Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM+ data

Potapov, Peter; Turubanova, Svetlana; Hansen, Matthew C.; Adusei, Bernard; Broich, Mark; Altstatt, Alice; Mané, Landing; Justice, Christopher O.

doi:10.1016/j.rse.2011.08.027

Cited by 342 publications

(326 citation statements)

References 30 publications

Supporting

Mentioning

268

Contrasting

Unclassified

Order By: Relevance

“…On the other hand, the priority of a number of researchers has been forest cover change in the Congo Basin. For example, Potapov et al (2012) [8] quantified forest cover loss in the DRC region of the study area from 2001 to 2010 using Landsat imagery. However, that important work is solely focused on forest change.…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing has been used successfully in mapping a range of land covers at a variety of spatial and temporal scales [10]. Satellite-based monitoring has one additional advantage; independence from official national agencies allowing open publication of monitoring results [8]. Landsat data constitutes the longest record of global-scale medium spatial resolution earth observation data available freely in the web since 2008 [4,11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Basnet

Vodacek

2015

Remote Sensing

View full text Add to dashboard Cite

Tracking land surface dynamics over cloud prone areas with complex mountainous terrain is an important challenge facing the Earth Science community. One such region is the Lake Kivu region in Central Africa. We developed a processing chain to systematically monitor the spatio-temporal land use/land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data. Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive. A systematic supervised classification was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher. Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities. The gross forest cover loss for 1988-2001 and 2001-2011 period was 216.4 and 130.5 thousand hectares, respectively, signifying significant deforestation in the period of civil war and a relatively stable and lower deforestation rate later, possibly due to conservation and reforestation efforts in the region. The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands. Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa. These multi-temporal datasets will be a valuable baseline for land use managers in the region OPEN ACCESSRemote Sens. 2015, 7 6684 interested in developing ecologically sustainable land management strategies and measuring the impacts of biodiversity conservation efforts.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Basnet

Vodacek

2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The SWBD captured the actual water state of 10 days in February 2000 from the orthorectified SRTM radar image [11]. Permanent water pixels of the GFC-datamask were selected as having a fraction of water-flagged observations for all non-cloudy observations greater or equal to 50% of the Landsat image time series selected during the growing season [29]. A thresholding algorithm was applied to multi-temporal SAR metrics to map water bodies of the SAR-WBI [12].…”

Section: Potential Products To Build the CCI Global Map Of Open Watermentioning

confidence: 99%

“…The GFC-datamask water omissions were found typically along lake banks, shallow water and dams. The GFC-datamask minimum water extent is probably due to its definition of water using a strict threshold greater or equal to 50% of water detections in the Landsat image in the time series [29]. Commission errors are marginal and occur along some lakes and over black lava rocks (e.g., Ethiopia).…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community

et al. 2017

View full text Add to dashboard Cite

Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90 • N/90 • S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000-2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between 98% and 100%. The CCI global map of open water bodies provided the best water class representation (F-score of 89%) compared to its constitutive inputs. When focusing on the challenging areas for water bodies' mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between 74% and 89%. The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km 2 ± 0.24 million km 2 . The dataset is freely available through the ESA CCI Land Cover viewer.

show abstract

“…With global coverage, the regularly acquired largest historical archive and freely available space-based earth observations, Landsat imagery is preferred for monitoring tropical forests (Vieira et al 2003;Salovaara et al 2005;Kumar et al 2010;Potapov et al 2012;Das and Singh 2013;Zhuravleva et al 2013). Several studies have reported deforestation and forest area changes in Indonesia using Landsat satellite data (Curran et al 2004;Hansen et al 2009, Miettinen andLiew 2010;Margono et al 2012).…”

Section: Other Actors' Role In Forest Managementmentioning

confidence: 99%

Opportunities for implementing REDD+ to enhance sustainable forest management and improve livelihoods in Lombok, NTB, Indonesia

Bae¹,

Kim²,

Kim³

et al. 2014

View full text Add to dashboard Cite

Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM+ data

Cited by 342 publications

References 30 publications

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community

Opportunities for implementing REDD+ to enhance sustainable forest management and improve livelihoods in Lombok, NTB, Indonesia

Contact Info

Product

Resources

About