The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping
نویسندگان
چکیده
منابع مشابه
The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping
Knowledge of the floral cycle and the spatial distribution and abundance of flowering plants is important for bee health studies to understand the relationship between landscape and bee hive productivity and honey flow. The key objective of this study was to show how AISA Eagle hyperspectral data and random forest (RF) can be optimally utilized to produce flowering and spatially explicit land u...
متن کاملMapping High Resolution Forest Chemistry with Aisa
In the current study, 2002 AVIRIS and 2006 AISA high resolution imagery were applied to the spectroscopic investigation of spatio-temporal variations in forest chemistry. Focused primarily on the foliar biochemistry of Douglas-fir (Pseudotsuga menziesii) stands within the Greater Victoria Watershed, Victoria, BC, Canada, samples were collected and relationships between chemistry and reflectance...
متن کاملApplication of Hyperspectral Data for Forest Stand Mapping
The objectives of this study are to evaluate the potential of Hyperspectral Mapper (HyMap) data and a spectral mixture model to characterize forest stands in a mixed coniferous and deciduous forest. A HyMap image was flown over a standard research site in Western Switzerland in summer 1998. The research forest-site can be characterized as heavily mixed forest. HyMap data were used to map the fo...
متن کاملdeterminate aster satellite data capability and classification and regression tree and random forest algorithm for forest type mapping
recognition equal units and segregation them and upshot planning per units most basic method for management forest units. aim this study presentation and comparison classification and regression tree (cart) and random forest (rf) algorithm for forest type mapping using aster satellite data in district one didactic and research forest's darabkola. in start using inventory network 500* 350 m...
متن کاملRandom Bits Forest: a Strong Classifier/Regressor for Big Data
Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth), boosting (for width), and random forests (for prediction accuracy). Through a gradient boosting scheme, it first generates and selects ~10,000 sma...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Remote Sensing
سال: 2015
ISSN: 2072-4292
DOI: 10.3390/rs71013298