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 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.

scholarly journals A simple approach for monitoring vegetation change using time series remote sensing analysis: A case study from the Thathe Vondo Area in Limpopo Province, South Africa

2021 ◽  
Vol 117 (7/8) ◽  
Author(s):  
Nndanduleni Muavhi
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Simple Approach ◽  
Limpopo Province ◽  
Difference Image ◽  
Normalised Difference Vegetation Index ◽  
Gain And Loss ◽  
The Difference

This study presents a simple approach of spatiotemporal change detection of vegetation cover based on analysis of time series remotely sensed images. The study was carried out at Thathe Vondo Area, which is characterised by episodic variation of vegetation gain and loss. This variation is attributable to timber and tea plantations and their production cycles, which periodically result in either vegetation gain or loss. The approach presented here was implemented on two ASTER images acquired in 2007 and 2017. It involved the combined use of band combination, unsupervised image classification and Normalised Difference Vegetation Index (NDVI) techniques. True colour composite (TCC) images for 2007 and 2017 were created from combination of bands 1, 2 and 3 in red, blue and green, respectively. The difference image of the TCC images was then generated to show the inconsistencies of vegetation cover between 2007 and 2017. For analytical simplicity and interpretability, the difference image was subjected to ISODATA unsupervised classification, which clustered pixels in the difference image into eight classes. Two ISODATA derived classes were interpreted as vegetation gain and one as vegetation loss. These classes were confirmed as regions of vegetation gain and loss by NDVI values of 2007 and 2017. In addition, the polygons of vegetation gain and loss regions were created and superimposed over the TCC images to further demonstrate the spatiotemporal vegetation change in the area. The vegetation change statistics show vegetation gain and loss of 10.62% and 2.03%, respectively, implying a vegetation gain of 8.59% over the selected decade.

scholarly journals Comparison of satellite-based evapotranspiration estimates over the Tibetan Plateau

2016 ◽  
Vol 20 (8) ◽  
pp. 3167-3182 ◽  
Author(s):  
Jian Peng ◽  
Alexander Loew ◽  
Xuelong Chen ◽  
Yaoming Ma ◽  
Zhongbo Su
Keyword(s):  
Tibetan Plateau ◽  
Vegetation Index ◽  
Water Cycle ◽  
Global Climate ◽  
Spatial Scales ◽  
Accurate Estimation ◽  
The Tibetan Plateau ◽  
North West ◽  
Comparison Results ◽  
Normalised Difference Vegetation Index

Abstract. The Tibetan Plateau (TP) plays a major role in regional and global climate. The understanding of latent heat (LE) flux can help to better describe the complex mechanisms and interactions between land and atmosphere. Despite its importance, accurate estimation of evapotranspiration (ET) over the TP remains challenging. Satellite observations allow for ET estimation at high temporal and spatial scales. The purpose of this paper is to provide a detailed cross-comparison of existing ET products over the TP. Six available ET products based on different approaches are included for comparison. Results show that all products capture the seasonal variability well with minimum ET in the winter and maximum ET in the summer. Regarding the spatial pattern, the High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) ET demonstrator dataset is very similar to the LandFlux-EVAL dataset (a benchmark ET product from the Global Energy and Water Cycle Experiment), with decreasing ET from the south-east to north-west over the TP. Further comparison against the LandFlux-EVAL over different sub-regions that are decided by different intervals of normalised difference vegetation index (NDVI), precipitation, and elevation reveals that HOLAPS agrees best with LandFlux-EVAL having the highest correlation coefficient (R) and the lowest root mean square difference (RMSD). These results indicate the potential for the application of the HOLAPS demonstrator dataset in understanding the land–atmosphere–biosphere interactions over the TP. In order to provide more accurate ET over the TP, model calibration, high accuracy forcing dataset, appropriate in situ measurements as well as other hydrological data such as runoff measurements are still needed.

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 Temporal Influences of Vegetation Cover (C) Dynamism on MUSLE Sediment Yield Estimates: NDVI Evaluation

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2707
Author(s):  
David Gwapedza ◽  
Denis Arthur Hughes ◽  
Andrew Robert Slaughter ◽  
Sukhmani Kaur Mantel
Keyword(s):  
Sediment Yield ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Scale Validation ◽  
Spatial Scales ◽  
Temporal Variations ◽  
Early Summer ◽  
Landsat 8 ◽  
Temporal Scales ◽  
Normalised Difference Vegetation Index

Vegetation cover is an important factor controlling erosion and sediment yield. Therefore, its effect is accounted for in both experimental and modelling studies of erosion and sediment yield. Numerous studies have been conducted to account for the effects of vegetation cover on erosion across spatial scales; however, little has been conducted across temporal scales. This study investigates changes in vegetation cover across multiple temporal scales in Eastern Cape, South Africa and how this affects erosion and sediment yield modelling in the Tsitsa River catchment. Earth observation analysis and sediment yield modelling are integrated within this study. Landsat 8 imagery was processed, and Normalised Difference Vegetation Index (NDVI) values were extracted and applied to parameterise the Modified Universal Soil Loss Equation (MUSLE) vegetation (C) factor. Imagery data from 2013–2018 were analysed for an inter-annual trend based on reference summer (March) images, while monthly imagery for the years 2016–2017 was analysed for intra-annual trends. The results indicate that the C exhibits more variation across the monthly timescale than the yearly timescale. Therefore, using a single month to represent the annual C factor increases uncertainty. The modelling shows that accounting for temporal variations in vegetation cover reduces cumulative simulated sediment by up to 85% across the inter-annual and 30% for the intra-annual scale. Validation with observed data confirmed that accounting for temporal variations brought cumulative sediment outputs closer to observations. Over-simulations are high in late autumn and early summer, when estimated C values are high. Accordingly, uncertainties are high in winter when low NDVI leads to high C, whereas dry organic matter provides some protection from erosion. The results of this study highlight the need to account for temporal variations in vegetation cover in sediment yield estimation but indicate the uncertainties associated with using NDVI to estimate C factor.

scholarly journals Glacier changes between 1971 and 2016 in the Jankar Chhu Watershed, Lahaul Himalaya, India

2018 ◽  
Vol 65 (249) ◽  
pp. 13-28 ◽  
Author(s):  
SURESH DAS ◽  
MILAP CHAND SHARMA
Keyword(s):  
Cloud Cover ◽  
Thematic Mapper ◽  
Landsat Thematic Mapper ◽  
Field Observations ◽  
Glacier Area ◽  
Ablation Zone ◽  
Change Analysis ◽  
Recession Rate ◽  
Glacier Changes ◽  
Sentinel 2A

ABSTRACTGlacier changes in the Jankar Chhu Watershed (JCW) of Chandrabhaga (Chenab) basin, Lahaul Himalaya were worked out based on Corona and Sentinel 2A images between 1971 and 2016. The JCW consists of 153 glaciers (>0.02 km2) with a total area of 185.6 ± 3.8 km2that include 82 glaciers with debris-covered ablation zone, comprising 10.9% of the total glacierized area as in 2016. Change analysis based on Corona (1971), Landsat (2000) and Sentinel 2A (2016) was restricted to 127 glaciers owing to the presence of cloud cover on 26 glaciers in 1971. A subset of glaciers was also mapped using Landsat Thematic Mapper (TM; 1989) image. The total glacier area decreased by 14.7 ± 4.3 km2(0.3 ± 0.1 km2a−¹). The number of glaciers in the JCW increased by four between 1971 and 2016 due to fragmentation. More recently (2000–16), recession rate has increased. Clean-ice area decreased by 21.8 ± 3.8 km2(0.5 ± 0.1 km2a−¹) while debris-covered ice increased by 7.2 ± 0.4 km2(0.2 ± 0.01 km2a−¹). Field observations of select glaciers also support derived recession trend in the JCW. Retreat rates in the JCW have been observed to be much lower than previously reported.

scholarly journals Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites

2020 ◽  
Author(s):  
Jakob Johann Assmann ◽  
Isla Heather Myers-Smith ◽  
Jeff Kerby ◽  
Andrew M. Cunliffe ◽  
Gergana N. Daskalova
Keyword(s):  
Vegetation Index ◽  
Leaf Growth ◽  
Spatial Scales ◽  
Yukon Territory ◽  
Ecological Impacts ◽  
The Arctic ◽  
Landsat 8 ◽  
Novel Technologies ◽  
Tundra Ecosystem ◽  
Normalised Difference Vegetation Index

Data across scales are required to monitor ecosystem responses to rapid warming in the Arctic and to interpret tundra greening trends. Here, we tested the correspondence among satellite- and drone-derived seasonal change in tundra greenness to identify optimal spatial scales for vegetation monitoring on Qikiqtaruk - Herschel Island in the Yukon Territory, Canada. We combined time-series of the Normalised Difference Vegetation Index (NDVI) from multispectral drone imagery and satellite data (Sentinel-2, Landsat 8 and MODIS) with ground-based observations for two growing seasons (2016 and 2017). We found high cross-season correspondence in plot mean greenness (drone-satellite Spearman’s ⍴ 0.67-0.87) and pixel-by-pixel greenness (drone-satellite R2 0.58-0.69) for eight one-hectare plots, with drones capturing lower NDVI values relative to the satellites. We identified a plateau in the spatial variation of tundra greenness at distances of around half a metre in the plots, suggesting that these grain sizes are optimal for monitoring such variation in the two most common vegetation types on the island. We further observed a notable loss of seasonal variation in the spatial heterogeneity of landscape greenness (46.2 - 63.9%) when aggregating from ultra-fine-grain drone pixels (approx. 0.05 m) to the size of medium-grain satellite pixels (10 – 30 m). Finally, seasonal changes in drone-derived greenness were highly correlated with measurements of leaf-growth in the ground-validation plots (mean Spearman’s ⍴ 0.70). These findings indicate that multispectral drone measurements can capture temporal plant growth dynamics across tundra landscapes. Overall, our results demonstrate that novel technologies such as drone platforms and compact multispectral sensors allow us to study ecological systems at previously inaccessible scales and fill gaps in our understanding of tundra ecosystem processes. Capturing fine-scale variation across tundra landscapes will improve predictions of the ecological impacts and climate feedbacks of environmental change in the Arctic.

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