Optical remotely sensed time series data for land cover classification: A review

Gómez, Cristina; White, Joanne C.; Wulder, Michael A.

doi:10.1016/j.isprsjprs.2016.03.008

Cited by 843 publications

(480 citation statements)

References 194 publications

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“…Now with the introduction of new satellite sensors (e.g. Sentinel 2) both the spatial and temporal resolutions have increased (Gómez, White, & Wulder, 2016).…”

Section: Introductionmentioning

confidence: 99%

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

et al. 2017

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“…Now with the introduction of new satellite sensors (e.g. Sentinel 2) both the spatial and temporal resolutions have increased (Gómez, White, & Wulder, 2016).…”

Section: Introductionmentioning

confidence: 99%

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

et al. 2017

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“…Awareness of these changes is growing and, therefore, a series of studies have been designed to detect and quantify their extent (ten CATEN et al, 2015;GRECCHI et al, 2014;ROCHA et al, 2011). Currently, geo-technological systems and products are indispensable tools for spatial and temporal detection, evaluation and monitoring of problems related to the environment (GÓMEZ et al, 2016;ANDREW et al, 2014;BODART et al, 2013;BODART et al, 2011). This is due to distinct data integration and overlapping at varied scales by geographic information systems (GISs), facilitating decision-making and shifts, if necessary, on land use and occupation policies (BEUCHLE et al, 2015;ten CATEN et al, 2015;MÜLLER et al, 2015;COELHO et al, 2014;GIRI et al, 2013;POTAPOV et al, 2011;DURIGAN & RATTER, 2006).…”

Section: Introductionmentioning

confidence: 99%

Evolution of Cerrado Vegetal Cover on a River Island Based on Orbital Imaging Data

et al. 2016

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This study aimed to assess vegetal cover evolution on a river island within the Ecological Station of (EEP), by remote sensing. For this purpose, Normalized Difference Vegetation Indexes were generated for Landsat 1 (1973( ) and Landsat 5 (1984( , 1990( , 2000 images. Five landscape units were identified in the field: bare soil, Rough savanna, Typical savanna, Forested savanna and Evergreen dry woods. Only Forested savanna and Evergreen dry woods showed poor spectral splitting, being thus considered as a forestry complex. Changes throughout time have occurred in all units, with decreasing in bare soil areas (-2.56 ha year -1 ), Rough savanna (-0.66 ha year -1 ) and Typical savanna (-0.94 ha year -1 ) and with an increase in the Forested savanna (5.97 ha year -1 ). Thus, the method used in this study was effective for sorting and assessing plant cover of the landscape units studied over time.

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“…The second approach to increase classification accuracy (i.e., by developing new algorithms) has been extensively used by the remote sensing community, which has rapidly adopted and adapted novel machine learning image classification approaches [25][26][27]. The combination of existing classifiers (ensemble of classifiers) has, however, received comparatively little attention, although it is known that ensemble classifiers increase classification accuracy because no single classifier outperforms the others [28].…”

Section: Introductionmentioning

confidence: 99%

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

et al. 2018

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Smallholder farmers cultivate more than 80% of the cropland area available in Africa. The intrinsic characteristics of such farms include complex crop-planting patterns, and small fields that are vaguely delineated. These characteristics pose challenges to mapping crops and fields from space. In this study, we evaluate the use of a cloud-based multi-temporal ensemble classifier to map smallholder farming systems in a case study for southern Mali. The ensemble combines a selection of spatial and spectral features derived from multi-spectral Worldview-2 images, field data, and five machine learning classifiers to produce a map of the most prevalent crops in our study area. Different ensemble sizes were evaluated using two combination rules, namely majority voting and weighted majority voting. Both strategies outperform any of the tested single classifiers. The ensemble based on the weighted majority voting strategy obtained the higher overall accuracy (75.9%). This means an accuracy improvement of 4.65% in comparison with the average overall accuracy of the best individual classifier tested in this study. The maximum ensemble accuracy is reached with 75 classifiers in the ensemble. This indicates that the addition of more classifiers does not help to continuously improve classification results. Our results demonstrate the potential of ensemble classifiers to map crops grown by West African smallholders. The use of ensembles demands high computational capability, but the increasing availability of cloud computing solutions allows their efficient implementation and even opens the door to the data processing needs of local organizations.

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Optical remotely sensed time series data for land cover classification: A review

Cited by 843 publications

References 194 publications

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Evolution of Cerrado Vegetal Cover on a River Island Based on Orbital Imaging Data

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

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