useful technique for enabling stakeholders to contribute to incomplete scientific knowledge, especially where up-to-date species distribution and abundance data are needed to inform wildlife research and management. Implications. The results of the present study will serve as a valuable information base for future research on deer management in the region. The flexibility of the approach makes it applicable to a range of species at different spatial scales and other wildlife conflict issues. These may include the management of invasive species or the conservation of threatened species, where accurate spatial data and enhanced community involvement are necessary in order to facilitate effective management.

scholarly journals A Comprehensive Review of Low Impact Development Models for Research, Conceptual, Preliminary and Detailed Design Applications

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1541 ◽  
Author(s):  
Sahereh Kaykhosravi ◽  
Usman Khan ◽  
Amaneh Jadidi
Keyword(s):  
Spatial Data ◽  
Spatial Scales ◽  
Low Impact Development ◽  
Model Development ◽  
Point Of View ◽  
Tree Canopy ◽  
Balance Model ◽  
Runoff Generation ◽  
Development Models ◽  
Model Features

This review compares and evaluates eleven Low Impact Development (LID) models on the basis of: (i) general model features including the model application, the temporal resolution, the spatial data visualization, the method of placing LID within catchments; (ii) hydrological modelling aspects including: the type of inbuilt LIDs, water balance model, runoff generation and infiltration; and (iii) hydraulic modelling methods with a focus on the flow routing method. Results show that despite the recent updates of existing LID models, several important features are still missing and need improvement. These features include the ability to model: multi-layer subsurface media, tree canopy and processes associated with vegetation, different spatial scales, snowmelt and runoff calculations. This review provides in-depth insight into existing LID models from a hydrological and hydraulic point of view, which will facilitate in selecting the best-suited model. Recommendations on further studies and LID model development are also presented.

scholarly journals Spatial analysis of hourly precipitation combining observations and ensemble forecasts

2021 ◽  
Author(s):  
Cristian Lussana ◽  
Thomas N. Nipen ◽  
Ivar A. Seierstad ◽  
Christoffer A. Elo
Keyword(s):  
Spatial Analysis ◽  
Gamma Distribution ◽  
Numerical Models ◽  
Spatial Scales ◽  
Data Sources ◽  
Optimal Interpolation ◽  
Interpolation Scheme ◽  
Background Error ◽  
Hourly Precipitation ◽  
Gaussian Anamorphosis

<p>Hourly precipitation is often simultaneously simulated by numerical models and observed by multiple data sources. Accurate precipitation fields based on all available information are valuable input for numerous applications and a critical aspect of climate monitoring. </p><p>Inverse problem theory offers an ideal framework for the combination of observations with a numerical model background. In particular, we have considered a modified ensemble optimal interpolation scheme. The deviations between background and observations are used to adjust for deficiencies in the ensemble. A data transformation based on Gaussian anamorphosis has been used to optimally exploit the potential of the spatial analysis, given that precipitation is approximated with a gamma distribution and the spatial analysis requires normally distributed variables. For each point, the spatial analysis returns the shape and rate parameters of its gamma distribution. </p><p>The ensemble-based statistical interpolation scheme with Gaussian anamorphosis for precipitation (EnSI-GAP) is implemented in a way that the covariance matrices are locally stationary, and the background error covariance matrix undergoes a localization process. Concepts and methods that are usually found in data assimilation are here applied to spatial analysis, where they have been adapted in an original way to represent precipitation at finer spatial scales than those resolved by the background, at least where the observational network is dense enough.</p><p>The EnSI-GAP setup requires the specification of a restricted number of parameters, and specifically, the explicit values of the error variances are not needed, since they are inferred from the available data. </p><p>The examples of applications presented over Norway provide a better understanding of EnSI-GAP. The data sources considered are those typically used at national meteorological services, such as local area models, weather radars, and in situ observations. For this last data source, measurements from both traditional and opportunistic sensors have been considered.</p>

scholarly journals Precision geoinformatical system of the pear gene-collection orchard

2012 ◽  
Vol 18 (1) ◽  
Author(s):  
J. Tamás ◽  
A. Nagy ◽  
T. Fórián ◽  
J. Nyéki ◽  
T. Szabó ◽  
...  
Keyword(s):  
Data Base ◽  
Spatial Data ◽  
Single Species ◽  
Field Work ◽  
Stress Sensitivity ◽  
Hyperspectral Data ◽  
Data Cube ◽  
The Sustainable Development ◽  
Fruit Species ◽  
Set Up

The principle task of the sustainable development is the preservation of the genetic variety, which is similar challenge in the horticulture regarding the sublimation of fruit species. The breeders of the traditional fruit strains give stock to the sustenance diversity of the agro-environment on the species and landscape level. In 2009, hyperspectral images have been taken by AISA Dual sensors from the pear gene pool in Újfehértó, Hungary. The hyperspectral data cube (in the wavelength range of 400-2500 nm, with 1.5 m ground resolution) ensured possibility to make the spectral library of pear species. In the course of the simultaneously field work the spatial position and individual extent of all pear trees was defined to set up a detailed GIS data base. The water stress sensitivity of single species and the descriptive spectral curves were determined with common evaluation of the spectral and spatial data. Based on the unique methodology processing and the hyperspectral data base suitable strains can be chosen for agro-environment and let take adaptive stocks regarding climate change into the genetic grafting work. Furthermore we could determine and map the sparsely species in the region with the help of the hyperspectral data.

scholarly journals Integrating Drone Technology into an Innovative Agrometeorological Methodology for the Precise and Real-Time Estimation of Crop Water Requirements

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 131
Author(s):  
Stavros Alexandris ◽  
Emmanouil Psomiadis ◽  
Nikolaos Proutsos ◽  
Panos Philippopoulos ◽  
Ioannis Charalampopoulos ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Precision Agriculture ◽  
Time Estimation ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Requirements ◽  
Ongoing Research ◽  
Ir Camera

Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The proposed methodology was based on the crop water stress index (CWSI) and was applied in Greece within the ongoing research project GreenWaterDrone. The innovative approach combines real spatial data, such as infrared canopy temperature, air temperature, air relative humidity, and thermal infrared image data, taken above the crop field using an aerial micrometeorological station (AMMS) and a thermal (IR) camera installed on an unmanned aerial vehicle (UAV). Following an initial calibration phase, where the ground micrometeorological station (GMMS) was installed in the crop, no equipment needed to be maintained in the field. Aerial and ground measurements were transferred in real time to sophisticated databases and applications over existing mobile networks for further processing and estimation of the actual water requirements of a specific crop at the field level, dynamically alerting/informing local farmers/agronomists of the irrigation necessity and additionally for potential risks concerning their fields. The supported services address farmers’, agricultural scientists’, and local stakeholders’ needs to conform to regional water management and sustainable agriculture policies. As preliminary results of this study, we present indicative original illustrations and data from applying the methodology to assess UAV functionality while aiming to evaluate and standardize all system processes.

scholarly journals A Spatial Analysis Approach to Understanding Caddoan Mounds in the Arkansas River Drainage

2004 ◽  
Author(s):  
Gregory Vogel
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Large Scale ◽  
Theoretical Background ◽  
Gis Analysis ◽  
Arkansas River ◽  
Gis Modeling ◽  
River Drainage ◽  
The People ◽  
Computationally Intensive

In this article I present a theoretical framework for understanding Caddoan mounds in the central Arkansas River drainage and the implications they may hold for the social structure and environmental adaptations of the people who made them. The power and efficiency of Geographic Information Systems (GIS) modeling now allows for large-scale, computationally intensive spatial analysis simply not possible before. Questions of landscape organization or spatial relationships that previously would have taken months or even years to answer can now be solved in a matter of minutes with GIS and related technologies, given the appropriate datasets. Quite importantly, though, such analyses must first be placed in context and theory if they are to be meaningful additions to our understanding of the past. While it is conventional to refer to “GIS analysis” (and I use the term in this article), it is important to keep in mind that data manipulations alone are not analysis. GIS, along with statistical software and related computer technologies, are tools of spatial analysis just as shovels and trowels are tools of excavation. Such tools can organize and reveal information if they are employed carefully, but the tools themselves have no agency and cannot interpret anything on their own. The terms “GIS analysis” or “GIS interpretation” are therefore somewhat misnomers, just as “trowel analysis” or “trowel interpretation” would be. It is not the GIS, or any component of it, that does the analysis or interpretation; it simply manipulates spatial data. We interpret these manipulations based upon theoretical background, previous research, and the questions we wish to answer.

scholarly journals SEMI-AUTOMATED APPROACH FOR MAPPING URBAN TREES FROM INTEGRATED AERIAL LiDAR POINT CLOUD AND DIGITAL IMAGERY DATASETS

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 127-134 ◽  
Author(s):  
M. A. Dogon-Yaro ◽  
P. Kumar ◽  
A. Abdul Rahman ◽  
G. Buyuksalih
Keyword(s):  
Digital Image ◽  
Spatial Data ◽  
Urban Areas ◽  
Point Cloud ◽  
Structural Complexity ◽  
Urban Ecosystems ◽  
Field Work ◽  
Urban Trees ◽  
Decision Makers ◽  
Image Datasets

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

scholarly journals Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

2020 ◽  
Vol 1 ◽  
pp. 1-23
Author(s):  
Majid Hojati ◽  
Colin Robertson
Keyword(s):  
Big Data ◽  
Spatial Analysis ◽  
Cellular Automata ◽  
Spatial Data ◽  
Data Model ◽  
Data Access ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Global Grid

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

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