All Outputs (8)

Monitoring high spatiotemporal water dynamics by fusing MODIS, Landsat, water occurrence data and DEM (2021)
Journal Article

Monitoring the spatiotemporal dynamics of surface water from remote sensing imagery is essential for understanding water's impact on the global ecosystem and climate change. There is often a tradeoff between the spatial and temporal resolutions of im... Read More about Monitoring high spatiotemporal water dynamics by fusing MODIS, Landsat, water occurrence data and DEM.

Detection of spatial and temporal patterns of liana infestation using satellite-derived imagery (2021)
Journal Article

Lianas (woody vines) play a key role in tropical forest dynamics because of their strong influence on tree growth, mortality and regeneration. Assessing liana infestation over large areas is critical to understand the factors that drive their spatial... Read More about Detection of spatial and temporal patterns of liana infestation using satellite-derived imagery.

Informing action for United Nations SDG target 8.7 and interdependent SDGs: Examining modern slavery from space (2021)
Journal Article

This article provides an example of the ways in which remote sensing, Earth observation, and machine learning can be deployed to provide the most up to date quantitative portrait of the South Asian ‘Brick Belt’, with a view to understanding the exten... Read More about Informing action for United Nations SDG target 8.7 and interdependent SDGs: Examining modern slavery from space.

rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back (2021)
Journal Article

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow. In this paper, we present a new R package—... Read More about rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back.

Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record (2021)
Journal Article

Information on forest disturbance is crucial for tropical forest management and global carbon cycle analysis. The long-term collection of data from the Landsat missions provides some of the most valuable information for understanding the processes of... Read More about Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record.

Impacts of ignorance on the accuracy of image classification and thematic mapping (2021)
Journal Article

Thematic maps are often derived from remotely sensed imagery via a supervised image classification analysis. The training and testing stages of a supervised image classification may proceed ignorant of the presence of some classes in the region to be... Read More about Impacts of ignorance on the accuracy of image classification and thematic mapping.

Scrutinizing relationships between submarine groundwater discharge and upstream areas using thermal remote sensing: A case study in the northern Persian gulf (2021)
Journal Article

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Nutrient input through submarine groundwater discharge (SGD) often plays a significant role in primary productivity and nutrient cycling in the coastal areas. Understanding relationships betwe... Read More about Scrutinizing relationships between submarine groundwater discharge and upstream areas using thermal remote sensing: A case study in the northern Persian gulf.

Comparison of simple averaging and latent class modeling to estimate the area of land cover in the presence of reference data variability (2021)
Journal Article

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite image... Read More about Comparison of simple averaging and latent class modeling to estimate the area of land cover in the presence of reference data variability.