Evaluating the trade-off between food and timber resulting from the conversion of Miombo forests to agricultural land in Angola using multi-temporal Landsat data.

The repopulation of abandoned areas in Angola after 27years of civil war led to a fast and extensive expansion of agricultural fields to meet the rising food demand. Yet, the increase in crop production at the expense of natural resources carries an inherent potential for conflicts since the demand for timber and wood extraction are also supposed to rise. We use the concept of ecosystem services to evaluate the trade-off between food and woody biomass. Our study area is located in central Angola, in the highlands of the upper Okavango catchment. We used Landsat data (spatial resolution: 30×30m) with a bi-temporal and multi-seasonal change detection approach for five time steps between 1989 and 2013 to estimate the conversion area from woodland to agriculture. Overall accuracy is 95%, user's accuracy varies from 89-95% and producer's accuracy ranges between 92-99%. To quantify the trade-off between woody biomass and the amount of food, this information was combined with indicator values and we furthermore assessed biomass regrowth on fallows. Our results reveal a constant rise in agricultural expansion from 1989-2013 with the mean annual deforestation rate increasing from roughly 5300ha up to about 12,000ha. Overall, 5.6% of the forested areas were converted to agriculture, whereas the FAO states a national deforestation rate for Angola of 5% from 1990-2010 (FAO, 2010). In the last time step 961,000t per year of woodland were cleared to potentially produce 1240t per year of maize. Current global agro-economical projections forecast increasing pressure on tropical dry forests from large-scale agriculture schemes (Gasparri et al., 2015; Searchinger and Heimlich, 2015). Our study underlines the importance of considering subsistence-related change processes, which may contribute significantly to negative effects associated with deforestation and degradation of these forest ecosystems.

[1]  Birger Solberg,et al.  Estimation of biomass and volume in Miombo Woodland at Kitulangalo Forest Reserve, Tanzania , 1994 .

[2]  S. Holden Adjustment Policies, Peasant Household Resource Allocation and Deforestation in Northern Zambia: An Overview and Some Policy Conclusions , 1997 .

[3]  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 .

[4]  João Manuel de Brito Carreiras,et al.  Estimating the Above-Ground Biomass in Miombo Savanna Woodlands (Mozambique, East Africa) Using L-Band Synthetic Aperture Radar Data , 2013, Remote. Sens..

[5]  David P. Roy,et al.  Accessing free Landsat data via the Internet: Africa's challenge , 2010 .

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

[7]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[8]  S. Syampungani Vegetation change analysis and ecological recovery of the copperbelt Miombo woodland of Zambia , 2009 .

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

[10]  Dirk Pflugmacher,et al.  Cross-border forest disturbance and the role of natural rubber in mainland Southeast Asia using annual Landsat time series , 2015 .

[11]  C. Woodcock,et al.  Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .

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

[13]  J. Oldeland,et al.  Impact of Shifting Cultivation on Dense Tropical Woodlands in Southeast Angola , 2015 .

[14]  Shoko Kobayashi,et al.  The integrated radiometric correction of optical remote sensing imageries , 2008 .

[15]  Joachim Hill,et al.  An Operational Radiometric Landsat Preprocessing Framework for Large-Area Time Series Applications , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[16]  S. A. Barber Corn Residue Management and Soil Organic Matter1 , 1979 .

[17]  John L. Dwyer,et al.  Landsat: building a strong future , 2012 .

[18]  Andrew J. Dougill,et al.  Floristic composition, species diversity and carbon storage in charcoal and agriculture fallows and management implications in Miombo woodlands of Zambia , 2013 .

[19]  E. Chidumayo,et al.  Changes in miombo woodland structure under different land tenure and use systems in central Zambia , 2002 .

[20]  Pete Smith,et al.  UK National Ecosystem Assessment:Technical report , 2011 .

[21]  D. Richardson,et al.  Spatial congruence between biodiversity and ecosystem services in South Africa , 2009 .

[22]  Michael Schmidt,et al.  Enhancing the Detectability of Clouds and Their Shadows in Multitemporal Dryland Landsat Imagery: Extending Fmask , 2015, IEEE Geoscience and Remote Sensing Letters.

[23]  J. B. Dent,et al.  Goal programming: Application in the management of the miombo woodland in Mozambique , 2001, Eur. J. Oper. Res..

[24]  J. Lund,et al.  Are we getting there? Evidence of decentralized forest management from the Tanzanian Miombo woodlands , 2008 .

[25]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

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

[27]  E. Chidumayo Estimating tree biomass and changes in root biomass following clear-cutting of Brachystegia-Julbernardia (miombo) woodland in central Zambia , 2013, Environmental Conservation.

[28]  Anne Schneibel,et al.  Agricultural expansion during the post-civil war period in southern Angola based on bi-temporal Landsat data. , 2013 .

[29]  T. Steudel,et al.  The current status of the Okavango Basin , 2015 .

[30]  C. Ryan,et al.  How resilient are African woodlands to disturbance from shifting cultivation? , 2015, Ecological applications : a publication of the Ecological Society of America.

[31]  Ana C. L. Sá,et al.  Assessing the feasibility of sub-pixel burned area mapping in miombo woodlands of northern Mozambique using MODIS imagery , 2003 .

[32]  R. DeFries,et al.  Land‐use choices: balancing human needs and ecosystem function , 2004 .

[33]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[34]  Sassan Saatchi,et al.  Aboveground biomass and leaf area index (LAI) mapping for Niassa Reserve, northern Mozambique , 2008 .

[35]  R. D. Groot,et al.  Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making , 2010 .

[36]  J. Abdallah,et al.  Overview of Miombo Woodlands in Tanzania , 2007 .

[37]  R. Malimbwi,et al.  Contribution of charcoal extraction to deforestation: experience from CHAPOSA Research Project. , 2000 .

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

[39]  Soo Chin Liew,et al.  Deforestation rates in insular Southeast Asia between 2000 and 2010 , 2011 .

[40]  Kevin Balkwill,et al.  Economics of charcoal production in miombo woodlands of eastern Tanzania: some hidden costs associated with commercialization of the resources , 2000 .

[41]  Partha Dasgupta,et al.  Living beyond our means : natural assets and human well-being, statement form the board , 2005 .

[42]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[43]  Christian P. Giardina,et al.  The effects of slash burning on ecosystem nutrients during the land preparation phase of shifting cultivation , 2000, Plant and Soil.

[44]  Ana Piedade,et al.  Cusseque - Use of Woody Plants , 2013 .

[45]  Timothy D. Searchinger,et al.  Installment 9 of "Creating a Sustainable Food Future" AVOIDING BIOENERGY COMPETITION FOR FOOD CROPS AND LAND , 2015 .

[46]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[47]  Gary W. Johnson,et al.  ARIES (ARtificial Intelligence for Ecosystem Services ): a new tool for ecosystem services assessment, planning, and valuation. , 2009 .

[48]  Les Kaufman,et al.  The Multiscale Integrated Model of Ecosystem Services (MIMES): Simulating the interactions of coupled human and natural systems , 2015 .

[49]  Jan Verbesselt,et al.  Using spatial context to improve early detection of deforestation from Landsat time series , 2016 .

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

[51]  David P. Roy,et al.  Continuity of Landsat observations: Short term considerations , 2011 .

[52]  T. Kuemmerle,et al.  The Emerging Soybean Production Frontier in Southern Africa: Conservation Challenges and the Role of South‐South Telecouplings , 2016 .

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

[54]  Stephen V. Stehman,et al.  A comparison of sampling designs for estimating deforestation from Landsat imagery: A case study of the Brazilian Legal Amazon , 2009 .

[55]  Giles M. Foody,et al.  Good practices for estimating area and assessing accuracy of land change , 2014 .

[56]  P. Deschamps,et al.  Atmospheric modeling for space measurements of ground reflectances, including bidirectional properties. , 1979, Applied optics.

[57]  J. Kerr,et al.  From space to species: ecological applications for remote sensing , 2003 .

[58]  G. Daily,et al.  Ecosystem Services in Decision Making: Time to Deliver , 2009 .