Assessing the feasibility of a global model for multi-temporal burned area mapping using SPOT-VEGETATION data

Silva, João M. N.; Cadima, Jorge; Pereira, José M. C.; Grégoire, Jean‐Marie

doi:10.1080/01431160412331291251

Cited by 39 publications

(18 citation statements)

References 37 publications

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“…The variability of spectral signatures of burned area depends on the vegetation types that burn [111,112]. For example, the NIR reflectance decreases significantly due to the destruction of leaf cells within vegetation for all the types of vegetation [100,[112][113][114]. However, the reflectance of visible and SWIR spectral channels of forests and woodlands may increase [113], or decrease [112], and decrease for grasslands and croplands [112].…”

Section: Spectral Signatures Based Approachesmentioning

confidence: 99%

Section: Spectral Signatures Based Approachesmentioning

confidence: 99%

“…For example, the NIR reflectance decreases significantly due to the destruction of leaf cells within vegetation for all the types of vegetation [100,[112][113][114]. However, the reflectance of visible and SWIR spectral channels of forests and woodlands may increase [113], or decrease [112], and decrease for grasslands and croplands [112]. Mallinis et al found that mid-infrared band (or Landsat-5/7/8 band 7, SWIR 2) shows significant increase due to the reduction in canopy moisture and shadow [100].…”

Section: Spectral Signatures Based Approachesmentioning

confidence: 99%

“…This statistical multivariate technique consists of two major categories: binomial (or binary) and multinomial, which coincide with two post-fire burn measurements, that is burned area mapping and burn severity mapping [100,114]. Binomial logistic regression involves the situation of occurrences in a dichotomous manner, for instance, burned or unburned [112,113,121]. Multinomial logistic regression involves the circumstance in which the results can comprise three or more possible types, like unburned, light burn, moderate burn, and severe burn [126].…”

Section: Statistical Theory Based Approachesmentioning

confidence: 99%

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A Review of Swidden Agriculture in Southeast Asia

et al. 2014

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Swidden agriculture is by far the dominant land use system in the mountainous regions of Southeast Asia (SEA). It provides various valuable subsistence products to local farmers, mostly the poor ethnic minority groups. Controversially, it is also closely connected with a number of environmental issues. With the strengthening regional economic cooperation in SEA, swidden agriculture has experienced drastic transformations into other diverse market-oriented land use types since the 1990s. However, there is very limited information on the basic geographical and demographic data of swidden agriculture and the socio-economic and biophysical effects of the transformations. International programs, such as the Reducing Emissions from Deforestation and forest Degradation (REDD), underscore the importance of monitoring and evaluating swidden agriculture and its transition to reduce carbon emission due to deforestation and forest degradation. In this context, along with the accessibility of Landsat historical imagery, remote sensing based techniques will offer an effective way to detect and monitor the locations and extent of swidden agriculture. Many approaches for investigating fire occurrence and burned area can be introduced for swidden agriculture mapping due to the common feature of fire relatedness. In this review paper, four broad approaches involving spectral signatures, phenological characteristics, statistical theory and landscape ecology were summarized for swidden agriculture delineation. Five research priorities about swidden agriculture involving remote sensing techniques, spatial pattern, change, drivers and impacts were proposed accordingly. To our knowledge, a synthesis review on the remote sensing and outlook on swidden agriculture has not been reported yet. This review paper aims to give a comprehensive overview of swidden agriculture studies in the domains of debated definition, trends, remote sensing methods and OPEN ACCESSRemote Sens. 2014, 6 1655 outlook research in SEA undertaken in the past two decades.

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Section: Spectral Signatures Based Approachesmentioning

confidence: 99%

Section: Spectral Signatures Based Approachesmentioning

confidence: 99%

Section: Spectral Signatures Based Approachesmentioning

confidence: 99%

Section: Statistical Theory Based Approachesmentioning

confidence: 99%

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A Review of Swidden Agriculture in Southeast Asia

et al. 2014

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“…Remote sensing has been used to effectively perform this task in forested areas (Koutsias and Karteris 2000, Li et al 2004, Loboda et al 2007, Boschetti et al 2008) and savannah areas (Roy et al 1999, Hudak and Brockett 2004, Smith et al 2005, Trigg et al 2005, Dempewolf et al 2007), but rarely in grassland areas (Rahman and Gamon 2004). This is unfortunate, as even similar areas such as grassland and savannah exhibit different spectral responses during burn recovery (Stroppiana et al 2002, Pereira 2003, Silva et al 2004.…”

Section: Introductionmentioning

confidence: 95%

A comparison of red, NIR, and NDVI for monitoring temporal burn signature change in tallgrass prairie

Mohler

Goodin

2010

Remote Sensing Letters

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Prescribed burning in tallgrass prairie benefits both human and natural systems. However, negative aspects of burning, such as air pollution, also exist. Balancing the advantages and disadvantages of burning requires knowledge of the burn regime in the tallgrass system. One way to acquire this knowledge is by mapping burned areas with remotely sensed data. Unfortunately, burned area mapping is often complicated by the transient nature of burn scars, by cloud cover, and by a lack of spectral contrast between burned areas and non-burned senescent vegetation. In this study, we used in situ measurements of spectral reflectance to track the efficacy of bandpasses that simulated Moderate Resolution Imaging Spectroradiometer (MODIS) and Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETMþ) sensor responses. Our results showed that red and Normalized Difference Vegetation Index (NDVI) outperformed Near-Infrared (NIR) for delineating burned areas. From these findings it is clear that identifying/mapping burned areas can be performed accurately for several months with either red reflectance or NDVI.

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