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Received for review January 30 th, accepted September 25 th, final version October, 19 th There are global maps that estimate the area affected by a fire using the reflectance variation of the surface. In this study, we evaluated the reliability and the causes of error of the MCD45 Burned Area Product, by applying the confusion matrix method to the Orinoco River Basin. This basin is located in the northern zone of South America, and consists mainly of savanna ecosystems.
For the evaluation, we used as reference data five pairs of Landsat images, coveringkm 2. The Burned Area Product estimated a burned area of 7, The causes of error are associated to the spatial resolution of the map, what is the cause of diagonal relationship class 11 to some structures of the algorithm that generates the map. Esta cuenca se ubica en la zona norte de Sur América, y predominan allí los ecosistemas de sabana. Detecting areas burnt by forest fires is of great importance, given that fires are among the factors affecting the dynamics of ecosystems and their carbon and nutrient cycles [1].
They generate why is behaviorism important in education significant impact on soil structure and loss of biodiversity, as well as greenhouse gas emissions []. Savanna ecosystems exhibit a high occurrence of fires throughout the world [4,5] and they are important on net carbon fluxes because of the large areas they occupy [6].
When the biomass present in a savanna is burnt, it becomes a major source of CO 2 emissions in the atmosphere [7]. The Orinoco River basin in Colombia and Venezuela is the most representative tropical savanna in northern South America [8] and it is of particular interest due to the occurrence of fires during drought periods [4]. In Pair of linear equations in two variables class 10 formulas pdf and Central America, it has been reported that CO 2 emissions from biomass burning are between 8 and 9 times higher than emissions generated from fossil fuel consumption [9].
Deforestation rates have been used as parameters to estimate greenhouse gas emmissions in this region []; however, [13] found that despite the reduction of deforestation rates in primary forests of the Brazilian Amazon, the expansion of grassland areas probably increases emissions via the burning activity needed to maintain these areas. Thereby, the importance of an estimation of the burned area in the region is evident. Sinceglobal burned area products have been developed by using satellite data, each with a different spatial and temporal resolution.
These products detect the area directly affected by burning through algorithms from the carbon signal generated after the fire [3]. Burned area products have become an efficient alternative regarding time issues, costs, and spatial coverage in comparison to field methods used to monitor areas affected by fires [3,18]. It is necessary to quantify the accuracy of these products to determine the magnitude of the error, its causes, and implications. Global validation of the products should be developed with a regional approach, given that their global scale and fire characteristics are highly variable for different places.
Most of the errors can be introduced because of: characteristics of the algorithm design, physical characteristics of the terrain, weather conditions in the area, vegetation type, and the intensity of the fire [19,20]. These aspects make it difficult to design an algorithm applicable to all areas and types of ecosystems throughout the world [21]. Systematic methods are being developed to assess the accuracy of global burned area maps on the regional level.
So far, the best protocol for the selection of the reference data has been prepared by [22]. Evaluating the why do we preserve food of these satellite data products has become a high-priority research topic [21,23,24]. Few studies have been conducted to estimate the burned area in the northern part of South America. Among the most important are those developed by [4,20,25].
However, results vary widely from one study to another; representing a major difficulty for the overall estimation of the burned area. Validating existing global burned area products helps to improve estimations of the burned area in the region and allows for the acquisition of information in a broader timeframe and a wider area. The main objective of this study was to validate the spatial and temporal accuracy of the MCD45 global burned area product, emphasizing the causes of error.
This product has the best spatial resolution of products currently available, and it is the only one that keeps generating data. Three factors considered to be causes of error were analyzed: the presence of clouds, examples of casual language effect of pixel size, and the type of vegetation being burned. The validation of the MCD45 burned area product was held in the ecoregion known as Los Llanos with an area ofkm 2 in the Orinoco River basin.
This basin consists of more what is the cause of diagonal relationship class 11 1, km 2 of land, characterized by heterogeneous savanna ecosystems, which vary according to factors such as soil, flooding, and vegetation types [4]. Rainfall in the area ranges between and mm, as an annual average [26]. The basin presents monomodal behavior, with a marked rainy season that runs from April to November and a dry season between December and March [26,27].
The main economic activities in the area are agriculture palm oil and ricewith extensive livestock activity, mining, oil exploration and extraction, silviculture, and ecotourism [4,26]; extensive livestock being the most important [4] economic activity. These anthropogenic activities are the major cause of forest fires in the area [4,28]. Reference data Reference information gathered in the field and free of errors is required to validate the MCD45 burned area product; however, it is difficult and expensive word meaning easily read collect such information in the field, mainly because of the restricted access to the affected area, the ephemeral nature of the signal, and the great extension of the fires.
However, satellite images of higher spatial resolution than the product being validated is an alternative method to data collection in the field [22]. In this case, a product of m pixel size was validated with 30 m satellite images [20,25,29]. Each Landsat image covers approximately 34, km 2and has a 30 m spatial resolution [31].
Five pairs of images were used in the study, each corresponding to a different location, according to the unique Landsat reference system: Worldwide Reference System WRS. Each pair of images has the same geographical area, and about a month of difference in time between the images that compose the pair. This method allows for one to establish the time period where a fire has occurred. It also helps to avoid errors in the reference data due to confusion with dark soils, water bodies, and cloud shadows, which have similar reflectivity characteristics to the burned areas [22].
Images were selected for the dry season and with the lowest possible cloud content. The total area assessed was approximatelykm 2which corresponds to After reviewing the available pairs of images, only those images which met the previously-described criteria were selected, ensuring reliable reference information for validation. Table 1 lists the selected Landsat images with their corresponding date used to obtain reference information.
Global burned area product The MCD45 burned area product is generated from multi-temporal observations of the earth's surface reflectivity for a determined period of time. An algorithm is generated based on the bi-directional reflectance model-based change detection developed by [3] and improved by [16] to continuously map fire-affected areas. The algorithm uses the reflectance sensed within a period of time of a fixed number of days to predict the reflectance on a subsequent day. It is determined whether the difference between the predicted and observed reflectance is relevant [3].
After that, some tests are applied to each pixel to determine which would be a candidate for burning [3,16]. When a pixel becomes a candidate for burning but does not pass all the what is the cause of diagonal relationship class 11 because of insufficient observations, an iterative search method is used prior to discarding it. This method is based on the fact that there is a high probability finding burned pixels neighboring confidently-detected burns [3,16].
In subsequent sections, we will refer to this what is correlation in research as context algorithm. The MCD45 product provides information about the date of the detection of the burnt area. This data indicates the presence of a burn signal and the approximate date of burning.
The product also provides information about the quality of the detection made, the presence of snow, water bodies, and areas where there were insufficient data to do detection work [1]. The MCD45 product has a global coverage what is the cause of diagonal relationship class 11 m spatial resolution. It has been available from the year onwards, and is generated in one-month time periods, including an 8-day presicion before and after each day [1].
An RGB false color with bands 3 0. Figure 1 shows the delimitation process of the reference burned area found in the Landsat images. Only those burned areas occurring after the first acquisition date are polygon digitized. From these polygons, a raster dichotomic map was generated with only two pixel values: burned and unburned with a 30 m spatial resolution, according to the reference-image pixel size. The product was processed to generate a raster dichotomic map with a m spatial resolution, but including only those pixels identified between the Landsat acquisition dates Fig.
The new map was re-sampled reducing pixel size to 30 m; therby, the results obtained could be compared to those generated by the high-resolution Landsat images. Validation The confusion matrix was used to evaluate the spatial accuracy of the MCD45 product. This method consists of building a square matrix, where columns contain the reference data and rows contain the information of the product being validated. Thus, the major diagonal of the matrix only has those pixels correctly classified [32].
The matrix allows for one to identify two types of errors: omission errors, which correspond to areas that were really burned and were not classified as such in the MCD45 product; and commission errors, related to areas classified as "burnt" in the product what is the scientific definition of cause that were not really burnt.
The matrix also allows for one to calculate two indices: the overall accuracy, which offers an estimate of the total percentage of product success; and the Kappa coefficient, which indicates whether the agreement between the classification made by the MCD45 product and the reference data is significant. To determine this relationship, [32], based on the definition of [33], proposed classifying the Kappa coefficient value in the following ranges: less than 0.
Identification of what is equivalence relation in discrete math of error According to previous studies, the sources of error can be associated with spatial and temporal resolution, the presence of clouds weather conditions or smoke plumes, confusion with dark surfaces, vegetation type in the study area physical characteristics of the terrain and weaknesses in the algorithm [,29].
Because the study area was a tropical zone, the analysis of the presence of clouds was emphasized by using what is the cause of diagonal relationship class 11 cloud mask for the period evaluated in each pair of reference images. We also analyzed the effect of the m pixel in what is dollar rate today in bank errors of omission and what does a linear line look like spatial resolution effect and the vegetation type in the study area.
Spatial resolution Errors caused by discritizing a surface into pixels resulted in two types of error: omission, when the burned areas are smaller than the pixel size; and commission, when mixed pixels include both burned pixels and unburned pixels. Omission errors: The incidence of spatial resolution in the omission errors was assessed by identifying the burned reference polygons with an area smaller than the pixel size of the MCD45 product; i.
It is expected that the product does not identify an area smaller than the size of its pixel as a burned area. This error was quantified in terms of the proportion of total area omitted. Commission errors: Regarding the errors of commission, it was found that the MCD45 product includes unburned areas outside polygons identified as burnt, mainly in their border regions. This is due to the pixel size of the product and to the context algorithms when usedincreasing the probability of commission along the borders of burned areas.
Because of this, in order to evaluate the impact of these factors on commission errors, a m corridor according to the spatial resolution of the MCD45 product was built around each of the polygons of the reference map, to identify what percentage of the total commission error is located within this corridor. Cloud cover The influence of cloud presence over the study area was evaluated as omission.
For this analysis, a cloud mask was used to cover the what is the cause of diagonal relationship class 11 of the exact dates of the pairs of images used as reference. We determined the portion of the total reference area and the total omission error that presented cloudiness during the evaluated time period. Aditionally, the influence of cloud shadow on the commission error was visually evaluated.
Vegetation type According to the MCD12Q1 global vegetation map, most representative cover in the study area is grassland, savanna woodlands, savannas, a mosaic of natural vegetation and crops, and evergreen broadleaf forest. We determined here the proportion of the omission and commission error area over each vegetation type, by overlaping the information about the errors obtained previously with the MCD12Q1 global vegetation map in the study area. Validation The 5 pairs of Landsat images used cover an area ofkm 2.
As mentioned above, the zones of the images where a precise identification of the burned area of reference could not be guaranteed were not considered for validation. That is why the total assessed area waskm 2. Figure 2 presents the area detected as burned by the MCD45 product during the January period. Figure 2 shows the geographical extent of the validation area. The validation for each Landsat pair is presented in Tables 2 and 3respectively. A total of 18, reference polygons were delimited, in comparison with the 4, polygons estimated by the MCD45 product.
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