Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data

Abstract This study uses 2010–2011 Landsat Thematic Mapper (TM) imagery to estimate total forested area in Haiti. The thematic map was generated using radiometric normalization of digital numbers by a modified normalization method utilizing pseudo-invariant polygons (PIPs), followed by supervised classification of the mosaicked image using the Food and Agriculture Organization (FAO) of the United Nations Land Cover Classification System. Classification results were compared to other sources of land-cover data produced for similar years, with an emphasis on the statistics presented by the FAO. Three global land cover datasets (GLC2000, Globcover, 2009 , and MODIS MCD12Q1), and a national-scale dataset (a land cover analysis by Haitian National Centre for Geospatial Information (CNIGS)) were reclassified and compared. According to our classification, approximately 32.3% of Haiti's total land area was tree covered in 2010–2011. This result was confirmed using an error-adjusted area estimator, which predicted a tree covered area of 32.4%. Standardization to the FAO's forest cover class definition reduces the amount of tree cover of our supervised classification to 29.4%. This result was greater than the reported FAO value of 4% and the value for the recoded GLC2000 dataset of 7.0%, but is comparable to values for three other recoded datasets: MCD12Q1 (21.1%), Globcover (2009) (26.9%), and CNIGS (19.5%). We propose that at coarse resolutions, the segmented and patchy nature of Haiti's forests resulted in a systematic underestimation of the extent of forest cover. It appears the best explanation for the significant difference between our results, FAO statistics, and compared datasets is the accuracy of the data sources and the resolution of the imagery used for land cover analyses. Analysis of recoded global datasets and results from this study suggest a strong linear relationship ( R 2  = 0.996 for tree cover) between spatial resolution and land cover estimates.

[1]  Robert A. Schowengerdt,et al.  A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification , 1995, IEEE Trans. Geosci. Remote. Sens..

[2]  Philip J. Howarth,et al.  High Spatial Resolution Remote Sensing Data for Forest Ecosystem Classification: An Examination of Spatial Scale , 2000 .

[3]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[4]  John Rogan,et al.  Mapping land-cover change in a Haitian watershed using a combined spectral mixture analysis and classification tree procedure , 2010 .

[5]  Michael A. Wulder,et al.  Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas , 2002 .

[6]  L. Teeter,et al.  Land tenure, population pressure, and deforestation in Haiti: The case of Forêt des Pins Reserve , 2007 .

[7]  Jeffrey A. McNeely,et al.  Ecosystem Considerations for Postdisaster Recovery: Lessons from China, Pakistan, and Elsewhere for Recovery Planning in Haiti , 2011 .

[8]  P. Wampler,et al.  Spring flow, bacterial contamination, and water resources in rural Haiti , 2011 .

[9]  Steffen Fritz,et al.  Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications , 2008 .

[10]  C. Koyuncu,et al.  The Impact of Corruption on Deforestation: A Cross-Country Evidence , 2008 .

[11]  T. Mitchell Aide,et al.  Land Change in the Greater Antilles between 2001 and 2010 , 2013 .

[12]  Chandra Giri,et al.  A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets , 2005 .

[13]  Conghe Song,et al.  The spatial manifestation of forest succession in optical imagery: The potential of multiresolution imagery , 2002 .

[14]  T. Mitchell Aide,et al.  A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America , 2010 .

[15]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[16]  B. Huber,et al.  High level of endemism in Haiti's last remaining forests: a revision of Modisimus (Araneae: Pholcidae) on Hispaniola, using morphology and molecules. , 2010 .

[17]  D. Geneletti,et al.  Analysing land cover and land use change processes at watershed level: A multitemporal study in the Lake Cuitzeo Watershed, Mexico (1975–2003) , 2011 .

[18]  N. Coops,et al.  High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization , 2004 .

[19]  Jeffrey S. Wilson,et al.  Remote Sensing of Spatial and Temporal Vegetation Dynamics in Hispaniola: A Comparison of Haiti and the Dominican Republic , 2001 .

[20]  A. Mather,et al.  Assessing the world's forests , 2005 .

[21]  Camilo Daleles Rennó,et al.  Assessment of deforestation in the Lower Amazon floodplain using historical Landsat MSS/TM imagery , 2011 .

[22]  S. Fritz,et al.  Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa , 2010 .

[23]  Gang Xu,et al.  Information fusion for rural land-use classification with high-resolution satellite imagery , 2003, IEEE Trans. Geosci. Remote. Sens..

[24]  Conghe Song,et al.  Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon , 2006 .

[25]  Eric F. Lambin,et al.  Forest transitions: towards a global understanding of land use change , 2005 .

[26]  Hugh D. Eva,et al.  Forest Cover Changes in Tropical South and Central America from 1990 to 2005 and Related Carbon Emissions and Removals , 2012, Remote. Sens..

[27]  M. Hansen,et al.  A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products , 2000 .

[28]  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 .

[29]  Alejandro Velázquez,et al.  Trends of tropical deforestation in Southeast Mexico , 2010 .

[30]  R. Foxx.  TE TERRE A FATIGE ‘THE EARTH IS TIRED’: REVERSING DEFORESTATION IN HAITI , 2012 .

[31]  Martin Jung,et al.  Exploiting synergies of global land cover products for carbon cycle modeling , 2006 .

[32]  Andrés Etter,et al.  Land Cover Change in Colombia: Surprising Forest Recovery Trends between 2001 and 2010 , 2012, PloS one.

[33]  Luis Cayuela,et al.  Author ' s personal copy Monitoring land cover change of the dryland forest landscape of Central Chile ( 1975 – 2008 ) , 2010 .

[34]  Frédéric Achard,et al.  Global forest land-use change 1990–2005 , 2012 .

[35]  Martin Herold,et al.  Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets , 2008 .

[36]  Eric F. Lambin,et al.  Estimation of tropical forest area from coarse spatial resolution data: A two-step correction function for proportional errors due to spatial aggregation , 1995 .

[37]  S. Nilsson,et al.  A spatial comparison of four satellite derived 1 km global land cover datasets , 2006 .

[38]  S. Wright,et al.  Tropical forests in a changing environment. , 2005, Trends in ecology & evolution.

[39]  F. Stevens,et al.  Amazon deforestation: Rates and patterns of land cover change and fragmentation in Pando, northern Bolivia, 1986 to 2005 , 2011 .

[40]  W. Mcconnell.  Meeting in the middle: The challenge of meso-level integration , 2002 .

[41]  W. Sun,et al.  Buffering functions of mangroves in the 2004 tsunami , 2009, Int. J. Digit. Earth.

[42]  A. Kangas.  Assessing the World's Forests , 2006 .

[43]  Azizur R. Molla,et al.  Using ArcMap, Google Earth, and Global Positioning Systems to select and locate random households in rural Haiti , 2013, International Journal of Health Geographics.

[44]  W. Cohen,et al.  Detection of deforestation and land conversion in Rondônia, Brazil using change detection techniques , 2004 .

[45]  Matthew C. Hansen,et al.  The global Landsat imagery database for the FAO FRA remote sensing survey , 2011, Int. J. Digit. Earth.

[46]  Analysis of vegetation patterns in the Hispaniola island using AVHRR data , 2006 .

[47]  Steffen Fritz,et al.  Highlighting continued uncertainty in global land cover maps for the user community , 2011 .

[48]  William J. Volchok,et al.  Radiometric scene normalization using pseudoinvariant features , 1988 .

[49]  Jinwei Dong,et al.  A comparison of forest cover maps in Mainland Southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA , 2012 .

[50]  T. Mitchell Aide,et al.  Land change for all municipalities in Latin America and the Caribbean assessed from 250-m MODIS imagery (2001–2010) , 2012 .

[51]  P. Nair,et al.  Agroforestry adoption in Haiti: the importance of household and farm characteristics , 2003, Agroforestry Systems.

[52]  R. Fournier,et al.  Remote sensing and the measurement of geographical entities in a forested environment. 2. The optimal spatial resolution , 1994 .

[53]  G. G. Stevenson.  The production, distribution, and consumption of fuelwood in Haiti. , 1989 .

[54]  S. Hedges.  An overview of the evolution and conservation of West Indian amphibians and reptiles , 2006 .

[55]  M. Herold,et al.  An assessment of deforestation and forest degradation drivers in developing countries , 2012 .

[56]  Tonny J. Oyana,et al.  Assessing the impact of the 2004 tsunami on mangroves using remote sensing and GIS techniques , 2008 .

[57]  Armel Thibaut Kaptué Tchuenté,et al.  Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[58]  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.

[59]  O. Evelyn,et al.  Forest cover and deforestation in Jamaica: an analysis of forest cover estimates over time 1 , 2003 .

[60]  Robert C. Harriss,et al.  Deforestation in Costa Rica: A Quantitative Analysis Using Remote Sensing Imagery1 , 2001 .

[61]  P. Defourny,et al.  Accuracy Assessment of a 300 m Global Land Cover Map : The GlobCover Experience , 2009 .

[62]  Peter H. Verburg,et al.  Combining remote sensing and household level data for regional scale analysis of land cover change in the Brazilian Amazon , 2010 .

[63]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[64]  M. Clark,et al.  Deforestation and Reforestation of Latin America and the Caribbean (2001–2010) , 2013 .

[65]  C. Justice,et al.  Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM + data , 2012 .

[66]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[67]  D. Hodell,et al.  A 10,300 14C yr Record of Climate and Vegetation Change from Haiti , 1999, Quaternary Research.

[68]  Gregory Husak,et al.  Using high resolution satellite imagery to estimate cropped area in Guatemala and Haiti , 2012 .

[69]  Kazuhito Ichii,et al.  Multi-temporal analysis of deforestation in Rondônia state in Brazil using Landsat MSS, TM, ETM+ and NOAA AVHRR imagery and its relationship to changes in the local hydrological environment , 2003 .

[70]  J. Cihlar.  Land cover mapping of large areas from satellites: Status and research priorities , 2000 .

[71]  Douglas C. Morton,et al.  Rapid assessment of annual deforestation in the Brazilian Amazon using MODIS data , 2005 .

[72]  William A. Gould,et al.  Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data , 2007 .

[73]  A. Belward,et al.  GLC 2000 : a new approach to global land cover mapping from Earth observation data , 2005 .

[74]  Thomas Blaschke,et al.  Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[75]  Michael A. Wulder,et al.  Landsat continuity: Issues and opportunities for land cover monitoring , 2008 .

[76]  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 .

[77]  Vereda Johnson Williams,et al.  A Case Study of the Desertification of Haiti , 2011 .