Lake Tahoe – Land Use Land Cover Analysis – 1997 & 2024

 Lake Tahoe – Land Use Land Cover Analysis – 1997 & 2024

(Geographic Information System – Remote Sensing Final)

Lake Tahoe, the largest alpine lake in North America, bordering California and Nevada has become increasingly eutrophic (having an excessive richness of nutrients) over the past 3 decades.  Nutrients have increased 5% annually and clarity have decreased at an average rate of 0.25 meters per year.  The Lake Tahoe region has undergone rapid urbanization over the past 3 decades and fine sediment, resulting from land disturbance in the basin, accounts for about half of lake contamination resulting in the lakes loss of clarity.    The largest source of fine sediment particles to Lake Tahoe is suspected to be from urban upland storm-water runoffs, comprising 73% of the total fine sediment particle load.  It is also projected that historic clarity, of approximately 30 meters, can be achieved by reducing approximately 75% of fine sediment from urban sources.  The goal of the Lake Tahoe – Land Use Land Cover (LULC) Analysis is to provide Lake Tahoe land managers historic LULC classification data, documented in attachment 1 (Map 1 - 1997) to present,  documented in attachment 2 (Map 2 – 2024), to help land managers plan for and upgrade storm water runoff infrastructure in an effort to reduce contamination and improve Lake Tahoe's water clarity and to document the efforts and process of the analysis.

               The process started by creating Map 1.  Map 1 was created from the National Land Cover Dataset (NLCD_nad27) from 1992 data file to include a data image and shape files updated in 1997.  Second, a few minor changes were made to correct feature classifications using techniques learned in lab 1.  Lab 1 visual interpretation techniques were used to view the image by patterns and association and compared with open data source from Tahoe Parcels Data | Tahoe Open Data .  Lake Tahoe’s Lake was found to be incorrectly classified, and the lakes attribution data and symbology was incorrectly placed in the vegetation shape file.  To correct the error a new feature class of lake was created, and Lake Tahoe was placed under the new feature class and symbology was updated.  Third, using knowledge, concepts and techniques learned in lab 2, Land Use Land Cover (LULC) and lab 5, Unsupervised and Supervised Classification, a re-coding of the LULC data re-code was performed to narrow LULC classifications to 5 classifications and symbology of each recoded feature class was updated.  Finally, a map layout of the updated information was created, providing a historical reference.

Map 2 – was created using landsat5_July6th2010 provided data set.  First the file was uploaded to the ERDAS software suite, using techniques and tools learned in lab 3, Image Spatial Enhancement, Multispectral Data and Band Indices, several viewing tools were used to analyze the image, and meta data was also viewed.  Additionally, the masking tool was used to create a one-layer image of the file, the image was uploaded to ArcGIS pro and a Composite Band image was created.  Next , the El_Dorado_Parcels Shape file published 5 October 2017 and  last updated 1 August 2024, datasets  containing updated Lake Tahoe parcel information, was downloaded from the Tahoe Regional Planning Agency at URL:   Tahoe Parcels Data | Tahoe Open Data.  Next, a new map and layout was created in ARCGis to include the El_Dorado_Pacels dataset and common data sets provided from 1997 using same or similar tools and techniques to create map 1. 

Module 5 Germantown Maryland Land Use Land Cover (LULC)

The map represents the city of German town, Maryland and it's current Land Use Land Cover (LU/LC) .  Maryland population increased by 30 percent while land consumption increased by 100 percent.  The map use case is intended to help the city of of German town work to "Smart Green and Growing Initiative".  

Module 4: Spatial Enhancement, Multispectral Data and Band Indices

Mod 4: Spatial Enhancement, Multispectral Data, and Band Indices

In this module, ERDAS Imagine was used to analyze different features under different band combinations. Below are 3 maps showcasing the features (water, water depth, and snow/ice.) under specific band combinations.

The feature highlighted in the map above is water. The False Color IR (R=4, G=3, B=2) band combination was used because the black stood out against the red.

The feature highlighted in the map above is water depth. A Custom Color IR (R=4, G=2, B=1) band combination was used because it made the difference in water depths distinguishable; showing shallow water as light blue and deeper water a darker blue.

The feature highlighted in the map above is snow and ice. The False Natural Color (R=5, G=4, B=3) band combination was used because it caused the snow and ice caps to show up as a bright blue color.

Washington State Advanced Very High Resolution Radiometer (AVHRR)

The ArcGIS Pro map consist of an image edited and imported ERDAS IMAGINE a geospatial data authoring system.   The map shows a small portion of Washington in a Advanced Very High Resolution Radiometer (AVHRR) view.  The map displays six classification and it's area unit to include Water, South East Vegetation, North West Vegetation, Riparian, Cloud and Bare ground.

Photo Sensing Module 2

In Module 2 I learned that the United States Geographical Survey created a Land Use / Land Cover (LU/LC) classification for remote sensor data collection and is used to promote uniformity of feature classifications. 

During lab 2 features of Pascagoula, Mississippi were identified, the features were then evaluated at LU/LC classification level II based on criteria such as, size, shape, color, texture, density and knowledge.  Next the attribute table was updated with the LU/LC code, code name, and then a description was provided.  Finally, a map was created to show Level II Land Use / Land Cover for the area of Pascagoula, Mississippi.  The the map created was compared to Google Earth to compare classification selection accuracy.  The comparison showed that 87% Land Use / Land Cover classification were accurately selected.

Remote Photo Sensing Module 1

In exercise 1, An aerial view of an area was provided and analyzed for different tones and textures. Then the polygon feature class was used to analyze the tones. Then repeated the steps with a point feature class to analyze textures.

In exercise 2, I analyzed different features on the provided .tif file, shadows, shape and size, patterns, and association. I did them by creating a point feature class. For shape and size I chose a building with a flat roof, a road, and a cluster of vehicles because they were easy to recognize by the shape and size. For the shadow feature, I chose a house, a building and the water tower because the shadows were easy to distinguish. For association I chose shoreline, pier, and the forest because they are easily distinguishable items. For the pattern category I chose a parking lot, a row of houses and a shoreline because they are distinguishable on the by its pattern on the map.

 

Bobwhite-Manatee Project

Florida Power and Light (FPL) plans to install a transmission line to support the growing Manatee and Sarasota County community.  

FPL has proposed a transmission line route to provide minimum impact on the environment, homes, and schools throughout the community.  In this intro2GIS assignment, arcGIS was utilized to analyze impacts on the environment, homes, and schools and to provide a cost analysis of the transmission line installation.  The data was used to confirm if FPL provided a feasible recommendation.

 https://docs.google.com/presentation/d/1-PQ7Ff7x3wbi4hJqlRyeZ1HUwLqNRlVW/edit?usp=sharing&ouid=109183053771690455279&rtpof=true&sd=true