Forest cover change in Miombo Woodlands: modeling land cover of African dry tropical forests with linear spectral mixture analysis

Abstract Dry tropical forests are experiencing some of the highest rates of change among the globe's forests. In sub-Saharan Africa, gross (loss, gain) and net changes in dry tropical forest areas are difficult to quantify at sub-national scales because of high spatio-temporal variability in land cover conditions due to vegetation phenology and land use practices. In this project, we developed new, field-validated remote sensing characterizations of dry season surface components to separate forest from non-forest land cover, and assessed forest changes from the 1990s–2010s in a Tanzanian Miombo Woodland landscape. Using a linear spectral mixture analysis (LSMA) approach with Landsat 5–8 data, we examined the hypothesis that higher proportions of substrate and non-photosynthetic vegetation (NPV) at non-forest regions distinguished them from forest cover against seasonally variable land cover conditions. Subsequently we evaluated the efficacy of multi-temporal classification and single-date image thresholding for identifying forest from non-forest cover. We found significantly greater proportions of substrate and NPV over non-forest compared to forest areas that enabled identification of forest cover across dry season images. Single-date, forest/non-forest maps based on an LSMA-derived metric attained overall accuracies of 81.0–85.3%, which approached multi-temporal unsupervised classifications (86.5% for forest/non-forest maps). Applying the LSMA-derived metric to study forest changes, our study region experienced a net 15.0% loss of 1995 forest area, and a 7.0% overall reduction in the total forest-occupied land cover from 1995–2011. Areas of gross forest gain were substantial, totaling 13.6% of the 1995 forest area. We found differing patterns in gross forest losses and gains among sub-regions and through time in our Tabora study area, which provide bases for testable hypotheses in future research on regional and localized drivers affecting forest cover. Our finding that non-green surface components distinguished forest from non-forest via an LSMA approach may be widely applicable to studying forest conversions in Miombo Woodlands and other dry tropical forests. This approach may also be useful for evaluating how land cover conditions change in response to potential land use or climate driving variables, or the impact of land changes for carbon balance and other ecosystem processes.

[1]  D. Roberts,et al.  Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries , 2013 .

[2]  Peng Gong,et al.  Land cover assessment with MODIS imagery in southern African Miombo ecosystems , 2005 .

[3]  Erik Prins,et al.  Deforestation and regrowth phenology in miombo woodland : assessed by Landsat Multispectral Scanner System data , 1996 .

[4]  E. Chidumayo.  Forest degradation and recovery in a miombo woodland landscape in Zambia: 22 years of observations on permanent sample plots , 2013 .

[5]  F. Achard,et al.  A Synthesis of Information on Rapid Land-cover Change for the Period 1981–2000 , 2005 .

[6]  M. Vasconcelos,et al.  Spatial dynamics and quantification of deforestation in the central-plateau woodlands of Angola (1990–2009) , 2011 .

[7]  S. L Furby,et al.  Calibrating images from different dates to ‘like-value’ digital counts , 2001 .

[8]  Alan R. Gillespie,et al.  Remote Sensing of Landscapes with Spectral Images , 2006 .

[9]  Frédéric Achard,et al.  Continental estimates of forest cover and forest cover changes in the dry ecosystems of Africa between 1990 and 2000 , 2013, Journal of biogeography.

[10]  F. Morari,et al.  Monitoring desertification in a Savannah region in Sudan using Landsat images and spectral mixture analysis , 2012 .

[11]  Andrea Perlis,et al.  Global forest resources assessment 2000 : main report , 2001 .

[12]  Davison Gumbo,et al.  The environmental impacts of charcoal production in tropical ecosystems of the world: a synthesis , 2013 .

[13]  Dar A. Roberts,et al.  Long‐term, high‐spatial resolution carbon balance monitoring of the Amazonian frontier: Predisturbance and postdisturbance carbon emissions and uptake , 2013 .

[14]  Casey M. Ryan,et al.  Carbon sequestration and biodiversity of re-growing miombo woodlands in Mozambique , 2008 .

[15]  Neil D. Burgess,et al.  Deforestation in an African biodiversity hotspot: extent, variation and the effectiveness of protected areas , 2013 .

[16]  Derek Eamus,et al.  Ecophysiology of trees of seasonally dry tropics: Comparisons among phenologies , 2001 .

[17]  C. Milesi,et al.  Multi-scale standardized spectral mixture models , 2013 .

[18]  M. Walsh,et al.  Identifying potential synergies and trade-offs for meeting food security and climate change objectives in sub-Saharan Africa , 2010, Proceedings of the National Academy of Sciences.

[19]  John B. Adams,et al.  Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon , 1995 .

[20]  P. D’Odorico,et al.  Physical and biological feedbacks of deforestation , 2012 .

[21]  Alan R. Gillespie,et al.  Structural stage in Pacific Northwest forests estimated using simple mixing models of multispectral images , 2002 .

[22]  A. Grainger.  Difficulties in tracking the long-term global trend in tropical forest area , 2008, Proceedings of the National Academy of Sciences.

[23]  Alan H. Strahler,et al.  Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments , 2005 .

[24]  L. Larson.  Ecology Control and Economic Development In East African History. The Case of Tanganyika 1850–1950 , 1978 .

[25]  Erkki Tomppo,et al.  A report to the food and agriculture organization of the united nations (FAO) in support of sampling study for National Forestry Resources Monitoring and Assessment (NAFORMA) in Tanzania , 2010 .

[26]  R. Colwell.  Remote sensing of the environment , 1980, Nature.

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

[28]  D. Lobell,et al.  Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. , 2000 .

[29]  J. W. Wagtendonk,et al.  Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity , 2004 .

[30]  Jian Yang,et al.  Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: Comparison of vegetation indices and spectral mixture analysis , 2012 .

[31]  H. Geist,et al.  Tobacco growers at the crossroads: towards a comparison of diversification and ecosystem impacts. , 2009 .

[32]  D. Fuller,et al.  Canopy phenology of some mopane and miombo woodlands in eastern Zambia , 1999 .

[33]  L. Merbold,et al.  The charcoal trap: Miombo forests and the energy needs of people , 2011, Carbon balance and management.

[34]  B. Campbell.  The miombo in transition: woodlands and welfare in Africa. , 1996 .

[35]  M. R. Carter,et al.  Terrestrial Ecosystems , 2018, Critical Transitions in Nature and Society.

[36]  E. Lambin,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:Global land use change, economic globalization, and the looming land scarcity , 2011 .

[37]  E. Lambin,et al.  Dynamics of Land-Use and Land-Cover Change in Tropical Regions , 2003 .

[38]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[39]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[40]  E. Lambin,et al.  A Multilevel Analysis of the Impact of Land Use on Interannual Land-Cover Change in East Africa , 2007, Ecosystems.

[41]  John F. Mustard,et al.  REGIONAL PATTERNS OF PLANT COMMUNITY RESPONSE TO CHANGES IN WATER: OWENS VALLEY, CALIFORNIA , 2003 .