Soil characterization based on land cover heterogeneity over a tropical landscape: an integrated approach using earth observation data-sets

Urban forests contribute significantly to the ecological integrity of urban areas and the quality of life of urban dwellers through air quality control, energy conservation, improving urban hydrology, and regulation of land surface temperatures (LST). However, urban forests are under threat due to human activities, natural calamities, and bioinvasion continually decimating forest cover. Few studies have used fine-scaled Earth observation data to understand the dynamics of tree cover loss in urban forests and the sustainability of such forests in the face of increasing urban population. The aim of this work was to quantify the spatial and temporal changes in urban forest characteristics and to assess the potential drivers of such changes. We used data on tree cover, normalized difference vegetation index (NDVI), and land cover change to quantify tree cover loss and changes in vegetation health in urban forests within the Nairobi metropolitan area in Kenya. We also used land cover data to visualize the potential link between tree cover loss and changes in land use characteristics. From approximately 6600 hectares (ha) of forest land, 720 ha have been lost between 2000 and 2019, representing about 11% loss in 20 years. In six of the urban forests, the trend of loss was positive, indicating a continuing disturbance of urban forests around Nairobi. Conversely, there was a negative trend in the annual mean NDVI values for each of the forests, indicating a potential deterioration of the vegetation health in the forests. A preliminary, visual inspection of high-resolution imagery in sample areas of tree cover loss showed that the main drivers of loss are the conversion of forest lands to residential areas and farmlands, implementation of big infrastructure projects that pass through the forests, and extraction of timber and other resources to support urban developments. The outcome of this study reveals the value of Earth observation data in monitoring urban forest resources.

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Factors affecting urban electricity consumption: a case study in the Bangkok Metropolitan Area using an integrated approach of earth observation data and data analysis

Environmental Science and Pollution Research ◽

10.1007/s11356-020-09157-6 ◽

2020 ◽

Author(s):

Can Trong Nguyen ◽

Diep Thi Hong Nguyen ◽

Diem Kieu Phan

Keyword(s):

Data Analysis ◽

Metropolitan Area ◽

Electricity Consumption ◽

Integrated Approach ◽

Earth Observation ◽

Observation Data ◽

Factors Affecting ◽

Earth Observation Data

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Space Observatory for carbon budget monitoring in Russian forests using Earth observations and modelling

10.5194/egusphere-egu2020-20567 ◽

2020 ◽

Author(s):

Sergey Bartalev

Keyword(s):

Land Cover ◽

Carbon Budget ◽

Earth Observation ◽

Forest Carbon ◽

Observation Data ◽

Space Observatory ◽

Russian Science ◽

Land Cover Types ◽

Russian Forests ◽

Earth Observation Data

Russian forest is a factor of global importance for implementation of international conventions on climate considering its potential for absorption and accumulation of the atmospheric carbon at an impressive scale. Considering recently adopted Paris agreement on climate the comprehensive and accurate estimation of Russian forests&#8217; carbon budget became a top priority research and development issue on national agenda. However existing quantitative estimates of Russian forests&#8217; carbon budget are of significant level of uncertainty. One of the most obvious reasons for such uncertainty is not sufficiently reliable and up-to-date information on characteristics of forests and their dynamics.The Russian Science Foundation has supported an ambitious research megaproject titled &#8220;Space Observatory for Forest Carbon&#8221; (SOFC) started in year 2019 and aimed at the development of a new concept and comprehensive methods for forest carbon budget monitoring using Earth observation data and forest growth and dynamics models. The main SOFC project objectives are as follows:- Development of a new concept and methodology for Russian forests and their carbon budget monitoring based on the integration of remote sensing and ground data along with improved models of forest structure and dynamics;- Development of new annually updated GIS databases on the characteristics and multi-annual dynamics of Russian forests;- Development of an informational system and technology for the continuous monitoring of Russian forests&#8217; carbon budget.Information necessary for carbon budget estimation includes data on various land cover types, forest characteristics (growing stock volume, species composition, age, site-index) and ecological parameters (Net Primary Production, heterotrophic respiration). Data on natural (fires, diseases and pests, windstorm, droughts) and anthropogenic (felling, pollution) forest disturbances causing deforestation, as well as information on subsequent reforestation processes are also vital.The existing remote sensing methods can provide significant part of missing country-wide information about the land cover types and forest characteristics for the national-scale carbon budget estimation and monitoring. Multi-year time series of this data since the beginning of the century allow modelling the forest dynamics and its biophysical characteristics. The Earth observation data derived information on forest fires&#8217; impact includes burnt area mapping over various land cover types as well as forest fire severity assessment allowing characterisation of fire induced carbon emissions. Furthermore, developed methods for processing and analysis of multi-year satellite data time series enable detection of forest cover changes caused by various destructive factors making it possible to substantially improve the accuracy of carbon budget estimation.Obtained information on forest ecosystems&#8217; parameters is used to improve existing and develop new approaches to forest carbon budget estimation, as well as to simulate various scenarios of Russian economy development depending on forest management practices and climate change trajectories.This work was supported by the Russian Science Foundation [grant number 19-77-30015].

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Mapping the extent of land cover colour harmony based on satellite Earth observation data

GeoJournal ◽

10.1007/s10708-018-9908-x ◽

2018 ◽

Vol 84 (4) ◽

pp. 1057-1072 ◽

Cited By ~ 2

Author(s):

Oleksandr Karasov ◽

Mart Külvik ◽

Igor Chervanyov ◽

Kostiantyn Priadka

Keyword(s):

Land Cover ◽

Earth Observation ◽

Observation Data ◽

Earth Observation Data

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Evaluation of the Potential of Convolutional Neural Networks and Random Forests for Multi-Class Segmentation of Sentinel-2 Imagery

Remote Sensing ◽

10.3390/rs11080907 ◽

2019 ◽

Vol 11 (8) ◽

pp. 907 ◽

Cited By ~ 5

Author(s):

Vasileios Syrris ◽

Paul Hasenohr ◽

Blagoj Delipetrev ◽

Alexander Kotsev ◽

Pieter Kempeneers ◽

...

Keyword(s):

Random Forests ◽

Computational Models ◽

Earth Observation ◽

Data Sets ◽

Observation Data ◽

Computational Aspects ◽

Learning Set ◽

Earth Observation Data ◽

Class Segmentation ◽

Sentinel 2

Motivated by the increasing availability of open and free Earth observation data through the Copernicus Sentinel missions, this study investigates the capacity of advanced computational models to automatically generate thematic layers, which in turn contribute to and facilitate the creation of land cover products. In concrete terms, we assess the practical and computational aspects of multi-class Sentinel-2 image segmentation based on a convolutional neural network and random forest approaches. The annotated learning set derives from data that is made available as result of the implementation of European Union’s INSPIRE Directive. Since this network of data sets remains incomplete in regard to some geographic areas, another objective of this work was to provide consistent and reproducible ways for machine-driven mapping of these gaps and a potential update of the existing ones. Finally, the performance analysis identifies the most important hyper-parameters, and provides hints on the models’ deployment and their transferability.

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