Classification and Change Detection Using Landsat TM Data

Song, Conghe; Woodcock, Curtis E.; Seto, Karen C.; Lenney, Mary Pax; Macomber, Scott

doi:10.1016/s0034-4257(00)00169-3

Cited by 1,464 publications

(615 citation statements)

References 37 publications

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“…All these masks are stored in the DC as they can be useful when analyzing data. The second step concerns the conversion to SR by applying and correcting atmospheric effect (Song, Woodcock, Seto, Lenney, & Macomber, 2001). After testing different algorithms such as Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) (Schmidt, Jenkerson, Masek, Vermote, & Gao, 2013), R packages (RStoolbox and Landsat), Grass GIS algorithms (i.landsat.atcorr and i.atcorr), and the Simplifié Modèle d'Atmosphérique Correction (SMAC) (http://www.cesbio.ups-tlse.fr/multitemp/?page_id=2975), the final choice (based on efficiency, reliability, and easiness of integration in the workflow) was the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) algorithm available in the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI 9 ) software (Vermote, Tanre, Deuze, Herman, & Morcette, 1997).…”

Section: Landsat Scenes Pre-processingmentioning

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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Section: Landsat Scenes Pre-processingmentioning

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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“…We used the procedure suggested by Chander et al (2009) to convert the original digital numbers to radiance values (L λ ), and the image-based cosine of the solar transmittance (COST) method (Chavez, 1996), to convert radiance to surface reflectance (ρ). The formulae described in Song et al (2001) assuming 1% surface reflectance for dark objects (Chavez, 1989(Chavez, , 1996 allowed us to calculate the path radiance (L p ) values. We estimated the optical thickness for Rayleigh scattering (ρ r ) following the equation specified in Kaufman (1989).…”

Section: Preprocessing Of the Landsat Imagesmentioning

confidence: 99%

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Fernández–Manso

Quintano

Roberts

2016

Remote Sensing of Environment

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Mediterranean ecosystems are adapted to recurrent forest fires by having regeneration mechanisms that overcome the immediate effects of fire. However, the increasing frequency of fires in most European Mediterranean countries is challenging the natural regrowth capability of these ecosystems. In this context, monitoring post-fire vegetation recovery is a priority for forest management and soil erosion control. In this work, a 13-year series (1999-2011) of Landsat 5 Thematic Mapper (TM)/Landsat 7 Enhanced Thematic Mapper (ETM +) data was used to model post-fire vegetation recovery as a function of burn severity and to quantify post-fire resilience as a measure of vegetation cover regrowth. We evaluated a large forest fire located in Spain that burned approximately 30 km 2 of Pinus pinaster Ait. in August 1998. 88 field plots of four burn severity levels (unburned, low, moderate and high) were measured in the field a year after the fire. As a variable representative of vegetation, we chose the shade normalized green vegetation fraction image (SGV) obtained by applying Multiple Endmember Spectral Mixture Analysis (MESMA) to the original Landsat TM/ETM + images. The SGV values were extracted for the 88 field plots and, after performing a one-way analysis of variance (ANOVA), a Fisher's Least Significant Difference (LSD) test allowed us to estimate resilience of vegetation cover as the number of post-fire years exhibiting a statistically significant difference between burned and unburned areas. Next, SGV values were referenced to unburned control plots values and the vegetation recovery index (VRI) was defined. The evolution in time curve of VRI for low, moderate and highly fire affected vegetation was fit using trend models (specifically, an exponential trend for VRI in high and moderate burn severity levels; a linear trend for low burn severity level, Root Mean Square Error, RMSE = 0.18, 0.13, and 0.09, respectively). We observed that vegetation cover affected by low severity fire recovered to its original state after 7 years, and vegetation cover affected by moderate severity recovered after 13 years. Vegetation affected by high severity fire was estimated to recover after 20 years. We conclude that VRI time series based on multitemporal MESMA fractions from Landsat data can be considered a valuable indicator of the post-fire vegetation cover recovery. Its temporal evolution represented post-fire vegetation cover regrowth adequately and facilitated the estimate of vegetation cover resilience in Mediterranean forests.

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“…NDVI is the most commonly used and well-known vegetation index and, as previously mentioned by Song et al (2001), has been used in several studies to monitor vegetation dynamics (Sader 1987;Lenney et al 1996;Michener and Houhoulis 1997).…”

Section: Image Processing and Classificationmentioning

confidence: 99%

Historical and future land-cover changes in the Upper Ganges basin of India

Tsarouchi

Mijic

Moulds

et al. 2014

International Journal of Remote Sensing

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The green revolution represents one of the greatest environmental changes in India over the last century. The Upper Ganges (UG) basin is experiencing rapid rates of change of land cover and irrigation practices. In this study, we investigated the historical rate of change and created future scenario projections by means of 30 m-resolution multitemporal Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus data of the UG basin. Post-classification change analysis methods were applied to Landsat images in order to detect and quantify land-cover changes in the UG basin. Subsequently, Markov chain analysis was applied to project future scenarios of land-cover change. Fifteen different scenarios were generated based on historic land-cover change. These scenarios diverged in terms of future projections, highlighting the dynamic nature of the changes. This study has shown that between the years 1984 and 2010 the main land-cover change trends are conversion from shrubs to forest (+4.7%), urbanization (+5.8%), agricultural expansion (+1.3%), and loss of barren land (-9.5%). The land-cover change patterns in the UG basin were mapped and quantified, showing the capability of Landsat data in providing accurate land-cover maps. These results, in combination with those derived from the Markov model, provide the necessary evidence base to support regional land-use planning and develop future-proof water resource management strategies.

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Classification and Change Detection Using Landsat TM Data

Cited by 1,464 publications

References 37 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)

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Historical and future land-cover changes in the Upper Ganges basin of India

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