Kglc 061709
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This thesis examines the use of remote sensing to assess rangeland condition on the Central Great Plains. Aerial imagery was collected using a multispectral camera across multiple dates to calculate NDVI values and monitor vegetation changes under different grazing practices. Field data on biomass and plant cover was also collected and related to the aerial imagery. The results provide preliminary evidence that rangeland management practices can produce measurable differences in vegetation detected by NDVI. Further statistical analysis will correlate the remote sensing data with field measurements to better understand how aerial imagery can assess rangeland condition.
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The document provides information on the Landsat satellite program, including details on Landsat 1-8 such as orbital characteristics, sensors, and resolutions. It launched between 1972-2013 with improved sensors over time. The Landsat satellites are used for applications like mapping land cover, monitoring vegetation and coastal zones. The document also summarizes the SPOT satellite program with details on SPOT 1-4 launched between 1986-1998.
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Remote sensing platforms can be ground-based, airplane-based, or satellite-based. Satellite platforms can be in sun-synchronous polar orbits for global coverage, non-sun-synchronous orbits for variable coverage, or geostationary orbits for continuous regional coverage. Remote sensing can be passive using sunlight or active using its own energy source like radar or lidar. Spatial, spectral, radiometric, and temporal resolutions provide information on a sensor's ability to distinguish locations, wavelengths, brightness values, and revisit times. Raster data formats represent imagery as a grid of pixels organized into rows, columns, and bands.
WE3.L10.4: KIYO TOMIYASU, CO-SEISMIC SLIP AND THE KRAFLA VOLCANO: REFLECTIONS...
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IA 2019 5a
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This slide provides a detail overview of MODIS sensors and it also mentions about the MODIS receiving station at Nepal.
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The document discusses remote sensing satellites. It begins by defining remote sensing as obtaining information about an object through analysis of data acquired from a distance without physical contact. There are two broad categories of remote sensing based on platforms: aerial and satellite. Satellite remote sensing has advantages like continuous data acquisition and broad area coverage. Remote sensing systems are classified based on the radiation source as passive or active, and based on spectral regions as optical, thermal infrared, or microwave. Key resolutions for remote sensing include spatial, spectral, temporal, and radiometric. Common applications are land cover mapping, change detection, flood monitoring, and more. Major satellite missions discussed are Landsat, SPOT, and IKONOS.
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A multispectral image is one that captures image data from two or more ranges of frequencies along the spectrum, such as visible light and infrared energy.
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The document discusses landmine detection using ground penetrating radar (GPR). It provides background on the landmine problem, current detection methods, and how GPR works to detect landmines. GPR transmits electromagnetic pulses into the ground and receives reflected signals that can reveal the presence of landmines. While GPR shows promise for landmine detection, challenges remain around generating false alarms from background signals and the size and power needs of GPR systems.
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Pratik
Synthetic aperture radar uses an antenna mounted on a moving platform like an aircraft or satellite to synthesize a large antenna aperture and collect radar data to produce high-resolution 2D images of target objects. It works by sending out narrow radar pulses and receiving the echoes to determine distance and Doppler shift for speed, and processing multiple images to map terrain, monitor environments, and support military systems through polarimetry and interferometry techniques.
Optical remote sensing
Optical remote sensing uses visible, near infrared, and shortwave infrared sensors to form images of the Earth's surface by detecting solar radiation reflected from targets. Different materials reflect and absorb light differently at different wavelengths, allowing targets to be differentiated by their spectral signatures. Optical remote sensing systems are classified as panchromatic, multispectral, hyperspectral, or superspectral depending on the number of spectral bands measured.
Ground Penetrating Radar (GPR)
Ground penetrating radar (GPR) is a geophysical method that uses radar pulses to image the subsurface. It can detect objects, changes in material, and voids or cavities underground. GPR works by transmitting electromagnetic pulses into the ground and measuring the time it takes for the pulses to reflect back to a receiver antenna. Different materials and objects underground cause different reflections that appear as hyperbolic patterns in GPR images. GPR systems consist of a transmitter antenna, receiver antenna, control unit and display. The frequency used depends on the desired depth of penetration and resolution needed. GPR has advantages of being non-invasive, fast, and able to provide 3D images of underground structures, but its effectiveness is limited by certain soil or terrain conditions.
Basics of remote sensing, pk mani
This document provides an outline and overview of the topics covered in a course on remote sensing and its applications in soil resource mapping. The outline includes definitions of remote sensing, the history and development of the technology, principles of electromagnetic radiation and its interactions with targets, spectral signatures, resolutions, satellite orbits, and applications of remote sensing. Examples are given of different remote sensing systems like the human eye. A definition of remote sensing is provided. The history section describes early developments dating back to the 1600s and important events in the 1900s. The document gives an overview of active and passive remote sensing as well as elements involved like the energy source, radiation, recording, transmission and more. Remote sensing principles, websites and literature are also listed.
GPS
GPS uses a constellation of 24 satellites that orbit Earth and emit signals to allow GPS receivers to determine their precise location. It was developed by the U.S. Department of Defense and consists of three segments - the space segment with the satellites, the control segment consisting of ground stations that monitor the satellites, and the user segment made up of receivers that detect satellite radio signals and use triangulation to calculate the user's position. GPS has both military and civilian applications including navigation, mapping, tracking resources and people.
Ground Penetrating Radar Survey
Ground Penetrating Radar (GPR) is a technique that uses radio waves between 1-1000 MHz to map underground structures by emitting signals and detecting echoes to build images. GPR is used to locate buried utilities, for mine site evaluation, forensic investigations, archaeological digs, and detecting unexploded ordnance by measuring signal time delay and strength. The document provides an overview of GPR including how it works and its various applications.
New landmines detection using ground penetrating radar
This document discusses ground penetrating radar (GPR) and its application in landmine detection. GPR uses low frequency electromagnetic waves to locate underground objects by imaging reflections. It has high resolution and can detect even small, non-metallic landmines. Models are presented for how electromagnetic waves propagate through soil and interact with buried objects like landmines. Combining GPR with other technologies like nuclear quadrupole resonance could improve detection accuracy and reduce false alarms in landmine detection and removal efforts.
Sentinel 2
The document provides information about accessing and using data from the Sentinel-2 satellite mission. It outlines several portals for downloading Sentinel-2 imagery, including the ESA SciHub, Amazon Web Services, and USGS websites. It describes how to search for and filter imagery using criteria like date, cloud cover percentage, and region. The document also defines some key terms related to Sentinel-2 imagery like granules, datastrips, and datatakes. It lists the spatial resolutions of the different spectral bands and gives examples of pre-processing steps that can be applied to the data like resampling, atmospheric correction, and classification.
Introduction to Remote Sensing
Lidar uses laser light to measure distances by illuminating targets. It is an active remote sensing method. The document discusses remote sensing concepts like platforms, sensors, data collection using electromagnetic radiation, and data interpretation techniques. It provides examples of Indian remote sensing satellites like Resourcesat and Cartosat, and describes their sensors and applications in areas like agriculture, mapping, and disaster management. Visual interpretation of remote sensing images involves analyzing tone, shape, size, pattern, texture, shadows, and associations of targets.
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Similar to Kglc 061709
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1Resolutions of Remote Sensing1. Spatial (what area an.docx
1
Resolutions of Remote Sensing
1. Spatial (what area and how detailed)
2. Spectral (what colors – bands)
3. Temporal (time of day/season/year)
4. Radiometric (color depth)
Spatial Resolution describes how much
detail in a photographic image is visible to
the human eye.
The ability to "resolve," or separate, small
details is one way of describing what we
call spatial resolution.
2
Spatial resolution of images acquired by satellite sensor
systems is usually expressed in meters.
For example, we often speak of Landsat as having “30-
meter" resolution, which means that two objects, thirty
meters long or wide, sitting side by side, can be
separated (resolved) on a Landsat image.
Other sensors have lower or higher spatial resolutions.
Comparison of Landsat Sensors
Thematic Mapper
(TM) Landsat 4 and 5
Enhanced Thematic
Mapper Plus (ETM+)
Landsat 7
Multispectral
Scanner (MSS)
Landsat 1-5
Spectral
Resolution
(µm)
1. 0.45-0.52 (B)
2. 0.52-0.60 (G)
3. 0.63-0.69 (R)
4. 0.76-0.90 (NIR)
5. 1.55-1.75 (MIR)
6. 2.08-2.35 (MIR)
7. 10.4-12.5 (TIR)
1. 0.45-0.52
2. 0.53-0.61
3. 0.63-0.69
4. 0.78-0.90
5. 1.55-1.75
6. 2.09-2.35
7. 10.4-12.5
8. 0.52-0.90 (Pan)
0.5-0.6 (green)
0.6-0.7 (red)
0.7-0.8 (NIR)
0.8-1.1 (NIR)
Spatial
Resolution
(meter)
30 x 30
120 x 120 (TIR)
15 x 15 (Pan)
30 x 30
60 x 60 (TIR)
79 x 79
Temporal
Resolution
(revisit in days)
16 16 18
Spatial
coverage (km)
185 x 185 183 x 170 185 x 185
Altitude (km) 705 705 915 (Landsat 1,2,3)
3
Planimetric data – roads, buildings, driveways
Spatial Resolution
80 meter MSS w/ planimetric overlay
Spatial Resolution
4
30 meter TM w/ planimetric overlay
Spatial Resolution
10 meter SPOT w/ planimetric overlay
Spatial Resolution
5
1 meter DOQ w/ planimetric overlay
Spatial Resolution
Sub-meter data w/ planimetric overlay
Spatial Resolution
6
Looking More Closely at Resolution
Looking More Closely at Resolution
7
Landsat MSS
Satellite
80 Meter
Resolution Grid
Cell
Looking More Closely at Resolution
Landsat TM
Satellite
30 Meter
Resolution Grid
Cell
Looking More Closely at Resolution
8
SPOT Satellite 10 Meter
Resolution Grid Cell
Looking More Closely at Resolution
IKONOS Satellite
4 Meter Resolution
Grid Cell
Looking More Closely at Resolution
9
IKONOS Satellite
1 Meter Resolution
Grid Cell
Looking More Closely at Resolution
Landsat 7
185 by 170 km
30-m multispectral
Indian Remote
Sensing
145 by 145 km
25-m multispectral
SPOT
60 by 60 km
20-m multispectral
QuickBird 2
16 by 16 km
2.5-m multispectral
IKONOS
11 by 11 km
4-m multispectral
Selected Satellite Footprints
OrbView 3
8 by 8 km
4-m multispectral
1-m panchromatic10
10 100
10 100
10
Resolutions of Remote Sensing
1. Spatial (what area and how detailed)
2. Spectral (what colors – bands)
3. Temporal (time of day/season/year)
4. Radiometric (color depth)
The best spatial resolution?
11
1. ...
Remote Sensing
This document provides an overview of image enhancement techniques for remote sensing. It first defines remote sensing as observing and gathering information about areas without direct contact. It then discusses different types of sensors and gives examples of image processing techniques like enhancement, restoration, and classification. The document demonstrates enhancing a Landsat image by adjusting band combinations and applying contrast stretching. It shows original and enhanced RGB images using different band combinations and concludes by asking if the reader has any questions.
Remote Sensing
Remote sensing involves obtaining information about objects through sensors without direct contact. It has applications in many fields including urban planning, agriculture, forestry, and land use mapping. Some key information obtained from remote sensing includes crop acreage, forest resource mapping, flood damage assessment, and monitoring of changes over time. Advantages include rapid information updating, infrastructure monitoring, and improved decision making for management and planning.
Vicarious Radiometric Calibration
Vicarious radiometric calibration refers to techniques used to calibrate remote sensing data without relying on onboard calibrators. Field spectroradiometers can be used to collect ground reflectance spectra and atmospheric parameters needed for vicarious calibration. Accurate vicarious calibration allows correction of instrument drift over time and comparison of datasets from different sensors, enabling monitoring of climate variables. Portable spectroradiometers like ASD's FieldSpec models are well-suited for rapid collection of calibration target and atmospheric data in the field.
Remote sensing concept and applications
Remote sensing involves collecting data about objects from a distance by detecting electromagnetic radiation. It has several components: an energy source, atmospheric interactions, target interactions, sensor recording, data transmission and processing, interpretation and analysis, and applications. Spatial, spectral, temporal, and radiometric resolutions characterize remote sensing data. Remote sensing is used for applications like urban planning, agriculture, natural resource management, and forestry by providing information on land use, crop growth, environmental changes, and forest resources over time.
Iirs rstechnologypdf
Remote sensing involves collecting information about objects or areas from a distance without making direct contact. It works by sensing and recording reflected or emitted energy and processing, analyzing data. Key points are that it obtains data through passive sensors that sense sunlight reflected by Earth or active sensors like radar that emit and sense their own radiation. Platforms can be ground, airborne or spaceborne. Spaceborne platforms are in either geostationary or polar orbits. [/SUMMARY]
Lrm 111 intro remote sensing lesson iv
Satellite sensors capture reflected electromagnetic radiation from Earth's surface features in different spectral bands. The reflectance values are converted to digital numbers representing pixel values. As the satellite moves forward, the sensors take snapshots called instantaneous fields of view of the surface. Each satellite system has limitations in spectral, spatial, temporal and radiometric resolution. Landsat and SPOT satellites are described in terms of their sensor systems and characteristics.
Basics of Remote Sensing
Remote sensing involves obtaining information about objects without physical contact. It works by sensing and recording electromagnetic radiation reflected or emitted from targets. The key components are an energy source, sensor, platforms, and data analysis to extract information. Sensors can be optical, thermal, or microwave. Platforms include satellites, aircraft, and ground bases. Applications of remote sensing include agriculture, forestry, geology, hydrology, urban planning, and national security.
Als seminar
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rsbasics-150227022648-conversion-gate01.pptx
Natural sensing uses electromagnetic radiation to obtain information about objects without physical contact. It involves sensing, analysis, and extracting knowledge. Remote sensing uses various sensors on different platforms to collect data across the electromagnetic spectrum. The key components are the target area, sensor, interpretation/analysis, energy source, atmosphere, and receivers. Sensors can be passive (depending on external energy) or active (with their own energy source) and operate on different platforms like ground, airborne, or spaceborne. The data has applications in agriculture, forestry, geology, hydrology, urban planning, and more.
Eng remote sensing and image measurement
Remote sensing uses sensors on platforms like satellites or aircraft to collect imagery and geospatial data of the Earth. Various sensors can extract different types of information like color, geometry, or 3D coordinates using principles like stereo imagery. Mathematical models like collinearity equations relate image coordinates to ground coordinates. Sensor position and attitude can be estimated using ground control points. 3D measurements are possible with stereo imagery. Different sensor types exist for applications like vegetation monitoring, land use mapping, and disaster monitoring.
Introduction to Landsat
Landsat is a series of Earth observation satellite missions jointly managed by NASA and the U.S. Geological Survey. The first Landsat satellite was launched in 1972 and subsequent satellites were launched through 2013 to acquire global land data. Landsat satellites carry imaging sensors to collect medium-resolution multi-spectral images of the Earth's surface on a 16-day repeat cycle. The images are used to observe changes in land use, monitor deforestation, and detect water pollution among other applications. Six Landsat satellites have been launched to date, each carrying improved sensors from the Multi-Spectral Scanner to the Enhanced Thematic Mapper Plus. Landsat provides the longest continuous space-based record of Earth's surface.
RS - Presentation by AP2023.pptx
This document provides an overview of key concepts in remote sensing and image processing. It discusses electromagnetic radiation and its interactions with the atmosphere and Earth's surface. Different materials have unique spectral signatures that are detected by sensors to analyze land features from satellite imagery. Image analysis involves examining spatial, spectral and radiometric resolutions. Processing techniques include classifying pixels based on their spectral properties to map and monitor Earth's changing surface over time.
Remote sensing Techniques Dr. Ange Felix NSANZIYERA
It is generally accepted that urbanization involves the shift in population from rural to urban settlements
Introduction to li dar technology advanced remote sensing
This document provides an overview of LiDAR technology presented by Mr. Ashenafi B. It describes the components of a LiDAR system including lasers, scanners, GPS, and high-precision clocks. The principles of LiDAR are explained such as how it measures distance using time of flight and records location and orientation data. Different LiDAR types and platforms like airborne, terrestrial, and satellite are covered. Applications including DEM generation, forest inventory, and landslide analysis are listed. Advantages of LiDAR are high accuracy and weather independence while disadvantages include high costs and lack of foliage penetration.
Wireless Sensor Networks and Drones for Measuring Forest Photosynthetic Bioph...
This document discusses using ground sensor networks and drones together to measure forest photosynthetic biophysics. It provides an overview of wireless sensor networks and how they can be used to monitor forest canopy structure and light absorption. It also discusses how lightweight drones can collect remote sensing data at landscape scales. The document proposes that combining sensor network and drone data allows measuring forest productivity and resilience by relating ecosystem structure observed from drones to photosynthetic function measured by sensor networks. Initial results show sensor network light absorption data can be overlaid on drone imagery to relate ground and aerial remote sensing techniques.
Marselis 2014 Vegetation Structure mapping with LiDAR for forest fire res...
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
在线制作加拿大萨省大学毕业证文凭证书实拍图原版一模一样
原版一模一样【微信：741003700 】【加拿大萨省大学毕业证文凭证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理阿卡迪亚大学毕业证（uvic毕业证）本科文凭证书原版一模一样
原版一模一样【微信：741003700 】【阿卡迪亚大学毕业证（uvic毕业证）本科文凭证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Learnings from Successful Jobs Searchers
Are you interested to know what actions help in a job search? This webinar is the summary of several individuals who discussed their job search journey for others to follow. You will learn there are common actions that helped them succeed in their quest for gainful employment.
一比一原版布拉德福德大学毕业证（bradford毕业证）如何办理
一模一样【微信：A575476】【布拉德福德大学毕业证（bradford毕业证）成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Leadership Ambassador club Adventist module
Aims to equip people who aspire to become leaders with good qualities,and with Christian values and morals as per Biblical teachings.The you who aspire to be leaders should first read and understand what the ambassador module for leadership says about leadership and marry that to what the bible says.Christians sh
All Of My Java Codes With A Sample Output.docx
Contains All of the Java Programs from Beginning to the Advanced Concepts that I had Practiced While Learning Java
Community Skills Building Workshop | PMI Silver Spring Chapter | June 12, 2024
Community Skills Building Workshop | PMI Silver Spring Chapter | June 12, 2024Hector Del Castillo, CPM, CPMM
We recently hosted the much-anticipated Community Skill Builders Workshop during our June online meeting. This event was a culmination of six months of listening to your feedback and crafting solutions to better support your PMI journey. Here’s a look back at what happened and the exciting developments that emerged from our collaborative efforts.
A Gathering of Minds
We were thrilled to see a diverse group of attendees, including local certified PMI trainers and both new and experienced members eager to contribute their perspectives. The workshop was structured into three dynamic discussion sessions, each led by our dedicated membership advocates.
Key Takeaways and Future Directions
The insights and feedback gathered from these discussions were invaluable. Here are some of the key takeaways and the steps we are taking to address them:
• Enhanced Resource Accessibility: We are working on a new, user-friendly resource page that will make it easier for members to access training materials and real-world application guides.
• Structured Mentorship Program: Plans are underway to launch a mentorship program that will connect members with experienced professionals for guidance and support.
• Increased Networking Opportunities: Expect to see more frequent and varied networking events, both virtual and in-person, to help you build connections and foster a sense of community.
Moving Forward
We are committed to turning your feedback into actionable solutions that enhance your PMI journey. This workshop was just the beginning. By actively participating and sharing your experiences, you have helped shape the future of our Chapter’s offerings.
Thank you to everyone who attended and contributed to the success of the Community Skill Builders Workshop. Your engagement and enthusiasm are what make our Chapter strong and vibrant. Stay tuned for updates on the new initiatives and opportunities to get involved. Together, we are building a community that supports and empowers each other on our PMI journeys.
Stay connected, stay engaged, and let’s continue to grow together!
About PMI Silver Spring Chapter
We are a branch of the Project Management Institute. We offer a platform for project management professionals in Silver Spring, MD, and the DC/Baltimore metro area. Monthly meetings facilitate networking, knowledge sharing, and professional development. For more, visit pmissc.org.Gabrielle M. A. Sinaga Portfolio, Film Student (2024)
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Kglc 061709
- 1. Rangeland Condition on the Central Great Plains Multitemporal & Multiscale Remote Sensing M.A. Thesis Geoffrey Folker, M.A.Candidate, G.R.A. Kevin P. Price, Professor Department of Geography and Kansas Applied Remote Sensing (KARS) Program
- 2. Advantages of Aerial Photography • Cost effective and efficient method for collecting data. • Ability to collect data at user specified spatial resolution. • Flexible imaging schedule allows for the collection of data when and where needed.
- 3. Digital Multispectral Imaging System • Spatial Resolution: 0.40 - 1.00 meters • Imaging Area: 1392 x 1040 pixels DT-MS3100 Downtown Lawrence, Kansas
- 4. - Ground based surveys require more time, people, and money - Rangeland was 21% of Total U.S. surface area in 2002 + Remote sensing can cover large areas in a short amount of time + Useable information in less time
- 5. Camera Specifications • Customized DuncanTech MS3100 digital camera • Multispectral camera captures data in three regions of the spectrum *Blue (450-520um) good for penetrating water *Red (630-690um) good for soil and vegetation *Near-Infrared (NIR) (760- 900um) good for measuring chlorophyll in vegetation (greenness) • Resolution of imagery is adjustable depending an altitude 10,000 feet = 1 meter 5,000 feet = 0.5 meters
- 6. Research Goals • Develop and test a Remote Sensing method to assess rangeland condition. • Determine the effects of Rotational vs. Continuous cattle grazing on the condition of mixed grass rangelands.
- 7. Study Area
- 8. Data Collection • Biomass: 07/02, 09/02, 03/03, 07/03, & 09/03 • Aerial VNIR Imagery: 07/02, 09/02, 04/03, 07/03, 10/03, & 05/04 • Cover Estimates 06/04 - 07/04 • On site Spectral Data : 06/04 - 07/04
- 9. Biomass Percentage September, 2003 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 C4 short C4 mid C4 tall C3 SD grass Forb SD forb Woody Succulent Growth Type %ofTotal AT% DT% ET% GT% HT% JT% Biomass by Growth Type
- 12. Range: 13 - 30
- 13. Cover % by Site 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 0 5 10 15 20 25 Site %Cover Live Plants Dead Plant Litter Bare Ground
- 14. % Cover by Growth Type 0.0 10.0 20.0 30.0 40.0 50.0 60.0 A D E G H J Ranch %Cover Grass Forbs Shrubs
- 15. Spectral Response •Duncan Tech VNIR Images •NIR, Red, & Blue bands (TM) •ASD Spectroradiometer (350nm – 2500nm) •Landsat TM (Bands 3Red & 4NIR) NDVI = NIR - Red NIR + Red Source:REMOTE SENSING METHODS FOR PRAIRIE IDENTIFICATION AND INVENTORY IN EASTERN KANSAS Matt Ramspott
- 17. Digital Multispectral Imaging System Three Spectral Bands: •Blue ~(450-530 nm) •Red ~(640-670 nm) •Near Infrared ~(750-850 nm) • Spatial Resolution: 0.40 - 1.00 meters • Imaging Area: 1392 x 1040 pixels DT-MS3100 Downtown Lawrence, Kansas Source:REMOTE SENSING METHODS FOR PRAIRIE IDENTIFICATION AND INVENTORY IN EASTERN KANSAS Matt Ramspott
- 18. Why: Radiance Correction • To correct for changes in F stop and Exposure time – DN array is adjusted in each band independently by the exposure time in DT cameras – No real relationship between the 3 bands in images • NDVI would be meaningless
- 19. How: Radiance Correction • Use calibration to transform DN array for each band to radiance • RAD = S(DN) + I – with S and I calculated as follows: – S = A * t-a and I = B * t-b – A, a, B, b calculated during calibration for each f stop – My project • (20 images X 5 dates) X 2 bands = 200 images corrected
- 20. Invariant NDVI Targets NDVI (Zonal Mean) Invariant Sites 0.10 0.20 0.30 07/01/02 10/31/02 03/02/03 07/02/03 11/01/03 03/02/04 07/02/04 Date NDVI I1 I2
- 21. NDVI Time Series NDVI (Zonal Mean) (all study sites on ranch combined for each date) 0.30 0.40 0.50 0.60 07/01/02 10/31/02 03/02/03 07/02/03 11/01/03 03/02/04 07/02/04 Date NDVI Ranch A Ranch D Ranch E Ranch G Ranch H Ranch J
- 22. From Air to Ground • Comparing aerial NDVI to ground NDVI – Verification of aerial NDVI
- 24. Relative Spectral Reflectance Mean 0.63 - 0.69 (red) Mean 0.76 - 0.90 (nIR) NDVI 0.061232716 0.226967133 0.575067673 Mean Reflectance Site D02 - June 24, 2004 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 350 400 450 500 550 600 650 700 750 800 850 900 950 1000 Wavelength (nm) Reflectance -0.01 0.09 0.19 0.29 0.39 0.49 0.59 0.69 0.79 0.89
- 25. Spectroradiometer NDVI Rank Order by Site 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 A04 E04 H02 G03 A06 H01 G04 D01 E06 G02 E05 A05 D02 J02 A01 A08 J01 J04 E02 A09 Site MeanReflectance
- 26. Remaining Questions? • Do rangeland management practices produce significant measurable differences in vegetation? • Does aerial NDVI suggest differences in vegetation between rangeland management types? • How well does the aerial NDVI data correlate with biophysical data?
- 27. Further Work & Data Analysis • Results presented here are mostly preliminary • Ongoing Data Analysis • Hierarchical Cluster analysis using Functional & Descriptive Attributes • Correlation, simple regression, multiple regression analysis • One-Way Analysis of Variance (ANOVA) to test for significant differences between variables
- 28. Acknowledgements: Thanks to all the following people: Kevin Price, Matt Ramspott, Kelly Kindscher, Mike Houts, Jerry Whistler, Jude Kastens, Matt Dunbar, Craig Freeman, and Ed Martinko, University of Kansas, Lawrence, KS Tanya Folker The Comanche Pool Prairie Resource Foundation Loren Graff NRCS Ron Renz (pilot), GUT Works LLC, Lawrence, KS A special thanks to the ranchers who provided access to their land
Editor's Notes
- The majority o ft he biomass occurs as C4 mid and Forbs.