Abstract
To consistently and repeatedly monitor forests over large areas, it is desirable to use remote sensing data and automated image analysis techniques. Several types of remote sensing data, including Aerial photography, optical Multispectral Scanner, radar, lidar (Laser) and Videographic data have been used by forest research and operational agencies to detect, identify, classify, evaluate and measure various forest cover types and their changes. Over the past decades tremendous progress has been made in demonstrating the potentials and limitations of the applications of remote sensing in forestry. Remote sensing can detect, identify, classify, evaluate and measure various forest characteristics in two ways: qualitatively and quantitatively. In a qualitative way remote sensing can classify forest cover types to: coniferous and deciduous forest, mangrove forest, swamp forest, forest plantations, etc. While the quantitative analysis can measure or estimate forest parameters (e.g., Dbh, height, basal area, number of trees per unite area, timber volume and woody biomass), floristic composition, life forms, and structure. For several types of applications of remote sensing in forestry in specific regions of the world such as tropical areas, users of forest information are demanding new establishment of sensors and platforms. In order to see what kind of information we can extract from the current remote sensing sensors and platforms and how accurate is that, an inventory of all remote sensing applications in forestry is needed. This paper presents a state of the art inventory of all remote sensing applications in forestry.
| Original language | English |
|---|---|
| Pages | 575-579 |
| Number of pages | 5 |
| Publication status | Published - 1 Jan 2000 |
| Event | 19th International Congress for Photogrammetry and Remote Sensing, ISPRS 2000 - Amsterdam, Netherlands Duration: 16 Jul 2000 → 23 Jul 2000 Conference number: 19 |
Conference
| Conference | 19th International Congress for Photogrammetry and Remote Sensing, ISPRS 2000 |
|---|---|
| Abbreviated title | ISPRS |
| Country | Netherlands |
| City | Amsterdam |
| Period | 16/07/00 → 23/07/00 |
Fingerprint
Keywords
- Applications
- Forestry
- Remote sensing
- Sensors
- Sustainable management
Cite this
}
Inventory of remote sensing applications in forestry for sustainable managment. / Hussin, Y.A.; Bijker, W.
2000. 575-579 Paper presented at 19th International Congress for Photogrammetry and Remote Sensing, ISPRS 2000, Amsterdam, Netherlands.Research output: Contribution to conference › Paper › Academic › peer-review
TY - CONF
T1 - Inventory of remote sensing applications in forestry for sustainable managment
AU - Hussin, Y.A.
AU - Bijker, W.
PY - 2000/1/1
Y1 - 2000/1/1
N2 - To consistently and repeatedly monitor forests over large areas, it is desirable to use remote sensing data and automated image analysis techniques. Several types of remote sensing data, including Aerial photography, optical Multispectral Scanner, radar, lidar (Laser) and Videographic data have been used by forest research and operational agencies to detect, identify, classify, evaluate and measure various forest cover types and their changes. Over the past decades tremendous progress has been made in demonstrating the potentials and limitations of the applications of remote sensing in forestry. Remote sensing can detect, identify, classify, evaluate and measure various forest characteristics in two ways: qualitatively and quantitatively. In a qualitative way remote sensing can classify forest cover types to: coniferous and deciduous forest, mangrove forest, swamp forest, forest plantations, etc. While the quantitative analysis can measure or estimate forest parameters (e.g., Dbh, height, basal area, number of trees per unite area, timber volume and woody biomass), floristic composition, life forms, and structure. For several types of applications of remote sensing in forestry in specific regions of the world such as tropical areas, users of forest information are demanding new establishment of sensors and platforms. In order to see what kind of information we can extract from the current remote sensing sensors and platforms and how accurate is that, an inventory of all remote sensing applications in forestry is needed. This paper presents a state of the art inventory of all remote sensing applications in forestry.
AB - To consistently and repeatedly monitor forests over large areas, it is desirable to use remote sensing data and automated image analysis techniques. Several types of remote sensing data, including Aerial photography, optical Multispectral Scanner, radar, lidar (Laser) and Videographic data have been used by forest research and operational agencies to detect, identify, classify, evaluate and measure various forest cover types and their changes. Over the past decades tremendous progress has been made in demonstrating the potentials and limitations of the applications of remote sensing in forestry. Remote sensing can detect, identify, classify, evaluate and measure various forest characteristics in two ways: qualitatively and quantitatively. In a qualitative way remote sensing can classify forest cover types to: coniferous and deciduous forest, mangrove forest, swamp forest, forest plantations, etc. While the quantitative analysis can measure or estimate forest parameters (e.g., Dbh, height, basal area, number of trees per unite area, timber volume and woody biomass), floristic composition, life forms, and structure. For several types of applications of remote sensing in forestry in specific regions of the world such as tropical areas, users of forest information are demanding new establishment of sensors and platforms. In order to see what kind of information we can extract from the current remote sensing sensors and platforms and how accurate is that, an inventory of all remote sensing applications in forestry is needed. This paper presents a state of the art inventory of all remote sensing applications in forestry.
KW - Applications
KW - Forestry
KW - Remote sensing
KW - Sensors
KW - Sustainable management
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2000/conf/hussin_inv.pdf
M3 - Paper
SP - 575
EP - 579
ER -