scholarly journals Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece

2010 ◽  
Vol 19 (5) ◽  
pp. 558 ◽  
Author(s):  
Sander Veraverbeke ◽  
Willem W. Verstraeten ◽  
Stefaan Lhermitte ◽  
Rudi Goossens
Keyword(s):  
Field Data ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Thematic Mapper ◽  
Alternative Methods ◽  
Burn Severity ◽  
Landsat Thematic Mapper ◽  
Spectral Indices ◽  
Moisture Index ◽  
Normalised Difference Vegetation Index

A vast area (more than 100 000 ha) of forest, shrubs and agricultural land burned on the Peloponnese peninsula in Greece during the 2007 summer. Three pre- and post-fire differenced Landsat Thematic Mapper (TM)-derived spectral indices were correlated with field data of burn severity for these devastating fires. These spectral indices were the Normalised Difference Vegetation Index (NDVI), the Normalised Difference Moisture Index (NDMI) and the Normalised Burn Ratio (NBR). The field data consist of 160 Geo Composite Burn Index (GeoCBI) plots. In addition, indices were evaluated in terms of optimality. The optimality statistic is a measure for the index’s sensitivity to fire-induced vegetation depletion. Results show that the GeoCBI–dNBR (differenced NBR) approach yields a moderately high R2 = 0.65 whereas the correlation between field data and the differenced NDMI (dNDMI) and the differenced NDVI (dNDVI) was clearly lower (respectively R2 = 0.50 and R2 = 0.46). The dNBR also outperformed the dNDMI and dNDVI in terms of optimality. The resulting median dNBR optimality equalled 0.51 whereas the median dNDMI and dNDVI optimality values were respectively 0.50 and 0.40 (differences significant for P < 0.001). However, inaccuracies observed in the spectral indices approach indicate that there is room for improvement. This could imply improved preprocessing, revised index design or alternative methods.

Assessing the susceptibility of semiarid rangelands to wildfires using Terra MODIS and Landsat Thematic Mapper data

2011 ◽  
Vol 20 (5) ◽  
pp. 690 ◽  
Author(s):  
Fang Chen ◽  
Keith T. Weber ◽  
Jamey Anderson ◽  
Bhushan Gokhal
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Late Summer ◽  
Thematic Mapper ◽  
Fuel Load ◽  
Landsat Thematic Mapper ◽  
Change Analysis ◽  
Active Growth ◽  
Normalised Difference Vegetation Index

In order to monitor wildfires at broad spatial scales and with frequent periodicity, satellite remote sensing techniques have been used in many studies. Rangeland susceptibility to wildfires closely relates to accumulated fuel load. The normalised difference vegetation index (NDVI) and fraction of photosynthetically active radiation (fPAR) are key variables used by many ecological models to estimate biomass and vegetation productivity. Subsequently, both NDVI and fPAR data have become an indirect means of deriving fuel load information. For these reasons, NDVI and fPAR, derived from the Moderate Resolution Imaging Spectroradiometer on-board Terra and Landsat Thematic Mapper imagery, were used to represent prefire vegetation changes in fuel load preceding the Millennial and Crystal Fires of 2000 and 2006 in the rangelands of south-east Idaho respectively. NDVI and fPAR change maps were calculated between active growth and late-summer senescence periods and compared with precipitation, temperature, forage biomass and percentage ground cover data. The results indicate that NDVI and fPAR value changes 2 years before the fire were greater than those 1 year before fire as an abundance of grasses existed 2 years before each wildfire based on field forage biomass sampling. NDVI and fPAR have direct implication for the assessment of prefire vegetation change. Therefore, rangeland susceptibility to wildfire may be estimated using NDVI and fPAR change analysis. Furthermore, fPAR change data may be included as an input source for early fire warning models, and may increase the accuracy and efficiency of fire and fuel load management in semiarid rangelands.

scholarly journals Use of Landsat ETM+ data for delineation of water bodies in hilly zones

2010 ◽  
Vol 13 (4) ◽  
pp. 661-671 ◽  
Author(s):  
Vijay S. Bhagat ◽  
Kishor R. Sonawane
Keyword(s):  
Water Body ◽  
Vegetation Index ◽  
Thematic Mapper ◽  
Water Bodies ◽  
Wetness Index ◽  
Surface Wetness ◽  
Small Dams ◽  
Raster Image ◽  
Normalised Difference Vegetation Index ◽  
Water Based

The remotely sensed Landsat Enhanced Thematic Mapper Plus (ETM+) dataset is used for the detection and delineation of water bodies in hilly zones. The water bodies were detected using Surface Wetness Index (SWI), Normalised Difference Vegetation Index (NDVI) and a slope map. The assessment of areas under dense vegetation in water bodies is omitted in the combined map prepared using classified raster images showing (1) the distribution of ‘water’ and ‘non-water’ based on SWI and (2) the distribution of ‘vegetation’ and ‘non-vegetation’ based on NDVI. The shadows' effect in estimated areas under water bodies is detected and delineated using the combination of (1) a combined raster image (classified SWI and NDVI) and (2) a slope map. About 3.8% (1370 ha) of the total area reviewed is estimated under water bodies with 91.74% overall accuracy. The water bodies include (1) major and minor dams, (2) watered streams, (3) springs distributed in foothill zones and (4) small dams on minor streams. The relatively smaller water body objects, i.e. streams and springs, have estimated less producer's (92–96%) and user's (85–92%) accuracy than the major water bodies, i.e. 96.77% producer's and 100% for user's accuracy.

scholarly journals Pinus halepensis regeneration after a wildfire in a semiarid environment: assessment using multitemporal Landsat images

2011 ◽  
Vol 20 (2) ◽  
pp. 195 ◽  
Author(s):  
Sergio M. Vicente-Serrano ◽  
Fernando Pérez-Cabello ◽  
Teodoro Lasanta
Keyword(s):  
Spatial Pattern ◽  
Vegetation Index ◽  
Pinus Halepensis ◽  
Strong Relationship ◽  
Burn Severity ◽  
Spatial And Temporal Patterns ◽  
Landsat Images ◽  
Normalised Difference Vegetation Index ◽  
Spatial Differences ◽  
Terrain Elevation

We studied the spatial and temporal patterns of forest regeneration using a 24-year time series of Landsat images and the normalised difference vegetation index (NDVI) in a homogeneous Pinus halepensis forest, 3000 ha of which were extensively burned in 1995. We demonstrated a progressive slow and linear recovery in NDVI values, based on Landsat images between 1997 and 2007. The forest tended to recover to pre-disturbance conditions, both with respect to the magnitude of the NDVI and in terms of the spatial pattern. We found that the spatial differences in the rates of NDVI recovery were not affected by the burn severity. Moreover, burn severity did not affect the rates of NDVI recovery after the fire. Although highly homogeneous P. halepensis regeneration was the dominant pattern in the study area (more than the 70% of the burn area showed positive and significant trends), some spatial differences in the magnitude of change were observed. The forest tended to recover the spatial pattern corresponding to pre-fire conditions, although it was difficult to establish whether terrain elevation or previous tree size and density were the main governing factors, given the strong relationship between them.

scholarly journals Mapping of Sub-Optimal Land Based on NDVI Sentinel 2a Value: Preliminary Study

2020 ◽  
Vol 17 (3) ◽  
pp. 194-204
Author(s):  
Indarto Indarto ◽  
Rufiani Nadzirah ◽  
Hadrian Reksa Belagama
Keyword(s):  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Vegetation Density ◽  
Residential Areas ◽  
Primary Input ◽  
Normalised Difference Vegetation Index ◽  
Sentinel 2A ◽  
Marginal Agricultural Land ◽  
Potential Use

Normalised Difference Vegetation Index (NDVI) is one of the vegetation indices used to analyse vegetation density. This study presents the potential use of NDVI to map dry-marginal-agricultural land (Dry-MAL). The study conducted in the eastern part of Situbondo, which includes three districts, namely, Arjasa, Asembagus and Jangkar. Sentinel-2A (recorded in 2018) and 450 Control points (GCPs) are used as the primary input. The region is an area with distinctive climate characteristics, where the dry season is longer than the rainy season. Analysis using "SNAP plug-ins" and "QGIS". Research procedures include (1) data inventory, (2) data pre-processing, (3) data processing and (4) accuracy testing. The NDVI classification can distinguish six (6) classes of land-use, i.e., water bodies, residential areas, dry MAL, non-irrigated rural area, irrigated paddy fields, forest-plantations. The NDVI classification produces Overall and Kappa accuracy values =  66,9% and 61,6%. Although the overall and kappa accuracy is below the standard, however, the result will benefit for further research of index vegetation or soil more applied for the identification of Dry-MAL

Beyond Landsat: a comparison of four satellite sensors for detecting burn severity in ponderosa pine forests of the Gila Wilderness, NM, USA

2010 ◽  
Vol 19 (4) ◽  
pp. 449 ◽  
Author(s):  
Zachary A. Holden ◽  
Penelope Morgan ◽  
Alistair M. S. Smith ◽  
Lee Vierling
Keyword(s):  
Ponderosa Pine ◽  
Vegetation Index ◽  
Thermal Emission ◽  
Burn Severity ◽  
Future Research ◽  
Accurate Information ◽  
Ponderosa Pine Forests ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Gila Wilderness

Methods of remotely measuring burn severity are needed to evaluate the ecological and environmental impacts of large, remote wildland fires. The challenges that were associated with the Landsat program highlight the need to evaluate alternative sensors for characterising post-fire effects. We compared statistical correlations between 55 Composite Burn Index field plots and spectral indices from four satellite sensors varying in spatial and spectral resolution on the 2003 Dry Lakes Fire in the Gila Wilderness, NM. Where spectrally feasible, burn severity was evaluated using the differenced Enhanced Vegetation Index (dEVI), differenced Normalised Difference Vegetation Index (dNDVI) and the differenced Normalised Burn Ratio (dNBR). Both the dEVI derived from Quickbird and the dNBR derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) showed similar or slightly improved correlations over the dNBR derived from Landsat Thematic Mapper data (R2 = 0.82, 0.84, and 0.78 respectively). The relatively coarse resolution MODIS-derived NDVI image was weakly correlated with ground data (R2 = 0.38). Our results suggest that moderately high-resolution satellite sensors like Quickbird and ASTER have potential for providing accurate information about burn severity. Future research should develop stronger links between higher-resolution satellite data and burn severity across a range of environments.

scholarly journals Green Space Assessment and Management in Biscay Province, Spain using Remote Sensing Technology

2021 ◽  
Vol 15 (4) ◽  
pp. 21-43
Author(s):  
Esther O. Makinde ◽  
Cristina M. Andonegui ◽  
Ainhoa A. Vicario
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Vegetation Index ◽  
Green Space ◽  
Total Carbon ◽  
Spectral Indices ◽  
Moisture Index ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Forest Lands

Our ecosystem, particularly forest lands, contains huge amounts of carbon storage in the world today. This study estimated the above ground biomass and carbon stock in the green space of Bilbao Spain using remote sensing technology. Landsat ETM+ and OLI satellite images for year 1999, 2009 and 2019 were used to assess its land use land cover (LULC), change detection, spectral indices and model biomass based on linear regression. The result of the LULC showed that there was an increase in forest vegetation by 12.5% from 1999 to 2009 and a further increase by 2.3% in 2019. However, plantation cover had decreased by 3.5% from 1999–2009; while wetlands had also decreased by 9% within the same period. There was, however, an increase in plantation cover from 2009 to 2019 by 2.1% but a further decrease in wetlands of 4.3%. Further results revealed a positive correlation across the three decades between the widely used Normalized Differential Vegetation Index (NDVI) with other spectral indices such as Enhance Vegetation Index (EVI) and Normalized Differential Moisture Index (NDMI) for biomass were: for 1999 EVI (R2 = 0.1826), NDMI (R2 = 0.0117), for 2009 EVI (R2 = 0.2192), NDMI (R2 = 0.3322), for 2019EVI (R2 = 0.1258), NDMI (R2 = 0.8148). A reduction in the total carbon stock from 14,221.94 megatons in 1999 to 10,342.44 megatons 2019 was observed. This study concluded that there has been a reduction in the amount of carbon which the Biscay Forest can sequester.

scholarly journals Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas

2020 ◽  
Vol 12 (12) ◽  
pp. 2062 ◽  
Author(s):  
Sergio Morell-Monzó ◽  
Javier Estornell ◽  
María-Teresa Sebastiá-Frasquet
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Agricultural Sector ◽  
Agricultural Land ◽  
Land Abandonment ◽  
Moisture Index ◽  
Economic Changes ◽  
Management Measures ◽  
High Resolution Imagery ◽  
Sentinel 2

Agricultural land abandonment is an important environmental issue in Europe. The proper management of agricultural areas has important implications for ecosystem services (food production, biodiversity, climate regulation and the landscape). In the coming years, an increase of abandoned areas is expected due to socio-economic changes. The identification and quantification of abandoned agricultural plots is key for monitoring this process and for applying management measures. The Valencian Region (Spain) is an important fruit and vegetable producing area in Europe, and it has the most important citrus industry. However, this agricultural sector is highly threatened by diverse factors, which have accelerated land abandonment. Landsat and MODIS satellite images have been used to map land abandonment. However, these images do not give good results in areas with high spatial fragmentation and small-sized agricultural plots. Sentinel-2 and airborne imagery shows unexplored potential to overcome this thanks to higher spatial resolutions. In this work, three models were compared for mapping abandoned plots using Sentinel-2 with 10 m bands, Sentinel-2 with 10 m and 20 m bands, and airborne imagery with 1 m visible and near-infrared bands. A pixel-based classification approach was used, applying the Random Forests algorithm. The algorithm was trained with 144 plots and 100 decision trees. The results were validated using the hold-out method with 96 independent plots. The most accurate map was obtained using airborne images, the Enhanced Vegetation Index (EVI) and Thiam’s Transformed Vegetation Index (TTVI), with an overall accuracy of 88.5%. The map generated from Sentinel-2 images (10 m bands and the EVI and TTVI spectral indices) had an overall accuracy of 77.1%. Adding 20 m Sentinel-2 bands and the Normalized Difference Moisture Index (NDMI) did not improve the classification accuracy. According to the most accurate map, 4310 abandoned plots were detected in our study area, representing 32.5% of its agricultural surface. The proposed methodology proved to be useful for mapping citrus in highly fragmented areas, and it can be adapted to other crops.

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