Earth: Our Green Home

Launched on October 28, 2011, satellite Suomi NPP was flung into a sun-synchronous orbit around earth by a Delta II rocket and has since gathered large amounts of highly detailed data about the amount of vegetation on Earth’s surface. NPP is a mission partnered by both NASA and the National Oceanic and Atmospheric Administration (NOAA) and has been gathering data on the amount of vegetation on Earth through images in visible and near-infrared light. The satellite was able to incorporate the data into the Normalized Difference Vegetational Index (NDVI) which is used to measure the photosynthetic potential of vegetation across the world, as well as plant growth, and biomass production. Some quarter of the Earth is covered in vegetation, while the rest is blue ocean.

Tomorrow I get to dump 10G of Landsat scenes into a 16T super-computer that was built specifically for processing all of the data: 33 scenes each for three study areas, each being unpacked from the tar file, geometrically corrected, radiometrically corrected, atmospherically corrected etc, then processed into NDVI trends omg I want to WATCH. Apparently when all the data are  unpacked and processed each study area will have ~10T of data. All of the processing @_@ 


From NASA Earth Observatory Photo Of The Day; December 18, 2014:

A Pulse of New Life on the Colorado River

A pulse of water released on the lower Colorado River in March 2014 resulted in a significant increase in green vegetation around the river banks downstream. The release of water—an experimental flow implemented under a U.S.-Mexico agreement called “Minute 319”—reversed a 12–year decline in greenness along the Colorado River Delta.

The last time the Colorado River reached the Sea of Cortez (between Mexico’s mainland and Baja California) was 2000. Since then, information from ground measurements and satellites have shown a decline in the amount of healthy vegetation along the lower reaches of the river.

The spring 2014 “pulse flow” brought back some of the green, as shown in the images on this page. The map above, built with data from the Operational Land Imager on Landsat 8, shows changes in the Normalized Difference Vegetation Index, or NDVI, along part of the lower Colorado River in Mexico. Green depicts areas where vegetation was healthier and greener in August 2014—after the pulse flow—than in August 2013. Brown shows areas with less or less healthy vegetation.

When the Minute 319 science team analyzed the NDVI data, they calculated a 43 percent increase in green vegetation along the route wetted by the flow, known as the inundation zone. They also found a 23 percent increase in greening of the riparian zone, the wider area on the river banks.

“The vegetation that desperately needed water was finally able to support more green leaves,” said Pamela Nagler, a scientist at the U.S. Geological Survey (USGS) and a leader of the study. “These are existing trees—like saltcedar, willow, and cottonwood—and a lot of shrubs and grasses that hadn’t seen much water in a long time.” Below you can see the year-over-year landscape change in images from Landsat 8; note how the difference is much more subtle to the eye in natural color than the NDVI analysis.

Image above taken August 12, 2013, below taken August 15, 2014

Although most of the water soaked into the ground in the first 60 kilometers (37 miles) below the dam, the river’s surface flow reached areas farther downstream that had been targeted for restoration. Groundwater revived vegetation along the entire route to the sea.

The Minute 319 pulse was part of an agreement adopted by the International Boundary and Water Commission under the framework of a 1944 U.S.–Mexico treaty that governs water allocations on the Colorado River. The 2012 agreement prescribed 130 million cubic meters (105,000 acre feet) of water to flow through Morelos Dam,which straddles the border.

“Remote sensing with satellites such as Landsat and sensors such as MODIS allows scientists to conduct a range of studies they wouldn’t otherwise be able to,” said Karl Flessa, part of the Minute 319 Science Team and a geosciences professor at the University of Arizona. It’s just one of the tools scientists are using, along with on-the-ground monitoring, to detect changes in the river channel, surface water, groundwater, plant growth, and habitat for resident marsh birds and migratory birds.

Using greenness data collected both from the ground and from satellites, researchers will investigate the long-term impacts to groundwater, and they’ll continue to study whether new trees and shrubs take root due to the flow. They will also study how the new vegetation affects birds migrating along the Pacific Flyway.

“There’s hope that we could release a pulse of water below Morelos Dam again,” Flessa said.


NASA Earth Observatory images by Jesse Allen, using Landsat data from the U.S. Geological Survey; Landsat is a joint project of NASA and USGS. Caption by Kate Ramsayer, with Mike Carlowicz; Instrument(s): Landsat 8 - OLI

Infragram Webcam Lets You Take Infrared Photos Of Plants Like NASA

Infragram Webcam Lets You Take Infrared Photos Of Plants Like NASA

The new Infragram Webcam was recently released as a beta for $55; now, as anyone could have guessed, this is not an average webcam. In fact, the technology, which has a pixel resolution of 1600X1200, allows any photographer the ability to analyze the health of their plants.

The technology comes from the folks at Public Lab, “an open network of collaborators who develop affordable environmental…

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quick example showing the importance of surface reflectance correction for Landsat 8 imagery (and most other satellites’ imagery too).

at the top, surface reflectance-corrected NDVI along a swath of western Israel. productive vegetation really pops out here.

at the bottom, NDVI without any surface reflectance correction. highs are washed with mediums, and lows look even lower.

Differences in shades of green in this photo reflect vegetation conditions worldwide. High values represent dense green funtioning vegetation, and low values represent sparse green vegetation. Mediterranean and Aegean parts of Turkey are my master thesis study area😏
NDVI adı verilen bir ölçü birimi diyebiliriz buna. Temel olarak gezegen üzerindeki bitki örtüsünü temsil ediyor. Türkiye'nin akdeniz ve Ege bölgelerinde görülen yeşil yerler benim yüksek lisans tez çalışma alanım.
#NASA #vegetation #satellite #map #amazing #book #bilim #biology #biyoloji #beautiful #cool #cute #colorful #conservation #doğa #dünya #earth #ecology #quotes #ocean #insect #good #bird #gorgeous #kitap #love #learn #nature #passion #inspiration

Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration

Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration

Biogeosciences, 12, 3885-3897, 2015

Author(s): P. Choler

A remote sensing approach is used to examine the direct and indirect effects of snow cover duration and weather conditions on the growth response of mountain grasslands located above the tree line in the French Alps. Time-integrated Normalized Difference Vegetation Index (NDVIint), used as a surrogate for aboveground primary productivity, and snow cover duration were derived from a 13-year long time series of the Moderate-resolution Imaging Spectroradiometer (MODIS). A regional-scale meteorological forcing that accounted for topographical effects was provided by the SAFRAN–CROCUS–MEPRA model chain. A hierarchical path analysis was developed to analyze the multivariate causal relationships between forcing variables and proxies of primary productivity. Inter-annual variations in primary productivity were primarily governed by year-to-year variations in the length of the snow-free period and to a much lesser extent by temperature and precipitation during the growing season. A prolonged snow cover reduces the number and magnitude of frost events during the initial growth period but this has a negligible impact on NDVIint as compared to the strong negative effect of a delayed snow melting. The maximum NDVI slightly responded to increased summer precipitation and temperature but the impact on productivity was weak. The period spanning from peak standing biomass to the first snowfall accounted for two-thirds of NDVIint and this explained the high sensitivity of NDVIint to autumn temperature and autumn rainfall that control the timing of the first snowfall. The ability of mountain plants to maintain green tissues during the whole snow-free period along with the relatively low responsiveness of peak standing biomass to summer meteorological conditions led to the conclusion that the length of the snow-free period is the primary driver of the inter-annual variations in primary productivity of mountain grasslands.

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How to download multiple MODIS NDVI images for time series

This short tutorial shows the important steps to create a bulk download of multiple MODIS satellite imagery from earth explorer


I’ve just completed a big analysis of changes in NDVI (normalized difference vegetation index) across the West Bank from 1973 through 2013. I used Landsat 1, 2, 4, 5, 7, and 8 data – whew! – as well as high-resolution (1-8m) imagery from five different commercial systems. In total, nearly 50 images spanning 40 years were used to map changes in the West Bank’s vegetative productivity.

Here, I show the two dates bookending the study, 1973 and 2013. Though a direct comparison between the two maps is made difficult by differing radiometric, spectral, and spatial resolutions between Landsat 1 and 8 imagery, general trends are still apparent. The most striking difference is that vegetation within the West Bank appears to be much more productive in 2013 than 1973, almost on par with that to west of the West Bank.

Next steps include a full suite of inter-sensor calibration between the 11 different systems used in the study, consideration for the influence of rainfall events on NDVI patterns, and integrating MODIS and AVHRR imagery for monthly NDVI composites across the study area.


How to calculate NDVI with ArcMap 10.1

In this video I use ArcMap 10.1 with a 4 band Aerial Photo (RGBiR) to calculate the Normalized Difference Vegetation Index (NDVI), a graphical indicator that can be used to analyze remote sensing measurements to assess whether the target being observed contains live green vegetation or not. I use Spatial Analyst extension along with Raster Calculator in the Map Algebra Toolbox.