Remote Sensing of Rangeland Degradation Assessment: Classifying Increaser Species,Used

Remote Sensing of Rangeland Degradation Assessment: Classifying Increaser Species,Used

In Stock
SKU: DADAX365920014X
Brand: LAP Lambert Academic Publishing
Sale price$93.67 Regular price$133.81
Save $40.14
Quantity
Add to wishlist
Add to compare

Processing time: 1-3 days

US Orders Ships in: 3-5 days

International Orders Ships in: 8-12 days

Return Policy: 15-days return on defective items

Payment Option
Payment Methods

Help

If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.

Customer service

All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com

Sale & Press

If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com

The degradation of rangeland grass is currently one of the most serious environmental problems in South Africa. Increaser grass species has been used as indicators to evaluate rangeland condition. Therefore, classifying these species and monitoring their relative abundance is an important step for sustainable rangelands management. Traditional methods have been used in classifying increaser species over small geographic areas. These methods are regarded as being costly and timeconsuming, because grasslands usually cover large expanses that are situated in isolated and inaccessible areas. In this regard, remote sensing techniques offer a practical and economical means for quantifying rangeland degradation over large areas. This study advocates the development of techniques based on remote sensing to classify four dominant increaser species associated with rangeland degradation namely: Hyparrhenia hirta, Eragrostis curvula, Sporobolus africanus and Aristida diffusa in Okhombe communal rangeland, KwaZuluNatal, South Africa. Results showed that remotely sensed data with the random forest algorithm has the potential to accurately discriminate species with best overall accuracy.

⚠️ WARNING (California Proposition 65):

This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.

For more information, please visit www.P65Warnings.ca.gov.

Recently Viewed