Name
Abstract | ||
|---|---|---|
Prediction of Land use and cover change using remotely sensed imagery has attracted huge attention. From several decades, multiple researchers have investigated different approaches. The complex nature of the land use change process, due to human-nature interactions and the singularities of satellite images, demands a well-studied approach. Yet, a synthesis document is needed to provide a synthetic director paper combining the proposed and/or used models, their advantages and drawbacks. Hence, such studies are required to face the huge demands of land cover changes prediction needs. Therefore, this paper presents a review of prediction models used for land cover change variability purposes. A classification scheme is proposed to enable better specification of current forecasting models. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ATSIP.2017.8075511 | 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) |
Keywords | Field | DocType |
Prediction,Land use,land cover change,remotely sensing imagery | Satellite,Computer science,Classification scheme,Land use, land-use change and forestry,Remote sensing,Support vector machine,Land use land cover,Predictive modelling,Land cover,Land use | Conference |
ISBN | Citations | PageRank |
978-1-5386-0552-3 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oumayma Bounouh | 1 | 0 | 0.34 |
| Houcine Essid | 2 | 0 | 0.68 |
| Imed Riadh Farah | 3 | 86 | 26.16 |