Question Paper Solutions of chapter Applications of GIS & Remote Sensing of GIS & Remote Sensing, 8th Semester , Civil Engineering

This project serves as a focal point of capability and expertise for integrating remote sensing, satellite telemetry and GIS. Working collaboratively with other principal investigators, this project applies satellite and software technologies to study spatial and temporal interactions between wildlife populations and their environment. There are three primary objectives: 1) develop optimal structures for wildlife distribution databases with emphasis on satellite tracking data; 2) develop environmental thematic databases with emphasis on Arctic regions; and 3) develop GIS algorithms for integrated data analyses. Commensurate with accelerating advances in remote sensing, satellite telemetry, and geographic information system (GIS) technology, the primary objective of this task is to evaluate and apply these state-of-the-art tools for developing or improving the methodologies used in wildlife and ecosystem research. The need for cost-effective techniques to systematically acquire environmental data for remote or inaccessible areas, and locational data for highly mobile or migratory species, crosses bureau, program and issue boundaries. This is especially true in arctic regions, where numerous fish and wildlife populations often range internationally, across extensive landscapes of tundra, boreal forest, polar sea-ice, and aquatic ecosystems. Remote sensing technologies provide alternatives to traditional sampling methods, which are typically too expensive to implement across large spatial scales or severely compromised by hazardous weather conditions and extended winter darkness. Publications: Douglas, D.C., 2010, Arctic sea ice decline: Projected changes in timing and extent of sea ice in the Bering and Chukchi Seas: U.S. Geological Survey Open-File Report 2010-1176, 32 p. Belchansky, G. I., D. C. Douglas, and N. G. Platonov (2005), Spatial and temporal variations in the age structure of Arctic sea ice, Geophys. Res. Lett.,32, L18504, doi:10.1029/2005GL023976 Belchanksy, G. I., D. C. Douglas, I. N. Mordvintsev, and N. G. Platonov (2004), Estimating the time of melt onset and freeze onset over Arctic sea-ice area using active and passive microwave data. Remote Sens. Environ., 92 , 21-39. Belchansky, G. I., D. C. Douglas, and N. G. Platonov (2004), Duration of the Arctic sea ice melt season: Regional and interannual variability, 1979-2001, J. Climate, 17 , 67-80. Belchansky, G. I., D. C. Douglas, I. V. Alpatsky, and N. G. Platonov (2004) , Spatial and temporal multiyear sea ice distributions in the Arctic : A neural network analysis of SSM/I data, 1988-2001, J. Geophys. Res. , 109 (C12), doi:10.1029/2004JC002388. Stone, R. S., D. C. Douglas, G. I. Belchansky, S. D. Drobot, and J. Harris (2005), Cause and effect of variations in western Arctic snow and sea ice cover. 8.3, Proc. Am. Meteorol. Soc. 8 th Conf. on Polar Oceanogr. and Meteorol. , San Diego , CA , 9-13 January. Belchansky, G. I., D. C. Douglas, V. A. Eremeev, and N. G. Platonov (2005), Variations in the Arctic's multiyear sea ice cover: A neural network analysis of SMMR-SSM/I data, 1979-2004. Geophys. Res. Lett. Vol. 32, No. 9, L09605, doi:10.1029/2005GL022395. Stone, R. S., D. C. Douglas, G. I. Belchansky, and S. D. Drobot (2005), Polar climate: Arctic sea ice, Pages 39-41 in D. H. Levinson (ed.), State of the Climate in 2004, Bull. Amer. Meterol. Soc., Vol. 86, No. 6, 86 pp. Stone, R. S., D. C. Douglas, G. I. Belchansky, and S. D. Drobot (2005), Correlated declines in western Arctic snow and sea ice cover. Arctic Res. United States, 19:18-25.

Note: This LCMS CONUS Cause of Change image service has been deprecated. It has been replaced by the LCMS CONUS Annual Change image service, which provides updated and consolidated change data.Please refer to the new service here: https://usfs.maps.arcgis.com/home/item.html?id=085626ec50324e5e9ad6323c050ac84dThis product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS change attribution classes for each year. See additional information about change in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. https://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAZhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Discover the booming Satellite Remote Sensing Software market! This in-depth analysis reveals market size, CAGR, key drivers, trends, and restraints, including regional breakdowns and leading companies. Explore the opportunities in agriculture, water management, and more. Learn about the growing impact of AI and open-source solutions.

The booming remote sensing software market is projected to reach $5 billion by 2025, growing at a CAGR of 8% until 2033. Driven by advancements in sensor technology and cloud computing, this market caters to various sectors, including environmental monitoring, urban planning, and defense. Learn about key market trends and leading players.

Discover the booming Satellite Remote Sensing Software market! Explore key trends, growth drivers, and regional market shares in our comprehensive analysis. Learn about leading companies and the future of this technology in agriculture, forestry, and beyond. Get the insights you need to make informed decisions.

The Computer Vision in Geospatial Imagery market is experiencing robust growth, driven by increasing demand for accurate and efficient geospatial data analysis across various sectors. Advancements in artificial intelligence (AI), deep learning, and high-resolution imaging technologies are fueling this expansion. The market's ability to extract valuable insights from aerial and satellite imagery is transforming industries such as agriculture, urban planning, environmental monitoring, and defense. Applications range from precision agriculture using drone imagery for crop health monitoring to autonomous vehicle navigation and infrastructure inspection using high-resolution satellite data. The integration of computer vision with cloud computing platforms facilitates large-scale data processing and analysis, further accelerating market growth. We estimate the 2025 market size to be approximately $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is expected to continue, driven by increasing adoption of advanced analytics and the need for real-time geospatial intelligence. Several factors contribute to this positive outlook. The decreasing cost of high-resolution sensors and cloud computing resources is making computer vision solutions more accessible. Furthermore, the growing availability of large datasets for training sophisticated AI models is enhancing the accuracy and performance of computer vision algorithms in analyzing geospatial data. However, challenges remain, including data privacy concerns, the need for robust data security measures, and the complexity of integrating diverse data sources. Nevertheless, the overall market trend remains strongly upward, with significant opportunities for technology providers and users alike. The key players listed—Alteryx, Google, Keyence, and others—are actively shaping this landscape through innovative product development and strategic partnerships.

Abstract Among the various characteristics of the Brazilian territory, one is foremost: the country has the second largest forest reserve on the planet, accounting for approximately 10% of the total recorded global forest formations. In this scenario, seasonally dry tropical forests (SDTF) are the second smallest forest type in Brazil, located predominantly in non-forested biomes, such as the Cerrado and Caatinga. Consequently, correct identification is fundamental to their conservation, which is hampered as SDTF areas are generally classified as other types of vegetation. Therefore, this research aimed to monitor the Land Use and Coverage in 2007 and 2016 in the continuous strip from the North of Minas Gerais to the South of Piauí, to diagnose the current situation of Brazilian deciduous forests and verify the chief agents that affect its deforestation and regeneration. Our findings were that the significant increase in cultivated areas and the spatial mobility of pastures contributed decisively to the changes presented by plant formations. However, these drivers played different roles in the losses/gains. In particular, it was concluded that the changes occurring to deciduous forests are particularly explained by pastured areas. The other vegetation types were equally impacted by this class, but with a more incisive participation of cultivation.

Snow, glaciers and permafrost in cold mountain areas such as the Swiss Alps are especially sensitive to changes in environmental conditions due to their proximity to melting conditions. In addition, mass wasting is most intensive in those mountain areas with high relief energy. Environmental changes in high mountain regions substantially influence the potential for glacial and periglacial hazards. Ice- and moraine-dammed lakes represent a widespread hazard potential closely related to glacier fluctuations. Magnitude and frequency of ice avalanches from steep glaciers - in principle a normal expression of mass exchange under such topographic conditions - are coupled with stability conditions affected by glacier advance/retreat and, hence, with long-term atmospheric impacts. Steep and unstable reservoirs of loose debris, a potential source of debris flows, are often the result of glacier shrinkage. In a similar way, changes in the stress regime due to vanishing glaciers lead to potential destabilization of adjacent valley flanks.

 Since the Alps are among the most densely populated high mountain areas in the
 world, Switzerland is particularly impacted by glacial and periglacial hazards
 but, on the other hand, also has an extensive and well-recognized tradition in
 investigating such processes. A number of specific monitoring and modeling
 studies related to single hazardous situations have been performed, mainly
 based on recent catastrophes or imminent hazard situations. An urgent need
 exists for area-wide modeling of glacier hazard potentials with a view to
 establishing an integrated and adequate information base for planning and
 detailed monitoring, but a corresponding systematic approach is, for the
 present, still lacking.

 The proposed project aims at closing this gap in several ways: Work Package
 (WP) (1): By developing techniques for detection of glacier hazard potentials
 based on optical spaceborne remote sensing data which rarely has been used to
 date in Swiss glacier monitoring; multispectral analyses and multitemporal and
 multiscale fusion will play a major role in this, with a special focus on
 recent or upcoming high resolution sensors. WP (2): By integrating empirical
 models for glacier hazard assessment into geographical information systems
 (GIS) which have proven to be successful for hazard simulation but have not
 been used yet for determining glacier hazard potentials; GIS modeling
 especially allows for the fusion of remote sensing and elevation data for
 spatial (3D) analyses. To ensure high synergy, WPs (1) and (2) will be closely
 related to the ongoing SNF project "The Swiss Glacier Inventory 2000" (SWI
 2000) (no. 21-54073.98) and the international project "Global Land Ice
 Monitoring from Space" (GLIMS). WP (3): By applying the methods from WPs (1)
 and (2), an initial attempt will be undertaken to implement an area-wide model
 for integrating glacier hazard potentials of extensive regions in the Swiss
 Alps following a downscaling strategy with varying resolution and accuracy
 levels, both with respect to data and to models. As hazard management in
 Switzerland is the domain of local and regional authorities, the proposed
 project does not aim at preparing detailed local hazard maps (Gefahrenkarten),
 but rather will provide new remote sensing and modeling techniques for
 decision support. It should demonstrate the usefulness of these techniques for
 overview mapping (Gefahrenhinweiskarten) as a basis for decision-making and for
 scenario simulations in connection with climate change effects. The efforts
 made in this project will contribute to handle economically complex
 mathematical and physical models and represent a decision basis for the
 specific need of further detailed case studies. A further outcome will be a
 documentation of historical glacier catastrophes in the Swiss Alps, which will
 - among others - be used for model calibration and verification.

 [Summary provided by Christian Huggel, University of Zurich.]

This data support the paper "A systematic review on the integration of remote sensing and GIS to forest and grassland ecosystem health attributes, indicators, and measures " by Irini Soubry, Thuy Doan, Thuan Chu and Xulin Guo 2021 in the journal of "Remote Sensing" by MDPI. It includes the "Search_Effort.csv" list with the keywords and number of studies selected for further examination, the "Potential_Studies.csv" with the post-filtering of suitability and notes related to each study, the "Metadata.csv" with the information collected for each metadata variable per study, and the "ExtractedData.csv" with the information collected for each extracted dta variable per study. More information about the data collection and procedures can be found in the respective manuscript.

Question Paper Solutions of chapter Spatial Data Analysis of GIS & Remote Sensing, 8th Semester , Civil Engineering

Discover the booming satellite remote sensing software market! This in-depth analysis reveals key trends, growth drivers, regional market shares, and leading companies shaping this $2.2 billion (2025 est.) industry. Explore the potential of precision agriculture, water resource management, and more.

This data set contains high-resolution QuickBird imagery and geospatial data for the entire Barrow QuickBird image area (156.15° W - 157.07° W, 71.15° N - 71.41° N) and Barrow B4 Quadrangle (156.29° W - 156.89° W, 71.25° N - 71.40° N), for use in Geographic Information Systems (GIS) and remote sensing software. The original QuickBird data sets were acquired by DigitalGlobe from 1 to 2 August 2002, and consist of orthorectified satellite imagery. Federal Geographic Data Committee (FGDC)-compliant metadata for all value-added data sets are provided in text, HTML, and XML formats.

Accessory layers include: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); an index map for the 62 QuickBird tiles (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow QuickBird image area and the Barrow B4 quadrangle area (ESRI Shapefile format).

Unmodified QuickBird data comprise 62 data tiles in Universal Transverse Mercator (UTM) Zone 4 in GeoTIFF format. Standard release files describing the QuickBird data are included, along with the DigitalGlobe license agreement and product handbooks.

The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest. Data are provided on four DVDs. This product is available only to investigators funded specifically from the National Science Foundation (NSF), Office of Polar Programs (OPP), Arctic Sciences Section. An NSF OPP award number must be provided when ordering this data. Contact NSIDC User Services at nsidc@nsidc.org to order the data, and include an NSF OPP award number in the email.

The Remote Sensing Interpretation Software market is booming, with a projected CAGR exceeding 10% through 2033. Driven by advancements in technology and increasing demand across agriculture, petroleum, and other sectors, this market offers lucrative opportunities. Explore key trends, regional insights, and leading companies shaping this dynamic industry.

Land cover maps are a critical component to make informed policy, development, planning, and resource management decisions. However, technical, capacity, and institutional challenges inhibit the creation of consistent and relevant land cover maps for use in developing regions. Many developing regions lack coordinated capacity, infrastructure, and technologies to produce a robust land cover monitoring system that meets land management needs. Local capacity may be replaced by external consultants or methods which lack long-term sustainability. In this study, we characterize and respond to the key land cover mapping gaps and challenges encountered in the Lower Mekong (LMR) and Hindu Kush-Himalaya (HKH) region through a needs assessment exercise and a collaborative system design. Needs were assessed using multiple approaches, including focus groups, user engagement workshops, and online surveys. Efforts to understand existing limitations and stakeholder needs resulted in a co-developed and modular land cover monitoring system which utilizes state-of-the-art cloud computing and machine learning which leverages freely available Earth observations. This approach meets the needs of diverse actors and is a model for transnational cooperation.

Supporting information for: REMAP: An online remote sensing application for land cover classification and monitoringcsv and json files for implementing land cover classifications using the remap, the remote ecosystem assessment and monitoring pipeline (https://remap-app.org/)Nearmap aerial photograph courtesy of Nearmap Pty Ltd.For further information see:Murray, N.J., Keith, D.A., Simpson, D., Wilshire, J.H., Lucas, R.M. (accepted) REMAP: A cloud-based remote sensing application for generalized ecosystem classifications. Methods in Ecology and Evolution.

The satellite remote sensing market is booming, projected to reach $4911.2 million by 2025, with a 17.9% CAGR. Discover key drivers, trends, and leading companies shaping this dynamic sector, including Airbus, Boeing, and Planet Labs. Explore market size projections and regional breakdowns for informed strategic planning.

To investigate the feasibility of using satellite-based remote sensing to study habitat use of polar bears {Ursus aritimus), we compared distributions of satellite locations of radio-collared adult female bears to sea ice concentration (percent ice coverage of 25 x 25 km grid cells) in the Bering and Chukchi Seas at intervals of 10-14 days from April 1990 through February 1991. Ice concentrations were calculated from daily images of surface brightness temperatures detected by satellite-based passive microwave imaging {Special Sensor Microwave/Imager [SSM/I]). Limited precision of the satellite imagery and radio-tracking data prevented us from investigating use of ice concentrations <1%, which bears commonly used during late summer. Furthermore, lack of surface-truth data to support the ice classifications and the possibility of geolocation errors in the SSM/I data indicate that our results must be considered with caution. However, our data suggested that habitat use by female polar bears varied during the year. Most bears remained on the ice pack all year, and were widely distributed during winter and spring, when seasonal ice covered much of the Bering and Chukchi Seas. During summer, bears were found most often in areas with

Discover the Remote Sensing Object Segmentation Dataset Perfect for GIS, AI driven environmental studies, and satellite image analysis.

Discover the booming Remote Sensing Interpretation Software market! Our in-depth analysis reveals key trends, growth drivers, and leading companies in this dynamic sector. Explore market size projections, regional breakdowns, and application-specific insights to understand the future of geospatial analysis.