Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art

Land use/land cover (LULC) is a fundamental concept of theEarth’s system intimately connected to many phases of the human and physical environment. Earth observation (EO) technology provides an informative source of data covering the entire globe in a spatial and spectral resolution appropriate to better and easier classify land cover than traditional or conventional methods. The use of high spatial and spectral resolution imagery from EO sensors has increased remarkably over the past decades, as more andmore platforms are placed in orbit and new applications emerge in different disciplines. The aim of the present review work is to provide all-inclusive critical reflection on the state of the art in the use of EO technology in LULC mapping and change detection. The emphasis is placed on providing an overview of the different EO datasets, spatial-spectral-temporal characteristics of satellite data and classification approaches employed in land cover classification. The review concludes providing recommendations and remarks on what should be done in order to overcome hurdle faced using above-mentioned problems in LULC mapping. This also provides information on using classifier algorithms depending upon the data types and dependent on the regional ecosystems. One of the main messages of our review is that in future, there will be a need to assemble techniques specifically used in LULC with their merit and demerits that will enable more comprehensive understanding at regional or global scale and improve understanding between different land cover relationship and variability among them and these remains to be seen.

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Land Use/Land Cover in view of Earth Observation: Data Sources, Input Dimensions and Classifiers -a Review of the State of the Art

10.31219/osf.io/3sugp ◽

2019 ◽

Author(s):

Prem Chandra Pandey ◽

Nikos Koutsias ◽

George Petropoulos ◽

Prashant K. Srivastava ◽

Eyal Ben Dor

Keyword(s):

Land Use ◽

Land Cover ◽

Spectral Resolution ◽

State Of The Art ◽

Earth Observation ◽

The State ◽

Observation Data ◽

Land Use Land Cover ◽

Data Types ◽

Earth’S System

Land use/Land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment. Earth Observation (EO) technology provides an informative source of data covering the entire globe in a spatial and spectral resolution appropriate to better and easier classify land cover than traditional or conventional methods. The use of high spatial and spectral resolution imagery from EO sensors has increased remarkably over the past decades, as more and more platforms are placed in orbit and new applications emerge in different disciplines. The aim of the present review work is to provide all-inclusive critical reflection on the state of the art in the use of EO technology in LULC mapping and change detection. The emphasis is placed on providing an overview of the different EO datasets, spatial-spectral-temporal characteristics of satellite data and classification approaches employed in land cover classification. The review concludes providing recommendations and remarks on what should be done in order to overcome hurdle faced using above-mentioned problems in LULC mapping. This also provides information on using classifier algorithms depending upon the data types and dependent on the regional ecosystems. One of the main messages of our review is that in future, there will be a need to assemble techniques specifically used in LULC with their merit and demerits that will enable more comprehensive understanding at regional or global scale and improve understanding between different land cover relationship and variability among them and these remains to be seen.

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Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India

Geocarto International ◽

10.1080/10106049.2017.1343390 ◽

2017 ◽

Vol 33 (11) ◽

pp. 1202-1222 ◽

Cited By ~ 37

Author(s):

Sudhir Kumar Singh ◽

Prosper Basommi Laari ◽

Sk. Mustak ◽

Prashant K. Srivastava ◽

Szilárd Szabó

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

River Basin ◽

Earth Observation ◽

Data Sets ◽

Observation Data ◽

Land Use Land Cover ◽

Earth Observation Data ◽

Tons River Basin

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Past and future prediction of land cover land use change based on earth observation data by the CA–Markov model: a case study from Duhok governorate, Iraq

Arabian Journal of Geosciences ◽

10.1007/s12517-021-07984-6 ◽

2021 ◽

Vol 14 (15) ◽

Author(s):

Nabaz R. Khwarahm ◽

Peshawa M. Najmaddin ◽

Korsh Ararat ◽

Sarchil Qader

Keyword(s):

Land Use ◽

Land Use Change ◽

Land Cover ◽

Markov Model ◽

Earth Observation ◽

Observation Data ◽

Future Prediction ◽

Earth Observation Data

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EXPOSURE AND VULNERABILITY GEOSPATIAL ANALYSIS USING EARTH OBSERVATION DATA IN THE CITY OF LIEGE, BELGIUM

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-1-w1-149-2017 ◽

2017 ◽

Vol IV-1/W1 ◽

pp. 149-156

Author(s):

N. Stephenne ◽

B. Beaumont ◽

E. Hallot ◽

F. Lenartz ◽

F. Lefebre ◽

...

Keyword(s):

Risk Assessment ◽

Land Use ◽

Air Pollution ◽

Land Cover ◽

Urban Heat Island ◽

Earth Observation ◽

Observation Data ◽

Heat Island ◽

Urban Heat ◽

Earth Observation Data

Risk situation can be mitigated by prevention measures, early warning tools and adequate monitoring of past experiences where Earth Observation and geospatial analysis have an adding value. This paper discusses the potential use of Earth Observation data and especially Land Cover / Land Use map in addressing within the three aspects of the risk assessment: danger, exposure and vulnerability. Evidences of the harmful effects of air pollution or heat waves are widely admitted and should increase in the context of global warming. Moreover, urban areas are generally warmer than rural surroundings, the so-called urban heat island. Combined with in-situ measurements, this paper presents models of city or local climate (air pollution and urban heat island), with a resolution of less than one kilometer, developed by integrating several sources of information including Earth Observation data and in particular Land Cover / Land Use. This assessment of the danger is then be related to a map of exposure and vulnerable people. Using dasymetric method to disaggregate statistical information on Land Cover / Land Use data, the SmartPop project analyzes in parallel the map of danger with the maps of people exposure A special focus on some categories at risk such as the elderly has been proposed by Aubrecht and Ozceylan (2013). Perspectives of the project includes the integration of a new Land Cover / Land Use map in the danger, exposure and vulnerability models and proposition of several aspects of risk assessment with the stakeholders of Wallonia.

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FASTENER Feature Selection for Inference from Earth Observation Data

Entropy ◽

10.3390/e22111198 ◽

2020 ◽

Vol 22 (11) ◽

pp. 1198

Author(s):

Filip Koprivec ◽

Klemen Kenda ◽

Beno Šircelj

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Land Cover ◽

State Of The Art ◽

Land Cover Classification ◽

Earth Observation ◽

Supervised Machine Learning ◽

Observation Data ◽

Multi Objective ◽

Earth Observation Data

In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features as possible. The algorithm exploits entropy-based measures, such as mutual information in the crossover phase of the iterative genetic approach. FASTENER converges to a (near) optimal subset of features faster than other multi-objective wrapper methods, such as POSS, DT-forward and FS-SDS, and achieves better classification accuracy than similarity and information theory-based methods currently utilized in earth observation scenarios. The approach was primarily evaluated using the earth observation data set for land-cover classification from ESA’s Sentinel-2 mission, the digital elevation model and the ground truth data of the Land Parcel Identification System from Slovenia. For land cover classification, the algorithm gives state-of-the-art results. Additionally, FASTENER was tested on open feature selection data sets and compared to the state-of-the-art methods. With fewer model evaluations, the algorithm yields comparable results to DT-forward and is superior to FS-SDS. FASTENER can be used in any supervised machine learning scenario.

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Quantifying Tree Cover Loss in Urban Forests within Nairobi City Metropolitan Area from Earth Observation Data

Environmental Sciences Proceedings ◽

10.3390/iecf2020-07952 ◽

2020 ◽

Vol 3 (1) ◽

pp. 78

Author(s):

Francis Oloo ◽

Godwin Murithi ◽

Charlynne Jepkosgei

Keyword(s):

Land Cover ◽

Metropolitan Area ◽

Land Surface ◽

Urban Forest ◽

Urban Forests ◽

Earth Observation ◽

Tree Cover ◽

Observation Data ◽

Vegetation Health ◽

Earth Observation Data

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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Characterizing Land Use/Land Cover Using Multi-Sensor Time Series from the Perspective of Land Surface Phenology

Remote Sensing ◽

10.3390/rs11141677 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1677 ◽

Cited By ~ 8

Author(s):

Lan H. Nguyen ◽

Geoffrey M. Henebry

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

Land Surface ◽

Quadratic Model ◽

Observation Data ◽

Land Use Land Cover ◽

Land Surface Phenology ◽

Land Cover Map ◽

Phenological Metrics

Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVI2), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVI2 = f(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVI2 = f(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area.

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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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