Using satellite image-based maps and ground inventory data to estimate the area of the remaining Atlantic forest in the Brazilian state of Santa Catarina

Vibrans, Alexander Christian; McRoberts, Ronald E.; Moser, P.H.; Nicoletti, Adilson Luiz

doi:10.1016/j.rse.2012.10.023

Cited by 93 publications

(80 citation statements)

References 19 publications

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“…Bernardi et al 2007, Freitas et al 2011, Gazarini and Pedro 2013, Silva et al 2013, Loureiro and Gregorin 2015. Because deciduous seasonal and Araucaria Pine forest cover large extensions in the southern region of Brazil (Vibrans et al 2010, Vibrans et al 2013, it is possible that the distribution of M. neglectus is even larger in this region (Passos et al 2010).…”

Section: Discussionmentioning

confidence: 99%

First record of Molossops neglectus Williams & Genoways, 1980 (Chiroptera, Molossidae) from the state of Santa Catarina, southern Brazil

Althoff¹,

Carvalho²,

Luciano³

et al. 2018

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Herein we describe the first records of Molossops neglectus for the state of Santa Catarina, Brazil. The records are based on 3 specimens captured in the municipalities of Chapecó and São Domingos, in the western portion of the state. Considering that M. neglectus is an infrequent species in field surveys, new data on its geographical range is important to better understand the biology and ecology of M. neglectus.

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Section: Discussionmentioning

confidence: 99%

First record of Molossops neglectus Williams & Genoways, 1980 (Chiroptera, Molossidae) from the state of Santa Catarina, southern Brazil

Althoff¹,

Carvalho²,

Luciano³

et al. 2018

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“…through stratification of sampling with unequal probabilities. Secondly, auxiliary information can be used in the estimation phase, for example through post-stratification McRoberts et al 2006) or model-assisted estimation (Särndal et al 1992;Vibrans et al 2013). In the estimation phase, local level estimates can also be provided through synthetic estimation.…”

Section: Integrating Remote Sensing Auxiliary Informationmentioning

confidence: 99%

Adapting National Forest Inventories to changing requirements – the case of the Swedish National Forest Inventory at the turn of the 20th century

Fridman¹,

Holm²,

Nilsson³

et al. 2014

Silva Fenn.

198

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• National Forest Inventories supply invaluable long term time series of forest state. Recent developments and international harmonization of modern NFIs widen the scope to even include ecosystem goods, e.g. biodiversity and carbon sequestration. The combination of NFI field data with remote sensing techniques can give good estimates for areas smaller than national and regional level. AbstractNational Forest Inventories (NFIs) are becoming increasingly important worldwide in order to provide information about the multiple functions of forests, e.g. their provision of raw materials to industry, biodiversity and their capacity to store carbon for mitigating climate change. In several countries the history of NFIs is very long. For these countries a specific challenge is to keep the inventories up-to-date without sacrificing the advantages associated with long time series. At the turn of the 20th century European NFIs faced some major challenges. In this article we describe the history and the recent developments of the Swedish NFI as an example from which general observations are made and discussed. The Swedish NFI started in 1923 and has evolved from an inventory with a narrow focus on wood resources to an inventory today which aims to provide information about all major forest ecosystem services. It can be concluded that the traditional approaches of most European NFIs, e.g. to collect data through sample plot field inventories, has proved to be applicable even for a wide range of new information requirements. Specifically, detailed data about land use, trees, vegetation, and soils has found new important uses in connection with biodiversity assessments and the estimation of greenhouse gas emissions. Though time-consuming and difficult, making NFI information comparable across countries through harmonization appears to be a useful approach. The European National Forest Inventory Network (ENFIN) was formed in 2003 and has been successful in pan-European NFI harmonization.

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“…This estimator used the information from the sample only [26] and did not use the auxiliary data, which included simulated images and classification from HJ-1 in the case study using actual remote sensing data.…”

Section: Simple Random Sampling Estimatormentioning

confidence: 99%

“…Stehman [5] established model-assisted estimation as a unifying framework and pointed out a strong connection between accuracy assessment and area estimation for land cover or land cover change based on satellite imageries and compared and summarized a variety of confusion matrix calibration estimators [20]. McRoberts compared probability-based and model-based estimators based on logistic regression and remote sensing and inventory data to infer the proportion of forest [8], gave a systematic analysis about scientific inference using satellite imageries as auxiliary data [21], and implemented these estimators in many applications [22][23][24][25][26][27]. Foody [28] studied the impact of inaccurate ground reference data on the precision of land cover change area estimation in a simulation experiment and proved that the ground reference data imperfection can lead to a misestimation of land cover change area, the magnitude of which can be large when the area of land cover change is rare [29].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Model-Assisted Estimators to Infer Land Cover/Use Class Area Using Satellite Imagery

Zhu

Pan

et al. 2014

Remote Sensing

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Abstract:Remote sensing provides timely, economic, and objective data over a large area and has become the main data source for land cover/use area estimation. However, the classification results cannot be directly used to calculate the area of a given land cover/use type because of classification errors. The main purpose of this study is to explore the performance and stability of several model-assisted estimators in various overall accuracies of classification and sampling fractions. In this study, the confusion matrix calibration direct estimator, confusion matrix calibration inverse estimator, ratio estimator, and simple regression estimator were implemented to infer the areas of several land cover classes using simple random sampling without replacement in two experiments: a case study using simulation data based on RapidEye images and that using actual RapidEye and Huan Jing (HJ)-1A images. In addition, the simple estimator using a simple random sample without replacement was regarded as a basic estimator. The comparison results suggested that the confusion matrix calibration estimators, ratio estimator, and simple regression estimator could provide more accurate and stable estimates than the simple random sampling estimator. In addition, high-quality classification data played a positive role in the estimation, and the confusion matrix inverse estimators were more sensitive to the classification accuracy. In the simulated experiment, the average deviation of a confusion matrix calibration inverse estimator decreased by about 0.195 with the increasing overall accuracy of classification; otherwise, the variation of the other three model-assisted estimators was 0.102. Moreover, the simple regression estimator was slightly superior to the confusion matrix calibration

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Using satellite image-based maps and ground inventory data to estimate the area of the remaining Atlantic forest in the Brazilian state of Santa Catarina

Cited by 93 publications

References 19 publications

First record of Molossops neglectus Williams & Genoways, 1980 (Chiroptera, Molossidae) from the state of Santa Catarina, southern Brazil

First record of Molossops neglectus Williams & Genoways, 1980 (Chiroptera, Molossidae) from the state of Santa Catarina, southern Brazil

Adapting National Forest Inventories to changing requirements – the case of the Swedish National Forest Inventory at the turn of the 20th century

A Comparison of Model-Assisted Estimators to Infer Land Cover/Use Class Area Using Satellite Imagery

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Using satellite image-based maps and ground inventory data to estimate the area of the remaining Atlantic forest in the Brazilian state of Santa Catarina

Cited by 93 publications

References 19 publications

First record of Molossops neglectus Williams &amp; Genoways, 1980&nbsp;(Chiroptera, Molossidae) from the state of Santa Catarina,&nbsp;southern Brazil

First record of Molossops neglectus Williams &amp; Genoways, 1980&nbsp;(Chiroptera, Molossidae) from the state of Santa Catarina,&nbsp;southern Brazil

Adapting National Forest Inventories to changing requirements – the case of the Swedish National Forest Inventory at the turn of the 20th century

A Comparison of Model-Assisted Estimators to Infer Land Cover/Use Class Area Using Satellite Imagery

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First record of Molossops neglectus Williams & Genoways, 1980 (Chiroptera, Molossidae) from the state of Santa Catarina, southern Brazil

First record of Molossops neglectus Williams & Genoways, 1980 (Chiroptera, Molossidae) from the state of Santa Catarina, southern Brazil