A new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data

Барталев, С.А.; Egorov, V.A.; Loupian, Е.А.; Khvostikov, S.A.

doi:10.1080/2150704x.2013.870675

Cited by 33 publications

(23 citation statements)

References 9 publications

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“…The mapping method included pre-processing to reduce impact of interfere factors (clouds and their shadows, seasonal snow cover, hardware noise, etc. ), followed by subsequent classification of various types of forests and other land cover classes using the Locally Adaptive Global Mapping Algorithm (LAGMA) [22]. The algorithm ensures classifier adaptivity to spatial changes of physical and geographical conditions, satisfying one of the requirements for methods of processing remote sensing data for large areas.…”

Section: Estimation Of Watersheds Forest Coveragementioning

confidence: 99%

“…This, unquestionably, requires conducting a complex forest hydrologic research in the region. Availability of the new types of data, acquired by remote sensing of the Earth, allows to apply effective methods for vegetation cover mapping [22][23][24], as well as for the study of hydrological processes. For example, in [25], hydrological processes in the Amur basin from 2000-2013 were studied influenced by climate change and human activity.…”

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Impact of Deforestation on Streamflow in the Amur River Basin

2019

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In the basin of the Amur River in the Russian Far East, the influence of watershed areas covered by forests on the river basin has a complex nature, and no strict functional dependency has been established yet between these two factors. A study of the Amur River watershed in the current conditions, between 2000 and 2016 (climate, forest coverage, fires, and felling), has been conducted using the ground and satellite observations. The purpose of the study was to identify their influence on the river behaviour (flow, flooding, and levels of water). The study of hydrological regime of rivers was conducted in conjunction with the analysis of the dynamics of forest and burns areas over the synchronised periods of time. A special attention was given to the changing nature of the species composition of the forests (coniferous and deciduous forests separately) from 2000 to 2016, and climatic parameters over thirty years (atmospheric temperature, dew point, precipitation). New facts have been obtained, which provide an explanation of the reasons for predominant prolonged trends in the dynamics of the summer streamflow. In the view of the general tendency toward increased forest coverage combining all species of forest stand, the trend in the dynamics of the coniferous species areas is negative. Therefore, a conclusion can be made, that one of the major factors in the increase of the river flood flow (alongside the atmospheric precipitation), is deforestation of primary coniferous forests on the watershed areas, in contrast with the deciduous forests, where the trend is positive. Practicability of such conclusions can be justified, as different types of forests have different root systems, which mellow the ground and facilitate partial loss of the atmospheric precipitation and its transformation into the groundwater flow. Besides, coniferous forests attract more frequent and intensive fires, more subjected to felling, have longer regeneration period, and also, use larger volumes of ground waters for growing and functioning. Consequently, with their disappearance, an increase in streamflow should be expected. No changes in surface temperature and humidity of the forest cover in the watersheds during 1980-2016 despite global warming. Therefore, annual variability of forested areas of watersheds is greatly influenced by fires and felling. There are reasons to assume, that because of the tendency for decreasing areas of coniferous forests, the conditions contributing to the increases in rivers’ flood flow and flood risks during monsoon and frontal cyclonic rainfalls will remain.

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Section: Estimation Of Watersheds Forest Coveragementioning

confidence: 99%

mentioning

confidence: 99%

Impact of Deforestation on Streamflow in the Amur River Basin

2019

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“…One dedicated mapping effort in particular has been aimed at the development of the TerraNorte Russian land cover (RLC) map as an advanced regional land cover product, with both spatial resolution and thematic accuracy improvements (Bartalev et al 2011). The TerraNorte land cover map improvements were achieved as a combined effect of a mapping method based on the locally-adaptive global mapping algorithm (LAGMA) (Bartalev et al 2014) being applied to the 231.6 m spatial resolution surface reflectance data acquired by the MODIS instrument Terra. The land cover map legend consists of 22 thematic classes, including 18 various vegetation types, and has been designed to take into account vegetation life forms, leaf types and phenological dynamics in accordance with the land cover classification system (LCCS) criteria (Di Gregorio 2005).…”

Section: Introductionmentioning

confidence: 99%

Regional adaptation of a dynamic global vegetation model using a remote sensing data derived land cover map of Russia

Khvostikov

Venevsky

Барталев

2015

Environ. Res. Lett.

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The dynamic global vegetation model (DGVM) SEVER has been regionally adapted using a remote sensing data-derived land cover map in order to improve the reconstruction conformity of the distribution of vegetation functional types over Russia. The SEVER model was modified to address noticeable divergences between modelling results and the land cover map. The model modification included a light competition method elaboration and the introduction of a tundra class into the model. The rigorous optimisation of key model parameters was performed using a two-step procedure. First, an approximate global optimum was found using the efficient global optimisation (EGO) algorithm, and afterwards a local search in the vicinity of the approximate optimum was performed using the quasi-Newton algorithm BFGS. The regionally adapted model shows a significant improvement of the vegetation distribution reconstruction over Russia with better matching with the satellite-derived land cover map, which was confirmed by both a visual comparison and a formal conformity criterion.

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“…A locally-adaptive image classification method LAGMA (Locally-Adaptive Global Mapping Algorithm) [42] has been applied to recognize different land cover types using above mentioned seasonal image composites. The LAGMA method involves a regular grid based estimation of local (spectral and temporal) class signatures using spatially distributed reference data and supervised image classification.…”

Section: Land Cover Mappingmentioning

confidence: 99%

Exploiting Growing Stock Volume Maps for Large Scale Forest Resource Assessment: Cross-Comparisons of ASAR- and PALSAR-Based GSV Estimates with Forest Inventory in Central Siberia

Hüttich

Korets

Барталев

et al. 2014

Forests

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Growing stock volume is an important biophysical parameter describing the state and dynamics of the Boreal zone. Validation of growing stock volume (GSV) maps based on satellite remote sensing is challenging due to the lack of consistent ground reference data. The monitoring and assessment of the remote Russian forest resources of Siberia can only be done by integrating remote sensing techniques and interdisciplinary collaboration. In this paper, we assess the information content of GSV estimates in Central Siberian forests obtained at 25 m from ALOS-PALSAR and 1 km from ENVISAT-ASAR backscatter data. The estimates have been cross-compared with respect to forest inventory data showing 34% relative RMSE for the ASAR-based GSV retrievals and 39.4% for the PALSAR-based estimates of GSV. Fragmentation analyses using a MODIS-based land cover dataset revealed an increase of retrieval error with increasing fragmentation of the landscape. Cross-comparisons of multiple SAR-based GSV estimates helped to detect OPEN ACCESSForests 2014, 5 1754 inconsistencies in the forest inventory data and can support an update of outdated forest inventory stands.

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A new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data

Cited by 33 publications

References 9 publications

Impact of Deforestation on Streamflow in the Amur River Basin

Impact of Deforestation on Streamflow in the Amur River Basin

Regional adaptation of a dynamic global vegetation model using a remote sensing data derived land cover map of Russia

Exploiting Growing Stock Volume Maps for Large Scale Forest Resource Assessment: Cross-Comparisons of ASAR- and PALSAR-Based GSV Estimates with Forest Inventory in Central Siberia

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