I’ve been looking through my Leaflet mapping code, and realized the system by default lists over 100 layers. That’s probably too much and the way the code is written in an incremental piece-by-piece fashion, it’s really hard to maintain. So I am working on converting it over a flat-file CSV spreadsheet, then I can just iterate through that and pull the layers I actually need or think would be useful for a map. Also, I want more flexibility on base layers vs overlays — often there are layers I would like to add in a dual pane mode, but can’t do under the current design of code, such as National Land Cover vs Aerial Photo or NLCD 2001 vs 2016 to better track land use changes.
In our photo-saturated world, it’s natural to think of the images on NASA’s Earth Observatory website as snapshots from space. But most aren’t. Though they may look similar, photographs and satellite images are fundamentally different. A photograph is made when light is focused and captured on a light-sensitive surface such as film or a charge-coupled device in a digital camera. A satellite image is created by combining measurements of the intensity of certain wavelengths of light that are visible and invisible to human eyes.
Why does the difference matter? When we see a photo where the colors are brightened or altered, we think of it as artful (at best) or manipulated (at worst). We also have that bias when we look at satellite images that don’t represent the Earth’s surface as we see it. “That forest is red,” we think, “so the image can’t possibly be real.”
In reality, a red forest is just as real as a dark green one. Because satellites collect information beyond what human eyes can see, images made from other wavelengths of light look unnatural to us. We call these false-color images. To understand what they mean, it’s necessary to understand exactly what a satellite image is.
CIR Imagery is good at penetrating atmospheric haze and for determining the health of vegetation; the pigment in plant leaves, chlorophyll, strongly absorbs visible light. The cell structure of the leaves, on the other hand, strongly reflects near-infrared light. Therefore, the stronger the light sensed by the camera, the healthier the plant is.
NYS GIS Clearinghouse posted 2020 aerials from Oswego, Lewis, Jefferson, St. Lawrence, Franklin, Clinton — Chemung and Steuben – Nassau and Suffolk today. They previously posted Monroe and GLOW counties.
QGIS 3.18 introduces a host of enhancements and new features, along with a long-awaited feature - Native Point Cloud support in QGIS! Thanks to the efforts of Lutra, North Road, and Hobu, QGIS is now able to import and render point cloud data in various formats by leveraging the Open Source PDAL library. This functionality has been introduced due to the success of a crowd-funding campaign and would not have been possible without the support of the QGIS community and contributors. Thank you to all those involved in realizing this incredible milestone!
As QGIS Desktop 3.18 bids farewell to the DB2 database provider, it introduces support for users of the SAP HANA database system.
The Hillshade tool obtains the hypothetical illumination of a surface by determining illumination values for each cell in a raster. It does this by setting a position for a hypothetical light source and calculating the illumination values of each cell in relation to neighboring cells. It can greatly enhance the visualization of a surface for analysis or graphical display, especially when using transparency.
By default, shadow and light are shades of gray associated with integers from 0 to 255 (increasing from black to white).
