Land cover

Summary

Land cover is the physical material at the surface of Earth. Land covers include grass, asphalt, trees, bare ground, water, etc. Earth cover is the expression used by ecologist Frederick Edward Clements that has its closest modern equivalent being vegetation. The expression continues to be used by the United States Bureau of Land Management. There are two primary methods for capturing information on land cover: field survey, and analysis of remotely sensed imagery. Land change models can be built from these types of data to assess changes in land cover over time. One of the major land cover issues (as with all natural resource inventories) is that every survey defines similarly named categories in different ways. For instance, there are many definitions of "forest"—sometimes within the same organisation—that may or may not incorporate a number of different forest features (e.g., stand height, canopy cover, strip width, inclusion of grasses, and rates of growth for timber production

Official source

https://en.wikipedia.org/wiki/Land_cover

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (36)

Related publications

Related people (7)

Related people

Related units (4)

Related units

Related concepts (6)

Earth is the third planet from the Sun and the only astronomical object known to harbor life. This is enabled by Earth being a water world, the only one in the Solar System sustaining liquid surface w

Land use involves the management and modification of natural environment or wilderness into built environment such as settlements and semi-natural habitats such as arable fields, pastures, and manage

The cryosphere (from the Greek κρύος kryos, "cold", "frost" or "ice" and σφαῖρα sphaira, "globe, ball") is an all-encompassing term for the portions of Earth's surface where water is in solid form, i

Related concepts

Related courses (7)

This course covers optical remote sensing from satellites and airborne platforms. The different systems are presented. The students will acquire skills in image processing and machine/deep learning to extract end-products, such as land cover or risk maps, from the images.

"Hydrology for Engineers" is an introduction to the study of floods, droughts and a fair distribution of water. The course will introduce basic hydrologic concepts and methods: probability and statistics, surface and subsurface hydrological processes

The students will learn the fundamentals in ecology with the goal to perceive the environment beyond its physical and chemical characteristics. Starting from basic concepts, they will acquire mechanistic understanding of biodiversity, ecosystem functioning and global change.

Related courses

Related lectures (9)

Related lectures

Classification of urban structural types (UST) using multiple data sources and spatial priors

Arnaud Poncet Montanges

Remote sensing and geographic information science offer many pos- sibilities in terms of availability of diverse data. Some products like land cover layers or digital elevation models can be extracted from imagery and enable the realization of 3D city models. Starting from these morphological and geographical sources, an approach is proposed to extract information about urban structure types (UST), i.e. types of urban habitat at the neighborhoodscale. We propose an effective processing chain to describe UST : from the different data sources, we extract spectral and spatial indices and use them as features in a machine learning process to classify these urban structural types using support vector machine classification (SVM). Moreover, Markov Random Fields (MRF) are used to take into account the spatial distribution of the classe and increase the spatial consistency. This study focuses on the city of Munich and uses as different data sources the land cover data, the 3D city model, spectral images from LandSat TM 8 and OpenStreetMap (OSM) vector data to character- ize UST. The main hypothesis is that we can discriminate among urban structural types by using land cover information, spectral properties and 3D structure: in other words, that an industrial area won’t have the same structure nor the same properties as a residential or an agricultural area. The proposed processing chain enables to predict with a precision of 70% the 11 UST. This opens possibilities to describe the urban footprint of the city, to detect the key areas for urban planification and to better understand the city dynamics.

2014

The vegetation of semi-arid savanna landscapes relies on the fragile equilibrium between rainfall, grazing and fires . For a sustainable management of their farms, farmers need to take into account this delicate equilibrium and correctly balance the amount of animals and the available food and water resources. These two quantities are estimated every year at the end of the growing season (end of May) to define the yearly management plans. Ground based techniques for animal counting and biomass estimation are long and time consuming processes that can hardly provide a good coverage of the farm. Farmers are now more and more looking towards remote sensing techniques to overcome the field work and achieve both these tasks. The main goal of this project was to classify the soil cover in Kuzikus Wildlife Reserve, in the north-eastern Kalahari region of Namibia, in order to provide an estimate of the food availability for wildlife at the end of the growing season. Ultra-high resolution UAV images and coarse resolution satellite images were classified using both supervised and unsupervised classification techniques. Random Forest and Support Vector Machine supervised classifiers gave encouraging results and the research showed the great potential of UAV and satellite imagery for biomass estimation. Used together, these two complementary types of data allow to have both a detailed plant species recognition and a general overview of the study area.

2018

Land cover classification of the semi-arid Namibian savanna

Beatrice Bacchilega

The vegetation of semi-arid savanna landscapes relies on the fragile equilibrium between rainfall, grazing and res [Trodd and Dougill, 1998]. For a sustainable management of their farms, farmers need to take into account this delicate equilibrium and correctly balance the amount of animals and the available food and water resources. These two quantities are estimated every year at the end of the growing season (end of May) to dene the yearly management plans [Reinhard, 2017]. Ground based techniques for animal counting and biomass estimation are long and time consuming processes that can hardly provide a good coverage of the farm [Reinhard, 2017]. Farmers are now more and more looking towards remote sensing techniques to overcome the eld work and achieve both these tasks. The main goal of this project was to classify the soil cover in Kuzikus Wildlife Reserve, in the north-eastern Kalahari region of Namibia, in order to provide an estimate of the food availability for wildlife at the end of the growing season. Ultra-high resolution UAV images and coarse resolution satellite images were classied using both supervised and unsupervised classication techniques. Random Forest and Support Vector Machine supervised classiers gave encouraging results and the research showed the great potential of UAV and satellite imagery for biomass estimation. Used together, these two complementary types of data allow to have both a detailed plant species recognition and a general overview of the study area.

2018