Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems

Vogelmann, James E.; Xian, George; Homer, Collin G.; Tolk, Brian

doi:10.1016/j.rse.2011.06.027

Cited by 172 publications

(125 citation statements)

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“…The damage to vegetation caused by fire results in a significant decrease of the NDVI. Because of its applicability to a wide range of sensors, including those lacking SWIR bands, its simplicity and relatively good performance, NDVI has been used extensively to map burned areas, assess burn severity and monitor vegetation recovery [7,14,[55][56][57]. Similar to NDVI, GEMI uses the visible red and near infrared domains but was designed following a non-linear approach in an attempt to reduce undesired atmospheric effects.…”

Section: Index Full Name Abbreviation Equation Referencementioning

confidence: 99%

Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

2018

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Abstract:Remote mountainous regions are among the Earth's last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions.

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Section: Index Full Name Abbreviation Equation Referencementioning

confidence: 99%

Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

2018

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“…, in the study that developed the harmonic modeling approach we adapted to detect microrefugia, demonstrated that dense time series of all available Landsat data enabled detection of subtle forest thinning. Other studies, more commonly using time series of annual images (including composite images; eg, Roy et al, 2010), emphasize the power of LTS to detect gradual processes including forest decline due to diffuse disturbances such as insect outbreaks or drought (Ahmed et al, 2017;Cohen et al, 2016;Deel et al, 2012;Kennedy et al, 2010), forest succession and woodland densification (Vogelmann et al, 2012), and variation in ecosystem recovery following disturbance (Kennedy et al, 2007(Kennedy et al, , 2010Lawrence and Ripple, 1999;Storey et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

Andrew

Warrener

2017

Remote Sensing of Environment

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(in press). Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics. Remote Sensing of Environment. Accepted 4 August 2017. DOI:https: //doi.org/10.1016/j.rse.2017.08.005 Disclaimer:The PDF document is a copy of the final version of this manuscript that was subsequently accepted by the journal for publication. The paper has been through peer review, but it has not been subject to any additional copy-editing or journal specific formatting (so will look different from the final version of record, which may be accessed following the DOI above depending on your access situation). 2 AbstractMicrorefugia are sites with stable, high quality habitat within landscapes characterized by dynamic environmental conditions driven by climate variability or ecological disturbances. There is considerable interest in the potential of microrefugia to provide climate change resilience to landscapes and to biodiversity conservation. Although attractive conceptually, there is yet little guidance on how to identify climate change microrefugia in order to study and protect them, and the data required to do so are often lacking. This study demonstrates how time series remote sensing, using all available Landsat images of a study area, can be used to directly detect microrefugia maintained by water subsidies in a semi-arid landscape in southwest Western Australia.Microrefugia were identified as pixels with abundant vegetation and consistent vegetation dynamics between wet and dry years. At every pixel, a harmonic model was fit to the intra-annual time series of vegetation index values compiled from the wettest years in the Landsat-5 Thematic Mapper (TM) archive. This model was then used to predict the phenological cycle of the driest years at that pixel. Candidate microrefugia were defined to be those pixels with (1) high vegetation activity in dry years and (2) highly predictable phenologies that are consistent regardless of the weather conditions experienced in a given year. Spatial relationships between candidate microrefugia and landscape features associated with elevated moisture availability (thought to drive climate microrefugia in these semi-arid landscapes) were assessed. The candidate microrefugia show great promise. Evaluations against high-resolution imagery reveal that candidate microrefugia most likely buffer against drought, although refugia from other disturbances, especially fire, were also detected. In contrast, spatial proxies of the physical features expected to maintain microrefugia failed to adequately represent the distribution of microrefugia across the landscape, likely due to data quality and the heterogeneity of microrefugia. Direct detection of microrefugia with Earth observation data is a promising solution in data limited regions. Landsat time series analyses are well suited to this application as they can characterize both the habitat quality and stability aspects of microrefugia.

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“…The study highlights the benefits of using Landsat satellite imagery to detect, quantify and understand the trend in land cover change in forest environments. Vogelmann et al (2012) discuss the use of Landsat imagery for forest change analysis and they depict that Landsat imagery is applicable for detecting abrupt and gradual ecosystem changes. However in order to get more accurate result among forest disturbance is recommended to use annual time series images.…”

Section: Discussionmentioning

confidence: 99%

Temporal and spatial variation of forest coverage in Apuseni natural park, 2000-2014 period

Furtuna¹

2017

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ABSTRACT:Specific knowledge of forest cover change over a protected area, such as Apuseni Natural Park, is important for resource management, ecosystem quality and biodiversity protection. Landsat satellite imagery archive allows the annual changes characterization of forest cover over a long period of time. In the study area significant changes were detected in forest dynamic during the 2000-2014 period, it has been found, that around 15% of forests were disturbed during the period analysed. Spatial and temporal distributions of forest cover during the time period shows specific locations of forest loss, they are mainly spotted in the coniferous forest, and in the central part of the park. The overall accuracy was obtained 88.01%, the lowest values obtained was for 2002 (55.73%) and the higher for the year 2014 (95.48%). The forest loss year-to-year revealed the higher loss rate for year 2007 (21.13%).

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Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems

Abstract: Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems" (2012).

Cited by 172 publications

References 69 publications

Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

Temporal and spatial variation of forest coverage in Apuseni natural park, 2000-2014 period

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