Remote Sensing Applications: Beyond Land-Use and Land-Cover Change

Baynard, Chris W.

doi:10.4236/ars.2013.23025

Cited by 18 publications

(10 citation statements)

References 78 publications

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“…Satellite remote sensing is an excellent tool to assess land ecosystem dynamics as it provides periodic, spatially explicit and consistent measurements of biophysical processes at the Earth's surface (Baynard 2013). It has become an established monitoring instrument in the fields of forestry (Achard et al 2002), agriculture (Atzberger 2013) and plant ecology (Xie et al 2008).…”

Section: Introductionmentioning

confidence: 99%

A time series processing tool to extract climate-driven interannual vegetation dynamics using Ensemble Empirical Mode Decomposition (EEMD)

Hawinkel

Swinnen

Lhermitte

et al. 2015

Remote Sensing of Environment

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Interannual changes of vegetation are crucial in understanding ecosystem dynamics under global change. However, there is no automated tool to extract these interannual changes from remote sensing time series. To fill this gap, the Ensemble Empirical Mode Decomposition (EEMD) framework was refined and implemented to decompose time series of Normalized Difference Vegetation Index (NDVI) and reconstruct their interannual components. The performance of EEMD-based interannual NDVI detection was assessed using simulated time series, and its sensitivity to model and data parameters was determined to provide a basis for remote sensing applications. The sensitivity analysis highlighted application limitations for time series with low interannual to annual amplitude ratios and high irregularity in timing of growing seasons, as these factors have the strongest effects on the overall performance. However, within these limitations, the detected interannual components correspond well to simulated input components with respect to timing of episodes and composition of time scales. The applicability on real world NDVI time series was demonstrated by mapping the coupling between precipitation variability, interannual vegetation changes, and the El Niño Southern Oscillation and Indian Ocean Dipole phenomena for ecoregions in East and Central Africa. In most areas where precipitation was found sensitive to oceanic forcing, the EEMD detected vegetation changes matched the predicted response, except in dense forest ecosystems.

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Section: Introductionmentioning

confidence: 99%

A time series processing tool to extract climate-driven interannual vegetation dynamics using Ensemble Empirical Mode Decomposition (EEMD)

Hawinkel

Swinnen

Lhermitte

et al. 2015

Remote Sensing of Environment

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“…For instance, it is suggested that a higher value of spatial and spectral detail is appropriate in the case that the classified object is smaller than the spatial resolution of satellite image [35,36]. Therefore, values of spectral and spatial detail were set to 18 for all imagery (possible range: [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The size of objects is defined by the parameter of minimum segment size, which merges segments smaller than this criterion with their best fitting neighboring segment.…”

Section: Segmentationmentioning

confidence: 99%

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

2020

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Information on the spatial distribution of human disturbance is important for assessing and monitoring land degradation. In the Eastern Mongolian Steppe Ecosystem, one of the major driving factors of human-induced land degradation is the expansion of road networks mainly due to intensifications of oil exploration and exploitation. So far, neither the extents of road networks nor the extent of surrounding grasslands affected by the oil industry are monitored which is generally labor consuming. This causes that no information on the changes in the area which is affected by those disturbance drivers is available. Consequently, the study aim is to provide a cost-effective methodology to classify infrastructure and oil exploitation areas from remotely sensed images using object-based classifications with Random Forest. By combining satellite data with different spatial and spectral resolutions (PlanetScope, RapidEye, and Landsat ETM+), the product delivers data since 2005. For the classification variables, segmentation, spectral characteristics, and indices were extracted from all above mentioned imagery and used as predictors. Results show that overall accuracies of land use maps ranged 73%–93% mainly depending on satellites’ spatial resolution. Since 2005, the area of grassland disturbed by dirt roads and oil exploitation infrastructure increased by 88% with its highest expansion by 47% in the period 2005–2010. Settlements and croplands remained relatively constant throughout the 13 years. Comparison of multiscale classification suggests that, although high spatial resolutions are clearly beneficial, all datasets were useful to delineate linear features such as roads. Consequently, the results of this study provide an effective evaluation for the potential of Random Forest for extracting relatively narrow linear features such as roads from multiscale satellite images and map products that are possible to use for detailed land degradation assessments.

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“…For better environmental analysis and making sound decisions, reliable information about the land-use/cover is vital (Basanna and Wodeyar, 2013;Tilahun and Teferi, 2015;Rawat and Kumar, 2015;El-Hattab, 2016).The Remote Sensing technique is a very advanced method and has great role for obtaining timely and valid information about land cover status (Fu, 2003;Manandhar et al, 2009;Blaschke, 2010;Subhash, 2012;Fichera et al, 2012;Forkuo and Frimpong, 2012;Baynard, 2013;Esmail et al, 2016;Haque and Basak, 2017;Wang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Land-use/cover change analysis using Remote Sensing techniques in the landscape of Majang Zone of Gambella Region, Ethiopia

Muke¹,

Haile²

2018

Afr. J. Environ. Sci. Technol.

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Recently, forest land grant for investment which is often misquoted as bare land is posing a challenge to biodiversity conservation efforts in the Majang Zone of Gambella Region, Ethiopia. On the other hand, Majang zone has always been known for dense forest cover and rich biodiversity; but recently threatened due to plantation investment. In order to tackle such prevailing problems, timely information about past and existing land-use/cover scenarios is needed. This study therefore aim to drive reliable information about land-use/cover trends for the last 30 years using Remote Sensing techniques. Landsat Thematic Mapper (TM) for year 1987 and Landsat 8 Operational Land Imager (OLI) for year 2016 were used for image classification. By applying all the approaches and algorithms recommended for image classification, six major land-use/cover classes were identified. The landscape ever covered with dense forest was dramatically updated to new land-use/cover. The 1987 land-use/cover map put forest as the major land cover accounted for 86.73%. However, findings from recent satellite image uncovered new land-use/cover class-plantation accounted for16.16 % that comes out of almost none existent land use pattern in 1987. The result also showed that agricultural land and settlement expanded at alarming rate (3.4 and 0.13 hectare) per year respectively but, the forest cover is the most altered part decreasing by 0.32 hectare per year. Thus, it is important to take urgent action against further conversion of forest to other land cover class, which might have negative impacts in advance on the remaining natural forest.

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Remote Sensing Applications: Beyond Land-Use and Land-Cover Change

Cited by 18 publications

References 78 publications

A time series processing tool to extract climate-driven interannual vegetation dynamics using Ensemble Empirical Mode Decomposition (EEMD)

A time series processing tool to extract climate-driven interannual vegetation dynamics using Ensemble Empirical Mode Decomposition (EEMD)

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Land-use/cover change analysis using Remote Sensing techniques in the landscape of Majang Zone of Gambella Region, Ethiopia

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