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 Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

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 A New Application of the Disturbance Index for Fire Severity in Coastal Dunes

2021 ◽  
Vol 13 (23) ◽  
pp. 4739
Author(s):  
Marcio D. DaSilva ◽  
David Bruce ◽  
Patrick A. Hesp ◽  
Graziela Miot da Silva
Keyword(s):  
Vegetation Index ◽  
Fire Severity ◽  
Coastal Dune ◽  
Burn Severity ◽  
Soil Conditions ◽  
Initiation Mechanism ◽  
Disturbance Index ◽  
Dune Systems ◽  
Normalised Difference Vegetation Index ◽  
Vegetation Loss

Fires are a disturbance that can lead to short term dune destabilisation and have been suggested to be an initiation mechanism of a transgressive dune phase when paired with changing climatic conditions. Fire severity is one potential factor that could explain subsequent coastal dune destabilisations, but contemporary evidence of destabilisation following fire is lacking. In addition, the suitability of conventional satellite Earth Observation methods to detect the impacts of fire and the relative fire severity in coastal dune environments is in question. Widely applied satellite-derived burn indices (Normalised Burn Index and Normalised Difference Vegetation Index) have been suggested to underestimate the effects of fire in heterogenous landscapes or areas with sparse vegetation cover. This work assesses burn severity from high resolution aerial and Sentinel 2 satellite imagery following the 2019/2020 Black Summer fires on Kangaroo Island in South Australia, to assess the efficacy of commonly used satellite indices, and validate a new method for assessing fire severity in coastal dune systems. The results presented here show that the widely applied burn indices derived from NBR differentially assess vegetation loss and fire severity when compared in discrete soil groups across a landscape that experienced a very high severity fire. A new application of the Tasselled Cap Transformation (TCT) and Disturbance Index (DI) is presented. The differenced Disturbance Index (dDI) improves the estimation of burn severity, relative vegetation loss, and minimises the effects of differing soil conditions in the highly heterogenous landscape of Kangaroo Island. Results suggest that this new application of TCT is better suited to diverse environments like Mediterranean and semi-arid coastal regions than existing indices and can be used to better assess the effects of fire and potential remobilisation of coastal dune systems.

scholarly journals A feasibility study on the association between residential greenness and neurocognitive function in middle-aged Bulgarians

2019 ◽  
Vol 70 (3) ◽  
pp. 173-185
Author(s):  
Angel M. Dzhambov ◽  
Karamfil M. Bahchevanov ◽  
Kostadin A. Chompalov ◽  
Penka A. Atanassova
Keyword(s):  
Vegetation Index ◽  
Structural Integrity ◽  
Brain Regions ◽  
Neurocognitive Function ◽  
Future Research ◽  
Central Adiposity ◽  
Imaging Data ◽  
Middle Aged ◽  
Convenience Sample ◽  
Normalised Difference Vegetation Index

AbstractRecent research has indicated that exposure to residential vegetation (“greenness”) may be protective against cognitive decline and may support the integrity of the corresponding brain structures. However, not much is known about these effects, especially in less affluent countries and in middle-aged populations. In this study, we investigated the associations between greenness and neurocognitive function. We used a convenience sample of 112 middle-aged Bulgarians and two cognitive tests: the Consortium to Establish a Registry for Alzheimer’s Disease Neuropsychological Battery (CERAD-NB) and the Montreal Cognitive Assessment (MoCA). In addition, structural brain imaging data were available for 25 participants. Participants’ home address was used to link cognition scores to the normalised difference vegetation index (NDVI), a measure of overall neighbourhood vegetation level (radii from 100 to 1,000 m). Results indicated that higher NDVI was consistently associated with higher CERAD-NB and MoCA scores across radial buffers and adjustment scenarios. Lower waist circumference mediated the effect of NDVI on CERAD-NB. NDVI100-m was positively associated with average cortical thickness across both hemispheres, but these correlations turned marginally significant (P<0.1) after correction for false discovery rate due to multiple comparisons. In conclusion, living in a greener neighbourhood might be associated with better cognitive function in middle-aged Bulgarians, with lower central adiposity partially accounting for this effect. Tentative evidence suggests that greenness might also contribute to structural integrity in the brain regions regulating cognitive functions. Future research should build upon our findings and investigate larger and more representative population groups.

scholarly journals Residential green and blue space associated with better mental health: a pilot follow-up study in university students

2018 ◽  
Vol 69 (4) ◽  
pp. 340-349 ◽  
Author(s):  
Angel M. Dzhambov
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Urban Environment ◽  
Vegetation Index ◽  
Green Space ◽  
Future Research ◽  
Cross Sectional ◽  
Normalised Difference Vegetation Index ◽  
Anxiety Depression

AbstractPrevious research has suggested that natural urban environment (green space and blue space) benefit mental health, but only a few longitudinal studies have explored the underlying mechanisms. In this pilot study we aimed to examine mechanisms/variables mediating associations between residential green/blue space and symptoms of anxiety/depression in 109 Bulgarian students from Plovdiv university. The students were followed from the beginning to the end of the school year (October 2017 to May 2018). Residential green space was defined as the mean of the normalised difference vegetation index (NDVI) in circular buffers of 100, 300, and 500 m around their residences. Blue space was assessed based on its presence in the same buffers. Levels of anxiety/depression were assessed using the 12-item General Health Questionnaire. The investigated mediator variables included residential noise (LAeq) and air pollution (NO2), environmental annoyance, perceived restorative quality of the neighbourhood, neighbourhood social cohesion, physical activity, and sleep disturbance. Cross-sectional data (obtained at baseline) showed that higher NDVI correlated with better mental health only indirectly through higher physical activity and restorative quality. Longitudinal (follow-up) data showed improved mental health but no significant effect of mediator variables. Similarly, blue space correlated with better mental health in all models, but physical activity and restorative quality were significant mediator variables only in the cross-sectional analysis. Our findings support that green space and blue space are psychologically restorative features in urban environment. Future research should replicate these findings in the general population and employ longitudinal modelling tailored to the specific mechanisms under study.

Time series of high-resolution images enhances efforts to monitor post-fire condition and recovery, Waldo Canyon fire, Colorado, USA

2018 ◽  
Vol 27 (10) ◽  
pp. 699 ◽  
Author(s):  
Melanie K. Vanderhoof ◽  
Clifton Burt ◽  
Todd J. Hawbaker
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Vegetation Index ◽  
Burn Severity ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Object Based ◽  
Fire Condition ◽  
Normalised Difference Vegetation Index ◽  
High Resolution Images

Interpretations of post-fire condition and rates of vegetation recovery can influence management priorities, actions and perception of latent risks from landslides and floods. In this study, we used the Waldo Canyon fire (2012, Colorado Springs, Colorado, USA) as a case study to explore how a time series (2011–2016) of high-resolution images can be used to delineate burn extent and severity, as well as quantify post-fire vegetation recovery. We applied an object-based approach to map burn severity and vegetation recovery using Worldview-2, Worldview-3 and QuickBird-2 imagery. The burned area was classified as 51% high, 20% moderate and 29% low burn-severity. Across the burn extent, the shrub cover class showed a rapid recovery, resprouting vigorously within 1 year, whereas 4 years post-fire, areas previously dominated by conifers were divided approximately equally between being classified as dominated by quaking aspen saplings with herbaceous species in the understorey or minimally recovered. Relative to using a pixel-based Normalised Difference Vegetation Index (NDVI), our object-based approach showed higher rates of revegetation. High-resolution imagery can provide an effective means to monitor post-fire site conditions and complement more prevalent efforts with moderate- and coarse-resolution sensors.

Characterising soil moisture regimes on poorly drained soils in Ireland using optical satellite derived vegetation indices and the OPTRAM model

2021 ◽  
Author(s):  
Rumia Basu ◽  
Colin Brown ◽  
Patrick Tuohy ◽  
Eve Daly
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Crop Production ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Accurate Information ◽  
Effective Manner ◽  
Normalised Difference Vegetation Index ◽  
Drainage Capacity ◽  
Poorly Drained Soils

&lt;p&gt;Soil drainage capacity is the degree and frequency at which the soil is free of saturation. It influences land use and management, soil nutrient cycling and greenhouse gas fluxes. Accurate information on drainage conditions is crucial for crop production and management and fundamental in developing strategies to adhere to environmental sustainability goals. This is particularly important in Ireland where approximately 50% of the soils are classified as &amp;#8220;marginal&amp;#8221;. These are mainly poorly drained soils which negatively impact plant growth and productivity.&lt;/p&gt;&lt;p&gt;Soil moisture acts as a proxy for drainage capacity. Timely and accurate information on soil moisture allows for precision management strategies. It aids in designing effective interventions on farms for artificial drainage works which are often assessed by information on soil moisture, soil type and hydrology. Such data are conventionally acquired by in-situ point sampling techniques which are costly and time consuming. Remote sensing has the potential to provide a solution by allowing simultaneous coverage of large geographic areas, quickly and in a cost effective manner.&lt;/p&gt;&lt;p&gt;This study uses optical remote sensing data from Sentinel 2 to derive information on soil moisture conditions on selected sites in Ireland.&amp;#160; We develop the OPTRAM model of Sadeghi et al (2017) by exploring the use of remote sensing based vegetation indices such as the Normalised Difference Vegetation index, Enhanced Vegetation Index and Normalised Difference Red Edge Index for the years 2015-2020 along with short wave transformed infrared reflectance to estimate soil moisture variations for our study areas. We show that &amp;#160;non-linear estimates of the wet and dry edge curves in the model are better suited for Ireland, which is dominated by wet conditions for most of the year and also identify the best vegetation indices for studying soil moisture variations.&lt;/p&gt;

Evaluating the factors responsible for post-fire water quality response in forests of the western USA

2019 ◽  
Vol 28 (10) ◽  
pp. 769 ◽  
Author(s):  
Ashley J. Rust ◽  
Samuel Saxe ◽  
John McCray ◽  
Charles C. Rhoades ◽  
Terri S. Hogue
Keyword(s):  
Water Quality ◽  
Vegetation Index ◽  
Field Capacity ◽  
Phosphorus Loading ◽  
Burn Severity ◽  
Nitrogen And Phosphorus ◽  
Western Us ◽  
Normalised Difference Vegetation Index ◽  
Green Vegetation ◽  
Quality Response

Wildfires commonly increase nutrient, carbon, sediment and metal inputs to streams, yet the factors responsible for the type, magnitude and duration of water quality effects are poorly understood. Prior work by the current authors found increased nitrogen, phosphorus and cation exports were common the first 5 post-fire years from a synthesis of 159 wildfires across the western United States. In the current study, an analysis is undertaken to determine factors that best explain post-fire streamwater responses observed in those watersheds. Increased post-fire total nitrogen and phosphorus loading were proportional to the catchment extent of moderate and high burn severity. While post-fire dissolved metal concentrations were correlated with pre-fire soil organic matter. Total metal concentration increased where post-fire Normalised Difference Vegetation Index, a remote sensing indicator of live green vegetation, was low. When pre-fire soil field capacity exceeded 17%, there was a 750% median increase in total metals export to streams. Overall, the current analysis identified burn severity, post-fire vegetation cover and several soil properties as the key variables explaining extended post-fire water quality response across a broad range of conditions found in the western US.

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.

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