- Fully adopt
- Partially adopt
- Not adopted
- Missing Value
Please, select year and panels to show the info.
- Very Good Practice
- Good Practice
- Weak Practice
- Very Weak Practice
- Missing Value
Disclaimer: The data displayed on the Land Portal is provided by third parties indicated as the data source or as the data provider. The Land Portal team is constantly working to ensure the highest possible standard of data quality and accuracy, yet the data is by its nature approximate and will contain some inaccuracies. The data may contain errors introduced by the data provider(s) and/or by the Land Portal team. In addition, this page allows you to compare data from different sources, but not all indicators are necessarily statistically comparable. The Land Portal Foundation (A) expressly disclaims the accuracy, adequacy, or completeness of any data and (B) shall not be liable for any errors, omissions or other defects in, delays or interruptions in such data, or for any actions taken in reliance thereon. Neither the Land Portal Foundation nor any of its data providers will be liable for any damages relating to your use of the data provided herein.
AIM: For many applications in biodiversity and ecology, existing remote sensing‐derived land‐cover products have limitations due to among‐product inconsistency and their typically non‐continuous nature. Here we aim to help address these shortcomings by generating a 1‐km resolution global product that provides scale‐integrated and accuracy‐weighted consensus land‐cover information on an approximately continuous scale. LOCATION: Global. METHODS: Using a generalized classification scheme and an accuracy‐based integration approach, we integrated four global land‐cover products.
We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T (orthorectified).
Low or medium spatial resolution satellite images are used for environmental monitoring in remote, extremely cold areas such as Antarctica. However, they cannot provide detailed spatial and spectral information over Antarctic areas. For obtaining this information, we propose an Antarctic land-cover classification method using IKONOS and Hyperion data over Terra Nova Bay, Antarctica. High spatial resolution IKONOS imagery enabled the detection of detailed, accurate boundaries between areas of snow, ice, vegetation, water and rock.
Two species of flowering plant of Fuegian montane provenance have been discovered on Deception Island in the maritime Antarctic, 950 km south of South America. Four individuals of Nassauvia magellanica and one of Gamochaeta nivalis (both Asteraceae) are growing robustly and in close proximity of each other on dry ash and scoria soil near a ruined whaling station which, in recent years, has been frequently visited by large numbers of ship-borne tourists.
In this study, a terrain classification algorithm is presented that was derived from various properties of the returned full waveform signals collected from the Ice, Cloud and land elevation Satellite (ICESat) mission. Such an algorithm would be beneficial for current and future studies of the cryosphere, particularly Greenland and Antarctica, by helping to identify changes in the large scale surface properties over time. The algorithm developed was validated over a test region in the Dry Valleys of Antarctica, where the terrain is well known and regularly monitored.