Development of a Point-based Method for Map Validation and Confidence Interval Estimation: A Case Study of Burned Areas in Amazonia

Anderson, Liana O.; Cheek, David; Aragão, Luiz E. O. C.; Andere, Luaê; Duarte, Brenda; Salazar, Natàlia; Lima, André; Duarte, Valdete; Arai, Egídio

doi:10.4172/2469-4134.1000193

Cited by 12 publications

(11 citation statements)

References 42 publications

(68 reference statements)

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“…Maps containing burned area information from 2008 to 2012 were provided by the Tropical Ecosystems and Environmental Science Laboratory (TREES) at the Brazilian National Institute for Space Research (INPE). This dataset was generated following a well-established methodology [15,[39][40][41]. These maps are derived from MODIS surface reflectance products collection 5, which were resampled to 250 m of spatial resolution and subsequently processed by applying a Spectral Mixture Analysis (SMA) with vegetation, soil, and shade endmembers [42].…”

Section: Spatial Datasetsmentioning

confidence: 99%

Translating Fire Impacts in Southwestern Amazonia into Economic Costs

et al. 2019

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Between 1998 and 2017, climate-related disasters represented 91% of all occurrences worldwide, causing approximately US$ 2.245 billion of direct economic losses. In the Amazon region, fire is used as a widely spread technique for land clearing, agricultural management, hunting, and religious rituals. However, over the past 20 years, severe droughts caused a major amplification of fire occurrences, leading to several socioeconomic and environmental impacts. Particularly in Acre state, located in the southwestern Brazilian Amazon, the occurrence of extensive fires, associated with extreme climatic events, has been reported since 2005. However, fire dynamics, land tenure relationships, and associated impacts are poorly quantified. In this study, we aim to investigate the following: (1) The spatiotemporal variability of fire dynamics during anomalously dry and regular climate conditions; (2) the attribution of fire occurrence and land tenure relationship, and (3) the environmental, social, and economic impacts caused by fires and its consequences for Acre’s economy. We analyzed information on the spatial patterns of fire, its direct impacts on land use and land cover, carbon stocks, CO2 emissions, the indirect impact on human illness, and finally the costs of these impacts from 2008 to 2012. During the studied period, burned areas were concentrated around the major cities and roads, forming polygons up to 0.6 km2. However, in 2010, an extremely dry year, fires spread to remote areas, impacting protected private areas and sustainable-use conservation areas. In 2010, the total area affected by forest fires was approximately 16 times greater than in meteorologically normal years. The total economic loss estimated in 2010 was around US$ 243.36 ± 85.05 million and for the entire period, US$ 307.46 ± 85.41 million. These values represent 7.03 ± 2.45% and 9.07 ± 2.46% of Acre’s gross domestic product (GDP), respectively.

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Section: Spatial Datasetsmentioning

confidence: 99%

Translating Fire Impacts in Southwestern Amazonia into Economic Costs

et al. 2019

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“…Generally, larger the sample size, better the precision. Few important considerations in determining sample size are accuracy, cost and workload (Anderson et al 2017). In the case of a stratified random sampling of LULC classes, sample number could be calculated using the equation given by Cochran (1977).…”

Section: Post-claslite Unsupervised Classificationmentioning

confidence: 99%

“…However, a parameter estimate used in accuracy assessment (e.g. user's accuracy) without a CI might be misleading, as it provides an impression of certainty which might not be the case (Anderson et al 2017). Hence, it was recommended that estimates of parameters used in accuracy assessment of maps should be accompanied with a CI (Foody 2004;Olofsson et al 2013;Strahler et al 2006), as it gives a range of values of the estimated parameter along with margin of error (confidence interval) in estimation.…”

Section: Accuracy Assessment Of Lulc Change Mapsmentioning

confidence: 99%

“…The point estimate of a parameter, e.g. the estimates of carbon density or emissions in this case, without a confidence interval might be misleading, as it provides an impression that it is certain, but in reality, it might not be the case (Anderson et al 2017). For that, Chapter 3, Volume 1 of the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006a) recommended reporting the level of uncertainty in the emissions (and removal) estimates in the form of 95% confidence interval, estimated from the respective distribution about the mean.…”

Section: Estimation Of Biomass-carbon Dynamics Emission Factors and mentioning

confidence: 99%

“…However, as LULC is a continuous variable of spatial extent, using pixels or polygons as samples which have spatial extent too, might create problems of differentiation as to which class it falls under when they occupy both cover classes. Although this problem can be solved using fuzzy set theory (Gopal & Woodcock 1994;Strahler et al 2006), it complicates the design and analysis of response (Anderson et al 2017). When points are used as sampling units, no such problems arise provided that classboundaries are well-demarcated.…”

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confidence: 99%

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Mapping forest dynamics in Bangladesh from satellite images: implications for the global REDD+ program

Redowan¹

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Reducing emissions from deforestation and forest degradation (REDD+) is a global climate change mitigation initiative under negotiation by the United Nations Framework Convention on Climate Change (UNFCCC). This initiative provides financial incentives to developing countries for enhancing carbon stocks in their forests by abstaining from deforestation and forest degradation, which would lead to emission of CO2 into the atmosphere. To implement a REDD+ project, developing countries need to measure and monitor anthropogenic changes in their forests and as well as forest carbon to account for CO2 emissions and removals from such changes. It is also important to predict future scenarios of forest/landuse changes and to identify the areas at risk of changes so that appropriate conservation initiatives can be taken. Bangladesh is steadily progressing through its REDD+ roadmap. However, several key research issues still to address include: using remote sensing technology to detect deforestation, forest degradation and associated changes in forest carbon stock, and predict future forest/land-use pattern at local environmental setting. This study developed and evaluated approaches to detect the extent of historic (1995-2015) deforestation and forest degradation (Objective 1), to estimate the emissions of forest aboveground carbon due to deforestation and degradation activities (Objective 2), and to assess the future scenarios, driving forces and risk of deforestation and forest degradation (Objective 3) at Raghunandan Hill Reserve in northeastern Bangladesh using a combination of satellite and field-level data employing various geospatial, statistical and modeling tools. The thesis comprises seven chapters. The significance and background of the research along with aim, objectives, research questions and available literature on the topic were described in chapter 1, 2 and 3. Chapter 4 dealt with the first objective, where changes of forest areas to non-forest areas and one forest strata to another during 1995-2015 were detected with high accuracy (>90%) by applying Monte-Carlo spectral unmixing algorithm to Landsat images, followed by knowledge-based classification approach. The classification was verified using independent randomly drawn reference sample points using high-resolution Google Earth images. A post-classification comparison method was applied to quantify the spatial extent, location and rate of changes of forest classes by generating transition matrices. In chapter 5, emissions of carbon (t CO2e yr-1) from deforestation and forest degradation activities during 1995-2005 and 2005-2015 periods at Raghunandan forest were estimated using Landsat satellite and field-level biomass-carbon data applying regression analysis and geospatial techniques with acceptable accuracy. The accuracy of the estimate was verified using root mean square error iii (RMSE) of estimated carbon with field reference carbon. Results indicated that, during 1995-2005, the total estimated emission was 4589 tonnes (t) CO2e yr-1 from deforesta...

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Identifying local‐scale meteorological conditions favorable to large fires in Brazil

Rifai

Anderson

et al. 2021

Climate Resilience

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This study aims to investigate local-scale meteorological conditions associated with large fires in Brazil during recent decades. We assess whether there are large fire types with preceding predictors. Our results show that large fires, defined with a threshold of a daily burned area >95th percentile of the historical record, mainly occur in August and September in Brazil, and Amazônia and Cerrado experience much higher numbers of large fires than the other biomes. There are two large fire types that have robust meteorological signatures: (1) a wind driven type, characterized by peak wind speed on the day of the fire, and anomalously high wind speed a few (∼3) days before and after the fire; and (2) a Hot-Drought driven type, characterized by anomalously high temperature, low relative humidity, and consistent drought conditions indicated by anomalously high fuel aridity starting as far back as 5 months prior to the fires. A third one is characterized by no anomalous meteorological conditions. The wind driven type most frequently occurs in southern and southeastern Amazônia, Pantanal, and western and northern-to-central Cerrado, with some occurrences over the western Caatinga region bordering Cerrado, southern Cerrado, and southern Mata Atlântica; whereas the Hot-Drought driven type most frequently occurs in southern and southeastern Amazônia, Pantanal and western and northern-to-central Cerrado, with some occurrences over the western Caatinga region bordering Cerrado, southern Cerrado, central-tosouthern Mata Atlântica, and a few occurrences over Northern Brazil where the Amazônia meets Roraima. Southern and southeastern Amazônia, Pantanal and western and northern-to-central Cerrado are the major large fire prone regions. Our results highlight that understanding the temporal and spatial variability of the meteorological conditions associated with large fires is essential for developing spatially explicit forecasting, and future projections of large fire hazards under climate change in Brazil, in particular the Hot-Drought driven type.

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Development of a Point-based Method for Map Validation and Confidence Interval Estimation: A Case Study of Burned Areas in Amazonia

Cited by 12 publications

References 42 publications

Translating Fire Impacts in Southwestern Amazonia into Economic Costs

Translating Fire Impacts in Southwestern Amazonia into Economic Costs

Mapping forest dynamics in Bangladesh from satellite images: implications for the global REDD+ program

Identifying local‐scale meteorological conditions favorable to large fires in Brazil

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