Analysis Ready Data: Enabling Analysis of the Landsat Archive

Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and

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Remote Sensing Application for Exploring Land Use and Land Cover Dynamics in and Around Chatra Wetland, English Bazar, West Bengal

CREATIVE SPACE ◽

10.15415/cs.2021.91002 ◽

2021 ◽

Vol 9 (1) ◽

pp. 15-27

Author(s):

Saleha Jamal ◽

Md Ashif Ali

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Land Use Changes ◽

United States Geological Survey ◽

Landsat 8 ◽

Gis And Remote Sensing ◽

Erdas Imagine ◽

Landsat 7 ◽

Vegetation Water

Wetlands are often called as biological “supermarket” and “kidneys of the landscape” due to their multiple functions, including water purification, water storage, processing of carbon and other nutrients, stabilization of shorelines and support of aquatic lives. Unfortunately, although being dynamic and productive ecosystem, these wetlands have been affected by human induced land use changes. India is losing wetlands at the rate of 2 to 3 per cent each year due to over-population, direct deforestation, urban encroachment, over fishing, irrigation and agriculture etc (Prasher, 2018). The present study tries to investigate the nature and degree of land use/land cover transformation, their causes and resultant effects on Chatra Wetland. To fulfil the purpose of the study, GIS and remote sensing techniques have been employed. Satellite imageries have been used from United States Geological Survey (USGS) Landsat 7 Enhanced Thematic Mapper plus and Landsat 8 Operational Land Imager for the year 2003 and 2018. Cloud free imageries of 2003 and 2018 have been downloaded from USGS (https://glovis.usgs.gov/) for the month of March and April respectively. Image processing, supervised classificationhas been done in ArcGis 10.5 and ERDAS IMAGINE 14. The study reveals that the settlement hasincreased by about 90.43 per cent in the last 15 years around the Chatra wetland within the bufferzone of 2 Sq km. Similarly agriculture, vegetation, water body, swamp and wasteland witnessed asignificant decrease by 5.94 per cent, 57.69 per cent, 26.64 per cent 4.52 per cent and 55.27 per centrespectively from 2003 to 2018.

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Comparison between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Assessment of Vegetation Indices

Nigerian Journal of Environmental Sciences and Technology - March 2018 ◽

10.36263/nijest.2017.02.0046 ◽

2017 ◽

Vol 1 (2) ◽

pp. 355-366

Author(s):

E. O. Makinde ◽

A. D. Obigha

Keyword(s):

Statistical Analysis ◽

Land Cover ◽

Vegetation Index ◽

Vegetation Indices ◽

Thematic Mapper ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Water Index ◽

Landsat 7 ◽

Index 2

The Landsat system has contributed significantly to the understanding of the Earth observation for over forty years. Since May 2013, data from Landsat 8 has been available online for download, with substantial differences from its predecessors, having an extended number of spectral bands and narrower bandwidths. The objectives of this research were majorly to carry out a cross comparison analysis between vegetation indices derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and also performed statistical analysis on the results derived from the vegetation indices. Also, this research carried out a change detection on four land cover classes present within the study area, as well as projected the land cover for year 2030. The methods applied in this research include, carrying out image classification on the Landsat imageries acquired between 1984 – 2016 to ascertain the changes in the land cover types, calculated the mean values of differenced vegetation indices derived from the four land covers between Landsat 7 ETM+ and Landsat 8 OLI. Statistical analysis involving regression and correlation analysis were also carried out on the vegetation indices derived between the two sensors, as well as scatter plot diagrams with linear regression equation and coefficients of determination (R2). The results showed no noticeable differences between Landsat 7 and Landsat 8 sensors, which demonstrates high similarities. This was observed because Global Environmental Monitoring Index (GEMI), Improved Modified Triangular Vegetation Index 2 (MTVI2), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) and Land Surface Water Index (LSWI) had smaller standard deviations. However, Renormalized Difference Vegetation Index (RDVI), Anthocyanin Reflectance Index 1 (ARI1) and Anthocyanin Reflectance Index 2 (ARI2) performed relatively poorly because their standard deviations were high. the correlation analysis of the vegetation indices that both sensors had a very high linear correlation coefficient with R2 greater than 0.99. It was concluded from this research that Landsat 7 ETM+ and Landsat 8 OLI can be used as complimentary data.

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PEMETAAN DISTRIBUSI TOTAL SUSPENDED SOLID DAN PERUBAHAN GARIS PANTAI DI SIDOARJO-PASURUAN DENGAN MENGGUNAKAN DATA PENGINDERAAN JAUH

GEOMATIKA ◽

10.24895/jig.2020.26-1.1025 ◽

2020 ◽

Vol 26 (1) ◽

pp. 25

Author(s):

Niken Dwi Wijayanti

Keyword(s):

United States ◽

Marine Environment ◽

Total Suspended Solid ◽

Suspended Solid ◽

United States Geological Survey ◽

Landsat 8 ◽

Environment Monitoring ◽

Landsat 7 ◽

Marine Environment Monitoring ◽

Monitoring Service

Perairan Porong merupakan daerah muara sungai yang mengalami proses sedimentasi akibat bermuaranya air Sungai Porong ke Selat Madura yang membawa sedimen. Hal tersebut diduga akan menyebabkan terjadinya perubahan garis pantai yang ada di sekitarnya. Disamping itu, perubahan morfologi daratan seperti abrasi atau sedimentasi dipengaruhi oleh faktor oseanografi fisik seperti arus. Penelitian ini bertujuan untuk memahami pengaruh arus terhadap distribusi Total Suspended Solid (TSS) serta dampaknya terhadap perubahan garis pantai di Perairan Sidoarjo-Pasuruan. Data yang digunakan yaitu citra Landsat 7 (2002) dan Landsat 8 (2013 dan 2017) yang diperoleh dari United States Geological Survey serta data arus dari Copernicus Marine Environment Monitoring Service. Penginderaan jauh digunakan untuk menganalisa perubahan garis pantai dan distribusi TSS. Hasil penelitian menunjukkan arus, dengan kecepatan 0.02-0.1 m/s, di Perairan Sidoarjo-Pasuruan berpengaruh terhadap distribusi TSS dengan arah menuju Barat dan Barat Laut. Konsentrasi TSS yang tinggi di perairan dekat pantai menyebabkan terjadinya perubahan garis pantai yang ditandai dengan tingginya sedimentasi di lokasi tersebut. Lebih lanjut hasil menunjukkan bahwa perubahan garis pantai di Sidoarjo-Pasuruan tahun 2002-2013 sebesar 9,305 km dan 2013-2017 sebesar 3,226 km. Peningkatan konsentrasi TSS di Perairan Sidoarjo-Pasuruan sebanding dengan penambahan garis pantai.

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Local Search for Optimal Global Map Generation Using Mid-Decadal Landsat Images

AI Magazine ◽

10.1609/aimag.v30i2.2233 ◽

2009 ◽

Vol 30 (2) ◽

pp. 84 ◽

Cited By ~ 1

Author(s):

Lina Khatib ◽

Robert A. Morris ◽

John Gasch

Keyword(s):

Local Search ◽

The United States ◽

Thematic Mapper ◽

Sensor Data ◽

United States Geological Survey ◽

Landsat Images ◽

Remote Sensor ◽

Map Generation ◽

Landsat 7 ◽

Generation Problem

NASA and the United States Geological Survey (USGS) are collaborating to produce a global map of the Earth using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) remote sensor data from the period of 2004 through 2007. The map is comprised of thousands of scene locations and, for each location, there are tens of different images of varying quality to chose from. Constraints and preferences on map quality make it desirable to develop an automated solution to the map generation problem. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. The paper also describes the integration of a GMG solver into a graphical user interface for visualizing and comparing solutions, thus allowing for solutions to be generated with human participation and guidance.

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Constructing a Large-scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

10.36227/techrxiv.14365502 ◽

2021 ◽

Author(s):

savinay nagendra ◽

srikanth banagere manjunatha ◽

daniel kifer ◽

te pei ◽

weixin li ◽

...

Keyword(s):

United States ◽

Large Scale ◽

Satellite Images ◽

The United States ◽

Specific Model ◽

Google Earth ◽

Landslide Inventory ◽

United States Geological Survey ◽

Heterogeneous Environments ◽

Extensive Data

We use the landslide inventory database provided by the United States Geological Survey. USGS maintains a database of landslide reports with approximate locations and times, but no images. This is the most extensive data of its kind. We extract satellite images from Google Earth by using this inventory.

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Detection of Land Use Land Cover Changes Using Remote Sensing and GIS Techniques in a Secondary City in Bangladesh

Grassroots Journal of Natural Resources ◽

10.33002/nr2581.6853.040311 ◽

2021 ◽

Vol 4 (3) ◽

pp. 132-146

Author(s):

Md. Lutfor Rahman ◽

Syed Hafizur Rahman

Keyword(s):

Land Use ◽

Land Cover ◽

The United States ◽

United States Geological Survey ◽

Land Use Land Cover ◽

Error Matrix ◽

Barren Land ◽

Landsat 7 ◽

Landsat Satellite ◽

Secondary City

This study aims at classifying land use land cover (LULC) patterns and detect changes in a 'secondary city' (Savar Upazila) in Bangladesh for 30 years i.e., from 1990 to 2020. Two distinct sets of Landsat satellite imagery, such as Landsat Thematic Mapper (TM) 1990 and Landsat 7 ETM+ 2020, were collected from the United States Geological Survey (USGS) website. Using ArcMap 10.3, the maximum likelihood algorithm was used to perform a supervised classification methodology. The error matrix and Kappa Kat were done to measure the mapping accuracy. Both images were classified into six separate classes: Cropland, Barren land, Built-up area, Vegetation, Waterbody, and Wetlands. From 1990 to 2020, Cropland, Barren land, Waterbody, and Wetlands have been decreased by 30.63%, 11.26%, 23.54%, and 21.89%, respectively. At the same time, the Built-up area and Vegetation have been increased by 161.16% and 5.77%, respectively. The research revealed that unplanned urbanization had been practiced in the secondary city indicated by the decreases in Cropland, Barren land, Wetland, and Waterbody, which also showed direct threats to food security and freshwater scarcity. An increase in Vegetation (mostly homestead vegetation) indicates some environment awareness programs that encourage people to maintain homestead and artificial gardens. The study argues for the sustainable planning of a secondary city for a developing country's future development.

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Land Use Land Cover Change Detection and Measuring Horticulture Expansion through Remote Sensing and GIS Techniques in Rambiara Catchment, Kashmir Valley, India

CREATIVE SPACE ◽

10.15415/cs.2021.82004 ◽

2021 ◽

Vol 8 (2) ◽

pp. 49-56

Author(s):

Vajahat Khursheed ◽

Mohammad Taufique

Keyword(s):

Land Use ◽

Land Cover ◽

Supervised Classification ◽

Large Scale ◽

Landsat 8 ◽

Land Use Land Cover ◽

Multispectral Images ◽

Kashmir Valley ◽

Jammu And Kashmir ◽

Landsat 7

Horticulture industry is backbone of the economy of the Jammu and Kashmir, it has increased spontaneously from a recent couple of decades and had immensely impacted the socio-economic conditions of the inhabitants of the Rambiara Catchment. The study aimed to identify the varied land use and land cover categories prevailing over the Rambiara catchment and attempted to study the temporal changes. Multispectral images of the Landsat 7 and Landsat 8 were brought into use by making the LULC classes through the maximum supervised classification for the images of year 1999 and year 2019. Whole the study area was classified into eight major land cover categories i.e., Horticulture, Settlement, Water, Riverbed, Dense Forests, Sparce Forests and Waste Lands. The results obtained depicted that there was a large-scale positive change observed by the land cover categories of Horticulture +172.67 percent, Settlement +112.06 percent and sparse forest by +28.44 percent. The horticulture remained the highest achiever over the last 20 years and this is because of the high cash value realized from fruits, less agricultural production obtained from crops other than fruits and also due to changing climatic.

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Land cover change impacts on atmospheric chemistry: simulating projected large-scale tree mortality in the United States

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-2323-2016 ◽

2016 ◽

Vol 16 (4) ◽

pp. 2323-2340 ◽

Cited By ~ 13

Author(s):

Jeffrey A. Geddes ◽

Colette L. Heald ◽

Sam J. Silva ◽

Randall V. Martin

Keyword(s):

United States ◽

Land Use ◽

Air Quality ◽

Land Cover ◽

Land Cover Change ◽

Dry Deposition ◽

Large Scale ◽

Tree Mortality ◽

The United States ◽

Tropospheric Chemistry

Abstract. Land use and land cover changes impact climate and air quality by altering the exchange of trace gases between the Earth's surface and atmosphere. Large-scale tree mortality that is projected to occur across the United States as a result of insect and disease may therefore have unexplored consequences for tropospheric chemistry. We develop a land use module for the GEOS-Chem global chemical transport model to facilitate simulations involving changes to the land surface, and to improve consistency across land–atmosphere exchange processes. The model is used to test the impact of projected national-scale tree mortality risk through 2027 estimated by the 2012 USDA Forest Service National Insect and Disease Risk Assessment. Changes in biogenic emissions alone decrease monthly mean O3 by up to 0.4 ppb, but reductions in deposition velocity compensate or exceed the effects of emissions yielding a net increase in O3 of more than 1 ppb in some areas. The O3 response to the projected change in emissions is affected by the ratio of baseline NOx : VOC concentrations, suggesting that in addition to the degree of land cover change, tree mortality impacts depend on whether a region is NOx-limited or NOx-saturated. Consequently, air quality (as diagnosed by the number of days that 8 h average O3 exceeds 70 ppb) improves in polluted environments where changes in emissions are more important than changes to dry deposition, but worsens in clean environments where changes to dry deposition are the more important term. The influence of changes in dry deposition demonstrated here underscores the need to evaluate treatments of this physical process in models. Biogenic secondary organic aerosol loadings are significantly affected across the US, decreasing by 5–10 % across many regions, and by more than 25 % locally. Tree mortality could therefore impact background aerosol loadings by between 0.5 and 2 µg m−3. Changes to reactive nitrogen oxide abundance and partitioning are also locally important. The regional effects simulated here are similar in magnitude to other scenarios that consider future biofuel cropping or natural succession, further demonstrating that biosphere–atmosphere exchange should be considered when predicting future air quality and climate. We point to important uncertainties and further development that should be addressed for a more robust understanding of land cover change feedbacks.

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Multi-criteria parameter estimation for the unified land model

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-4417-2012 ◽

2012 ◽

Vol 9 (4) ◽

pp. 4417-4463 ◽

Cited By ~ 3

Author(s):

B. Livneh ◽

D. P. Lettenmaier

Keyword(s):

United States ◽

Remote Sensing ◽

Parameter Estimation ◽

Land Surface ◽

Large Scale ◽

The United States ◽

Limiting Factor ◽

Surface Model ◽

United States Geological Survey ◽

Skill Scores

Abstract. We describe a parameter estimation framework for the Unified Land Model (ULM) that utilizes multiple independent data sets over the Continental United States. These include a satellite-based evapotranspiration (ET) product based on MODerate resolution Imaging Spectroradiometer (MODIS) and Geostationary Operation Environmental Satellites (GOES) imagery, an atmospheric-water balance based ET estimate that utilizes North American Regional Reanalysis (NARR) atmospheric fields, terrestrial water storage content (TWSC) data from the Gravity Recovery and Climate Experiment (GRACE), and streamflow (Q) primarily from the United States Geological Survey (USGS) stream gauges. The study domain includes 10 large-scale (≥105 km2) river basins and 250 smaller-scale (<104 km2) tributary basins. ULM, which is essentially a merger of the Noah Land Surface Model and Sacramento Soil Moisture Accounting model, is the basis for these experiments. Calibrations were made using each of the criteria individually, in addition to combinations of multiple criteria, with multi-criteria skill scores computed for all cases. At large-scales calibration to Q resulted in the best overall performance, whereas certain combinations of ET and TWSC calibrations lead to large errors in other criteria. At small scales, about one-third of the basins had their highest Q performance from multi-criteria calibrations (to Q and ET) suggesting that traditional calibration to Q may benefit by supplementing observed Q with remote sensing estimates of ET. Model streamflow errors using optimized parameters were mostly due to over (under) estimation of low (high) flows. Overall, uncertainties in remote-sensing data proved to be a limiting factor in the utility of multi-criteria parameter estimation.

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Land cover change impacts on atmospheric chemistry: simulating projected large-scale tree mortality in the United States

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-29303-2015 ◽

2015 ◽

Vol 15 (20) ◽

pp. 29303-29345

Author(s):

J. A. Geddes ◽

C. L. Heald ◽

S. J. Silva ◽

R. V. Martin

Keyword(s):

United States ◽

Land Use ◽

Air Quality ◽

Land Cover ◽

Land Cover Change ◽

Dry Deposition ◽

Large Scale ◽

Tree Mortality ◽

The United States ◽

Tropospheric Chemistry

Abstract. Land use and land cover changes impact climate and air quality by altering the exchange of trace gases between the Earth's surface and atmosphere. Large-scale tree mortality that is projected to occur across the United States as a result of insect and disease may therefore have unexplored consequences for tropospheric chemistry. We develop a land use module for the GEOS-Chem global chemical transport model to facilitate simulations involving changes to the land surface, and to improve consistency across land–atmosphere exchange processes. The model is used to test the impact of projected national-scale tree mortality risk through 2027 estimated by the 2012 USDA Forest Service National Insect and Disease Risk Assessment. Changes in biogenic emissions alone decrease monthly mean O3 by up to 0.4 ppb, but reductions in deposition velocity compensate or exceed the effects of emissions yielding a net increase in O3 of more than 1 ppb in some areas. The O3 response to emissions is controlled by the ratio of baseline NOx : VOC concentrations, suggesting that in addition to the degree of land cover change, tree mortality impacts depend on whether a region is NOx-limited or NOx-saturated. Consequently, air quality (as diagnosed by the number of days that average 8 h O3 exceeds 65 ppb) improves in polluted environments where changes in emissions are more important than changes to dry deposition, but worsens in clean environments where changes to dry deposition are the more important term. Biogenic secondary organic aerosol loadings are significantly affected across the US, decreasing by 5–10 % across many regions, and by more than 25 % locally. Tree mortality could therefore impact background aerosol loadings by between 0.5 to 2 μg m−3. Changes to reactive nitrogen oxide abundance and partitioning are also locally important. These simulations suggest that changes in biosphere–atmosphere exchange must be considered when predicting future air quality and climate. We point to important uncertainties and further development that should be addressed for a more robust understanding of land cover change feedbacks.

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