The most extensive use of Remote Sensing data is in land cover/land use (LCLU) studies by means of automated image classification. The general objective of this research is to develop an automatic pixel-based classification methodology with the aim to produce a Regional land use map congruent with the CORINE Land Cover legend. Starting point are detailed ground data, already gathered fostering ...

Land cover type is a crucial parameter that is required for various land surface models that simulate water and carbon cycles, ecosystem dynamics, and climate change. Many land use/land cover maps used in recent years have been derived from field investigations and remote-sensing observations. However, no land cover map that is derived from a single source (such as satellite observation) proper...

Detailed urban land use data are important to government officials, researchers, and businesspeople for a variety of purposes. This article presents an approach to classifying detailed urban land use based on geometrical, textural, and contextual information of land parcels. An area of 6 by 14 km in Austin, Texas, with land parcel boundaries delineated by the Travis Central Appraisal District o...

Previous field research on the Horqin Sandy Land (China), which has suffered from severe desertification during recent decades, revealed how land use on a sand-dune topography affects both land degradation and restoration. This study aimed to depict the spatial distribution of local land use in order to shed more light on previous field findings regarding policies on a broader scale. We perform...

The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments. Contrary to our expectations, only 5...

Satellite based land cover classification for Africa’s semi-arid ecosystems is hampered commonly by heterogeneous landscapes with mixed vegetation and small scale land use. Higher spatial resolution remote sensing time series data can improve classification results under these difficult conditions. While most large scale land cover mapping attempts rely on moderate resolution data, PROBA-V prov...

The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types, the ability to discriminate different land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmo...

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data,...