Mapping forest and woodland loss in Swaziland: 1990–2015

Abstract Forests and woodlands are a very important part of the global ecosystem through their provision of ecosystem goods and services. However, conversion to other land uses is one of the biggest threats to their existence. Remote sensing presents opportunities for monitoring such changes over wide and inaccessible areas including those areas that have no field data. In this study, we use the Carnegie Landsat Analysis System-lite (CLASlite) software and Landsat imagery to make the first spatially explicit national estimate deforestation in Swaziland. This was compared with deforestation data derived from the Global Forest Change (GFC) dataset for the period 2000–2014. The CLASlite analysis identified an estimated 46,620ha of forest and woodland lost between 1990 and 2015 resulting in a mean deforestation rate of 1704 ha yr−1. The GFC dataset, on the other hand, indicates a mean deforestation rate 1563 ha yr−1 when excluding forest regrowth. Validation of the results based on multi-year Google Earth and Landsat imagery indicated that both approaches are feasible for monitoring deforestation. The GFC data captured more forest loss within the dense plantation and wattle forests whilst underestimating deforestation within natural forests and woodlands. Although there are inter-annual variations, the rate of deforestation is generally increasing and widespread in many parts of the country mainly concentrated in the eastern half of the country and a few western parts where agriculture (particularly sugarcane), human settlements and other infrastructure developments are dominant land uses. Acacia and broadleaf savanna are being depleted at higher rates with up to 8.1% of forest area lost since the year 2000. Forest policies and legislation need to be reviewed to respond to the observed trends and patterns with a focus on forest conservation, climate change mitigation and adaptation.

[1]  A. M. Manyatsi,et al.  Contribution of Sale of Firewood Towards Rural Livelihood in Swaziland, and its Environmental Sustainability , 2010 .

[2]  Martha C. Anderson,et al.  Landsat-8: Science and Product Vision for Terrestrial Global Change Research , 2014 .

[3]  Z. Buřivalová,et al.  Relevance of Global Forest Change Data Set to Local Conservation: Case Study of Forest Degradation in Masoala National Park, Madagascar , 2015 .

[4]  Joanne C. White,et al.  Forest Monitoring Using Landsat Time Series Data: A Review , 2014 .

[5]  F. M. Danson,et al.  Satellite remote sensing of forest resources: three decades of research development , 2005 .

[6]  S. Goetz,et al.  Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+ , 2015 .

[7]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[8]  Naeem,et al.  Ecosystems and Human Well-Being: Biodiversity Synthesis , 2005 .

[9]  C. B. Lantican,et al.  Assessing change in national forest monitoring capacities of 99 tropical countries , 2015 .

[10]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[11]  S. Goetz,et al.  Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change , 2011 .

[12]  David A. Coomes,et al.  A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring , 2015, Remote. Sens..

[13]  R. Powers,et al.  Spatiotemporal patterns of tropical deforestation and forest degradation in response to the operation of the Tucuruí hydroelectric dam in the Amazon basin , 2015 .

[14]  Zhiqiang Yang,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms , 2010 .

[15]  M. Rowshon,et al.  The CDAA Framework for development of sustainable large-scale smallholder irrigation schemes in Swaziland , 2014 .

[16]  Randy Showstack,et al.  Sentinel Satellites Initiate New Era in Earth Observation , 2014 .

[17]  Ulysses Paulino Albuquerque,et al.  Burning biodiversity: Fuelwood harvesting causes forest degradation in human-dominated tropical landscapes , 2015 .

[18]  Alexandre Bouvet,et al.  Estimating tropical deforestation from Earth observation data , 2010 .

[19]  C. Woodcock,et al.  Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation , 2013 .

[20]  Issues and challenges for the national system for greenhouse gas inventory in the context of REDD+ , 2012 .

[21]  M. Keller,et al.  CANOPY DAMAGE AND RECOVERY AFTER SELECTIVE LOGGING IN AMAZONIA: FIELD AND SATELLITE STUDIES , 2004 .

[22]  D. Pimentel,et al.  Fuelwood production and use in rural Swaziland: A case-study of two communities , 1988 .

[23]  Bret A. Collier,et al.  Dynamic Edge Effects in Small Mammal Communities across a Conservation-Agricultural Interface in Swaziland , 2013, PloS one.

[24]  M. Herold,et al.  Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia , 2015 .

[25]  Martha C. Anderson,et al.  Free Access to Landsat Imagery , 2008, Science.

[26]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[27]  G. Asner,et al.  Advancing reference emission levels in subnational and national REDD+ initiatives: a CLASlite approach , 2015, Carbon Balance and Management.

[28]  Lindsay C. Stringer,et al.  Testing the orthodoxies of land degradation policy in Swaziland , 2009 .

[29]  Michael W. Binford,et al.  Land-cover change within and around protected areas in a biodiversity hotspot , 2016 .

[30]  Michael Förster,et al.  Remote sensing for mapping natural habitats and their conservation status - New opportunities and challenges , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[31]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[32]  G. Asner,et al.  Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations , 2002 .

[33]  R. B. Jackson,et al.  Global biodiversity scenarios for the year 2100. , 2000, Science.

[34]  R. Porro,et al.  Forest use and agriculture in Ucayali, Peru: Livelihood strategies, poverty and wealth in an Amazon frontier , 2015 .

[35]  Petr Keil,et al.  Comment on “High-resolution global maps of 21st-century forest cover change” , 2014, Science.

[36]  David E. Knapp,et al.  Automated mapping of tropical deforestation and forest degradation: CLASlite , 2009 .