Week Fourteen Remote sensing of vegetation Remote sensing of water Significance Spectral characteristics of veg. Temporal characteristics of veg. Veg. indices Landscape ecology metrics Biodiversity and GAP analysis Remote sensing of water Spectral properties of water Spectral properties of suspended minerals Bathymetry – optical, sonar, lidar Precipitation

Significance Different types of veg. distinguished by: Leaf shape, size, density Plant shape Planting density / spacing Water content Soil type Crop monitoring for productivity: Crop type Crop health Stage of growth Predicted yields

Spectral characteristics of veg.

Spectral characteristics of veg. From 400 to 700 nm, veg. is dark in visible: high absorption of pigments (chlorophyll) Plants absorb blue and red better than green (550 nm). From 700 to 1200 nm (near IR), veg. is bright: Scattered & reflected in spongy mesophyll (cell wall / air contact) E.g., 76% reflected at 900 nm If plants absorbed 700 to 1200 nm, they would sizzle From 1300 to 2500 nm, veg. is dark: water absorbs these wavelengths

Spectral characteristics of veg. Natural color on left, color near infrared on right. Source: Your book.

Spectral characteristics of veg. Less chlorophyll means less reflectance (600 – 700 nm). Source: Your book.

Temporal characteristics of veg. Phenological cycles viewed as pixels in attribute space – Near IR vs. red Dancing pixels: Near IR vs. red. Image source: Your book.

Vegetation indices TM band 4 is near-IR; 1 blue, 2 green, 3 red Reflectance curves for the upper leaf surface of the seven selected weed and crop species; the wavelengths within the Landsat Thematic Mapper satellite sensor wavebands 1, 2, 3, 4, 5, and 7 are indicated by the shaded areas. Figure from: 2003. ANNE M. SMITH, ROBERT E. BLACKSHAW, Weed–Crop Discrimination Using Remote Sensing: A Detached Leaf Experiment, Weed Technology 2003 17 (4), 811-820

Vegetation indices Normalized Difference Vegetation Index (NDVI) of AZ 3/14/2002. [NDVI = (NIR – Red) / (NIR + Red)] Source: http://rangeview.arizona.edu/Tutorials/intro.asp

Landscape ecology metrics The study of structure, function, and changes in heterogeneous land areas composed of interacting organisms (pg. 393). Landscape ‘integrity’ can be monitored through indicators: Land cover composition and pattern Riparian extent and composition Ground water Greenness pattern Degree of biophysical contraints Erosion potential Managing ecosystems, communities, landscapes. Mixes scales.

Landscape ecology metrics

Biodiversity and gap analysis Looks at multiple species Looks for ‘gaps’ between habitats GIS based: Veg. cover layer Land ownership layer Land management status layer Distribution of terrestrial vertebrates as predicted from RS imagery and from in situ veg. mapping

A few applications of veg. RS Discriminating vegetative, crop and timber types Measuring crop and timber acreage Precision farming land management Monitoring crop and forest harvests Determining range readiness, biomass and health Determining soil conditions and associations Monitoring desert blooms Assessing wildlife habitat Characterizing forest range vegetation Monitoring and mapping insect infestations Monitoring irrigation practices Bison management Crop production estimates Quantifying burn severity Fighting crop insurance fraud Better Estimate of Boreal Forest Loss Crop water stress Crop water demand Rice production monitoring Forest damage caused by Hurricane Katrina

Remote sensing of water From http://www.water.umn.edu/Documents/presentation1.pdf

Significance 74% of Earth is water covered Meteorologists, climatologists, oceanographers, geographers, hydrologists, soil scientists, snow scientists, and you measure, monitor, and predict water…. In situ works sometimes; not too well for: Surface area measures Water constituents Water depth Water surface temperature SWE

Spectral properties of water Violet to light blue (400 - 500 nm) transmits best/deepest (not absorbed or scattered well) in water 740 – 2500 nm (middle infrared) used to discriminate water from land Water absorbs here; veg./land reflect here Caution: sediment and organics reflect near infrared

Spectral properties of suspended minerals Suspended mineral concentrations Calibrated in-situ using Secchi disk E.g., clays and silts have different spectral properties for different concentrations

Bathymetry - optical Optical (aerial photography) Optical film: 0.44 to 0.54 um Landsat blue band: 0.45 to 0.54 um 80 cm depth at 0.80 um For best results, calibrate using in-situ depth measurements

Bathymetry - SONAR Sound navigation and ranging (SONAR) Can penetrate to 36,000 ft. Resolution of a few cm Main types: > Single-beam for transects > Multiple beam for continuous coverage and feature mapping > Side-scanning for searching for objects (not for mapping; large overlap)

Bathymetry - lidar Lidar 532 nm (green) for bottom 1064 nm (near IR) for surface Can detect down to about 60 m Fly 200 - 400 m above water at about 130 mph Good for shallow water mapping (object detection)

Precipitation Precipitation Measured directly with NEXRAD radar (11.1 cm microwave energy) Precipitation rate measured indirectly with Cloud top temperature Cloud reflectance Frozen precip. detection Tropical rainfall Measuring Mission (TRMM) 5 instruments for measuring precip.

Unsupervised classification Helper image from pg. 184

Supervised classification Make sure to manually set the “ID or value” field for each class to 1.