Remote sensing of environmental parameters

The Chilan Mountain cypress forest, northeastern Taiwan, is the only one where the genus Chamaecyparis is situated in a subtropical region. The health of a forest ecosystem is closely tied to the evapotranspiration (ET) of water through forests. This study focused on estimating the ET of old-growth cypress in the Chilan Mountain area and investigated its spatial variability in different watershed divisions using remote sensing. Our methods included applying hybrid image classification to generate land cover maps using Landsat-5 images, calculating habitat characteristics of old-growth using the Surface Energy Balance Algorithm for Land (SEBAL), investigating spatial variability of ET in relation to environmental parameters, and examining the gap-snag effect on old-growth cypress ET. The results indicated that the study area was classified into three land cover types (i.e., old-growth, non-old growth, and others). Old-growth had lower values in net radiance, the normalized difference vegetation index (NDVI), and daily ET than did non-old-growth. Watershed divisions at various scales did cause the variation on old-growth ET characteristics according to the selected parameters and the number of parameters for predicting the value of ET. Finally, ET between gap-snag and non-gap-snag habitats was statistically different. A higher proportion in gap-snag composition would lead to a lower value in daily ET and the NDVI.

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A Remote Sensing and GIS-Based Model of Habitat as a Predictor of Biodiversity

The UW National Parks Service Research Station Annual Reports ◽

10.13001/uwnpsrc.1993.3165 ◽

1993 ◽

Vol 17 ◽

pp. 117-124

Author(s):

Diane Debinski

Keyword(s):

Remote Sensing ◽

Environmental Change ◽

Biological Diversity ◽

Wide Spectrum ◽

Environmental Parameters ◽

Species Distributions ◽

Remote Sensing And Gis ◽

Biodiversity Assessment ◽

Species Lists ◽

Global Concern

The loss of biodiversity has become a global concern. Biologists are just beginning to grapple with issues of how to assess biodiversity and to create databases that can be valuable to a wide spectrum of users (e.g., Scott et al. 1990, Margules and Austin 1991). For conservation biologists to make decisions regarding the management of biological diversity, they need adequate floral and faunal inventories for the lands they manage. Species lists are only a first step in addressing large questions regarding relationships between species and their environments, and, in particular, species responses to environmental change. Understanding the environmental parameters that define species distributions is an even more important component of biodiversity assessment.

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Development of a Compatible, Low Cost and High Accurate Conservation Remote Sensing Technology for the Hygrothermal Assessment of Historic Walls

Electronics ◽

10.3390/electronics8060643 ◽

2019 ◽

Vol 8 (6) ◽

pp. 643 ◽

Cited By ~ 7

Author(s):

Elena Lucchi ◽

Luisa Dias Pereira ◽

Mirco Andreotti ◽

Roberto Malaguti ◽

David Cennamo ◽

...

Keyword(s):

Remote Sensing ◽

Data Acquisition ◽

Data Storage ◽

Communication Systems ◽

Low Cost ◽

Environmental Parameters ◽

Data Acquisition System ◽

Acquisition System ◽

Remote Sensing Technology ◽

Sensing Technology

This article aims to properly assess the hygrothermal properties of walls located in historic buildings, this study discloses the development of a remote sensing technology compatible with an in-situ measurement implemented in Palazzo Tassoni (Italy). As required by the international recommendations adapted to cultural heritage (CH), this monitoring system balances CH conservation, performance aspects and economic costs using an integrated multidisciplinary approach. Electronics for measurement of environmental parameters is composed of sensor measurements, data acquisition system and data storage and communication system. Data acquisition system, equipped with standard modbus-rtu interface, is designed to run standalone and it is based on two cloned single board PCs to reduce the possibility of data loss. In order to reduce the costs, RaspberryPI single board PCs were chosen. These run a C/C++ software based on standard modbus library and designed to implement multi-client server TCP/IP to allow communication with other devices. Storage and communication systems are based on an industrial PC; it communicates with sensor measurements’ system through a modbus-TCPIP bridge. PC runs a Labview software to provide data storage on a local database and graphical user interface to properly see all acquired data. Herein, some sensing options and approaches of measurement are described, unveiling different possible ways of enhancing the retrofit of CH with adapted technology.

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Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing

Remote Sensing ◽

10.3390/rs10091329 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1329 ◽

Cited By ~ 6

Author(s):

Shangrong Lin ◽

Jing Li ◽

Qinhuo Liu ◽

Alfredo Huete ◽

Longhui Li

Keyword(s):

Remote Sensing ◽

Primary Production ◽

Forest Canopy ◽

Gross Primary Production ◽

Model Performance ◽

Single Layer ◽

Environmental Parameters ◽

Terrestrial Ecosystems ◽

Vertical Stratification ◽

Area Index

Gross primary production (GPP) in forests is the most important carbon flux in terrestrial ecosystems. Forest ecosystems with high leaf area index (LAI) values have diverse species or complex forest structures with vertical stratifications that influence the carbon–water–energy cycles. In this study, we used three light use efficiency (LUE) GPP models and site-level experiment data to analyze the effects of the vertical stratification of dense forest vegetation on the estimates of remotely sensed GPP during the growing season of two forest sites in East Asia: Dinghushan (DHS) and Tomakomai (TMK). The results showed that different controlling environmental factors of the vertical layers, such as temperature and vapor pressure deficit (VPD), produce different responses for the same LUE value in the different sub-ecosystems (defined as the tree, shrub, and grass layers), which influences the GPP estimation. Air temperature and VPD play important roles in the effects of vertical stratification on the GPP estimates in dense forests, which led to differences in GPP uncertainties from −50% to 30% because of the distinct temperature responses in TMK. The unequal vertical LAI distributions in the different sub-ecosystems led to GPP variations of 1–2 gC/m2/day with uncertainties of approximately −30% to 20% because sub-ecosystems have unique absorbed fractions of photosynthetically active radiation (APAR) and LUE. A comparison with the flux tower-based GPP data indicated that the GPP estimations from the LUE and APAR values from separate vertical layers exhibited better model performance than those calculated using the single-layer method, with 10% less bias in DHS and more than 70% less bias in TMK. The precision of the estimated GPP in regions with thick understory vegetation could be effectively improved by considering the vertical variations in environmental parameters and the LAI values of different sub-ecosystems as separate factors when calculating the GPP of different components. Our results provide useful insight that can be used to improve the accuracy of remote sensing GPP estimations by considering vertical stratification parameters along with the LAI of sub-ecosystems in dense forests.

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THE USE OF REMOTE SENSING, REGRESSION QUANTILES, AND GIS APPROACHES FOR MODELING OF SCALLOP LARVAE: A Case Study in Funka Bay, Hokkaido, Japan

Indonesian Aquaculture Journal ◽

10.15578/iaj.6.2.2011.191-204 ◽

2011 ◽

Vol 6 (2) ◽

pp. 191

Author(s):

I Nyoman Radiarta

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Environmental Changes ◽

Monitoring Program ◽

Environmental Parameters ◽

Sea Surface ◽

Remotely Sensed Data ◽

Funka Bay ◽

Scallop Larvae

In the development of scallop cultivation in Japan, larvae collection and propagation become an important factor. Although the monitoring program has been conducted, modeling of species distribution is becoming an important tool for understanding the effects of environmental changes and resources management. This study was conducted to construct a model for providing estimation of the scallop larvae distribution in Funka Bay, Hokkaido, Japan using the integration of remote sensing, Regression Quantile (RQ) and Geographic Information System (GIS)-based model. Data on scallop larvae were collected during one year spawning season from April to July 2003. Environmental parameters were extracted from multi sensor remotely sensed data (chlorophyll-a and sea surface temperature) and a hydrographic chart (water depth). These parameters together with larvae data were then analyzed using RQ. Finally, spatial models were constructed within a GIS by combining the RQ models with digital map of environmental parameters. The results show that the model was best explained by using only sea surface temperature. The highest larvae densities were predicted in a relatively broad distribution along with the shallow water regions (Toyoura and Sawara to Yakumo) and the deeper water areas (center of the bay). The spatial model built from the RQ provided robust estimation of the scallop larvae distributions in the study area, as confirmed by model validation using independent data. These findings could contribute on the monitoring program in this region in order to distinguish the potential areas for an effective spat collection.

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Application of satellite images and VNREDSAT-1 images in study on marine environment in Truong Sa region

Tạp chí Khoa học và Công nghệ Biển ◽

10.15625/1859-3097/19/3b/14522 ◽

2019 ◽

Vol 19 (3B) ◽

pp. 149-162

Author(s):

Do Huy Cuong ◽

Bui Thi Bao Anh ◽

Nguyen Xuan Tung ◽

Nguyen The Luan ◽

Le Dinh Nam ◽

...

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Marine Environment ◽

Surface Current ◽

Environmental Parameters ◽

Research Area ◽

Real Field ◽

Remote Sensing Images ◽

Field Surveys ◽

Different Depths

The remote sensing images, including images of MODIS, VNREDSAT-1 and altimeter, are applied for researching marine environment with the different resolutions. On the basis of different time remote sensing images, we concentrated on the assessment of several characteristics including the SST, chlorophyll-a concentration and sea surface current at the different depths in different monsoons as well. With the large areas, we used the images of MODIS and altimeter. The detailed research area focuses on the Nam Yet island, and the images of VNREDSAT-1 are used. The analysis method of environmental parameters of SST and chlorophyll-a used the regression functions based on the single and combined bands to enhance the accuracy of the analysis result. The marine parameters collected at different depths in the latest field surveys on Truong Sa archipelago in the years of 2015 and 2018 are presented in this paper. On the basis of these parameters, we can analyse the relationships and compare the real field survey data and corresponding results interpreted from remote sensing images.

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Air temperature estimation based on environmental parameters using remote sensing data

Journal of Applied researches in Geographical Sciences ◽

10.29252/jgs.18.48.131 ◽

2018 ◽

Vol 18 (48) ◽

pp. 131-152

Author(s):

Chenoor Mohammadi ◽

Manouchehr Farajzadeh ◽

Yousef Ghavdel Rahimi ◽

Abbas Ali Aliakbar Bidokhti ◽

◽

...

Keyword(s):

Remote Sensing ◽

Air Temperature ◽

Remote Sensing Data ◽

Environmental Parameters ◽

Temperature Estimation ◽

Sensing Data ◽

Air Temperature Estimation

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SPATIOTEMPORAL ANALYSIS FOR WILDLIFE-VEHICLE-COLLISIONS BASED ON ACCIDENT STATISTICS OF THE COUNTY STRAUBING-BOGEN IN LOWER BAVARIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-739-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 739-745

Author(s):

R. Pagany ◽

W. Dorner

Keyword(s):

Remote Sensing ◽

Land Use ◽

Remote Sensing Data ◽

Environmental Parameters ◽

Vegetation Period ◽

Traffic Density ◽

Future Research ◽

Small Scale ◽

Vehicle Collisions ◽

Sensing Data

During the last years the numbers of wildlife-vehicle-collisions (WVC) in Bavaria increased considerably. Despite the statistical registration of WVC and preventive measures at areas of risk along the roads, the number of such accidents could not be contained. Using geospatial analysis on WVC data of the last five years for county Straubing-Bogen, Bavaria, a small-scale methodology was found to analyse the risk of WVC along the roads in the investigated area. Various indicators were examined, which may be related to WVC. The risk depends on the time of the day and year which shows correlations in turn to the traffic density and wildlife population. Additionally the location of the collision depends on the species and on different environmental parameters. Accidents seem to correlate with the land use left and right of the street. Land use data and current vegetation were derived from remote sensing data, providing information of the general land use, also considering the vegetation period. For this a number of hot spots was selected to identify potential dependencies between land use, vegetation and season. First results from these hotspots show, that WVCs do not only depend on land use, but may show a correlation with the vegetation period. With regard to agriculture and seasonal as well as annual changes this indicates that warnings will fail due to their static character in contrast to the dynamic situation of land use and resulting risk for WVCs. This shows that there is a demand for remote sensing data with a high spatial and temporal resolution as well as a methodology to derive WVC warnings considering land use and vegetation. With remote sensing data, it could become possible to classify land use and calculate risk levels for WVC. Additional parameters, derived from remote sensed data that could be considered are relief and crops as well as other parameters such as ponds, natural and infrastructural barriers that could be related to animal behaviour and should be considered by future research.

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Using variations in High Arctic vegetation spectral properties to predict various types of plant, soil, and environmental variables.

Inquiry@Queen's Undergraduate Research Conference Proceedings ◽

10.24908/iqurcp.14722 ◽

2021 ◽

Author(s):

Sandra Yaacoub

Keyword(s):

Remote Sensing ◽

Learning Algorithm ◽

Vegetation Indices ◽

Net Ecosystem Exchange ◽

Environmental Parameters ◽

High Arctic ◽

Hyperspectral Data ◽

Nutrient Concentrations ◽

Polar Regions ◽

Foliar Nitrogen

In comparison to other regions, the High Arctic is experiencing accelerated rates of warming (Meredith et al., 2019). Hyperspectral remote sensing may provide a way to monitor changes in productivity without having to make detailed ground-based measurements. During the 2017 field season researchers on Melville Island, Nunavut, collected in-situ hyperspectral data, plant nutrient concentrations, carbon dioxide gas exchange measurements, and various environmental parameters in a wet-sedge tundra environment. These data were processed with the overall objective of determining if spectral information may be used to quantify changes in productivity across the High Arctic. Using a random forest machine learning algorithm, wavelengths from the hyperspectral data were identified for use in nine vegetation indices (VIs) based on relationships to foliar nitrogen concentrations. Using linear regressions, these VIs were compared to the environmental parameters. Although none correlated significantly to foliar nitrogen, three VIs showed p-values < 0.05 (alpha = 0.05) consistently for the following variables: soil nitrate and ammonia concentrations, net ecosystem exchange (NEE), and gross primary productivity (GPP) values. This shows promise for the use of remote sensing techniques to aid in monitoring the High Arctic. Additional research within this field would help pave way towards increased certainty on the kinds of responses that are in store for these landscapes if warming is to continue at an accelerated rate. This may bring increased monitoring frequency and scale of environmental assessment across the High Arctic, granting communities influenced by warming additional tools to aid in safer regional navigation and improved emergency response preparedness. References Meredith, M., Sommerkorn, M., Cassotta, S., Derksen, C., Ekaykin, A., Hollowed, A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., Muelbert, M. M. C. M. M. C., Ottersen, G., Pritchard, H., & Schuur, E. A. G. E. A. G. (2019). Polar Regions. In H.-O. Pörtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 203–320). https://www.ipcc.ch/srocc/chapter/chapter-3-2/

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