Choose satellites to monitor deforestation

Lynch, J. M.; Maslin, Mark; Balzter, Heiko; Sweeting, Martin

doi:10.1038/496293a

Cited by 71 publications

(38 citation statements)

References 6 publications

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“…Satellite data are considered as the only realistic means to monitor deforestation and forest degradation at a timely manner (Lynch et al 2013). Data time series are necessary to detect deforestation.…”

Section: Degradation / Deforestationmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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Section: Degradation / Deforestationmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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“…Time series satellite imagery is the only data stream capable of providing repetitive observations of global tropical forest areas [2,9]. Therefore, it is considered the primary data source to detect forest change in NRT [2].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is considered the primary data source to detect forest change in NRT [2]. Here, NRT deforestation detection refers to the capacity to detect change in a satellite image once it is available.…”

Section: Introductionmentioning

confidence: 99%

“…Forest plantations currently account for 6%-7% of the global forest area [6] and help to decrease the pressure on natural forest [5]. To support governments, communities and forestry stakeholders in improving forest management and enacting timely actions against illegal deforestation, near real-time (NRT) deforestation monitoring can provide a powerful tool [1,2,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Timely information on deforestation is crucial to effectively manage and protect forest resources in the tropics [1,2]. Poorly managed and illegal activities in natural and plantation forest cause a wide range of negative environmental effects, significant financial losses and depression of the world timber price [3].…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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Abstract:To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat-and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.

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The Sustainability of Biofuels

Lynch

2017

Biofuels and Bioenergy

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Choose satellites to monitor deforestation

Cited by 71 publications

References 6 publications

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

The Sustainability of Biofuels

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