Differentiation of Acacia koa forest stands across an elevation gradient in Hawai‘i using fine-resolution remotely sensed imagery

Koa (Acacia koa) forests are found across broad environmental gradients in the Hawaiian Islands. Previous studies have identified important environmental factors controlling stand structure and productivity at the plot level, but these have not been applied at the landscape level because of small-scale spatial variability. The goal of this study is to compare the differentiation of koa forest types across an elevation/temperature gradient ranging from 1200 to 2050 m asl (17–13°C mean annual temperature (MAT)) through the analysis of field measurements of forest structure and fine-resolution remotely sensed imagery. Several vegetation indices (VIs) (atmospherically resistant vegetation index (ARVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), simple ratio (SR) and modified simple ratio (MSR)) are calculated from IKONOS satellite imagery of these stands and analysed using supervised classification techniques. This procedure allows a clear differentiation of koa stands from areas dominated by grasses, shrubs and bare lava. Across the elevation gradient, VIs allow differentiation of three koa forest stand classes at upper, intermediate and lower elevations. In agreement with the image classification, analysis of variance (ANOVA) of tree height and leaf phosphorus (P) suggests that there are also three significantly different groups of koa stands at those elevations. A landscape-scale map of land cover and koa stand classes demonstrates both the general trend with elevation and the small-scale heterogeneity that exists across the elevation gradient. Application of these classification techniques with fine spatial resolution imagery can improve the characterization of different koa stand types across the islands of Hawai‘i, which should aid both the conservation and utilization of this ecologically important species.

[1]  M. Herold,et al.  Spatial Metrics and Image Texture for Mapping Urban Land Use , 2003 .

[2]  R. Houborg,et al.  Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data , 2007 .

[3]  E Brown de Colstoun,et al.  National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier , 2003 .

[4]  P. Scowcroft,et al.  Understory structure in a 23-year-old Acacia koa forest and 2-year growth responses to silvicultural treatments , 2008 .

[5]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[6]  Jiaguo Qi,et al.  Assessment of Tropical Forest Degradation with Canopy Fractional Cover from Landsat ETM+ and IKONOS Imagery , 2005 .

[7]  Hui Qing Liu,et al.  A feedback based modification of the NDVI to minimize canopy background and atmospheric noise , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Frederick C. Meinzer,et al.  Forest growth along a rainfall gradient in Hawaii: Acacia koa stand structure, productivity, foliar nutrients, and water- and nutrient-use efficiencies , 1995, Oecologia.

[9]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

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

[11]  C. Jordan Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[12]  Robert O. Green,et al.  Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS , 1997 .

[13]  Roberta E. Martin,et al.  Remote sensing of native and invasive species in Hawaiian forests , 2008 .

[14]  D. P. Groeneveld,et al.  Broadband vegetation index performance evaluated for a low‐cover environment , 2006 .

[15]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[16]  Nicholas C. Coops,et al.  The use of multiscale remote sensing imagery to derive regional estimates of forest growth capacity using 3-PGS , 2001 .

[17]  Didier Tanré,et al.  Atmospherically resistant vegetation index (ARVI) for EOS-MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..

[18]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[19]  P. Gong,et al.  Individual Tree-Crown Delineation and Treetop Detection in High-Spatial-Resolution Aerial Imagery , 2004 .

[20]  F. Fosberg,et al.  Vegetation of the Tropical Pacific Islands , 1997, Ecological Studies.

[21]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[22]  W. Walker,et al.  Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy , 2006 .

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

[24]  P. Vitousek,et al.  STAND DYNAMICS, NITROGEN ACCUMULATION, AND SYMBIOTIC NITROGEN FIXATION IN REGENERATING STANDS OF ACACIA KOA , 2001 .

[25]  J. Ewel,et al.  Koa (Acacia koa) ecology and silviculture , 2013 .

[26]  Andreas Huth,et al.  Concepts for the aggregation of tropical tree species into functional types and the application to Sabah's lowland rain forests , 2000, Journal of Tropical Ecology.

[27]  J. Chen Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications , 1996 .

[28]  S. Franklin,et al.  OBJECT-BASED ANALYSIS OF IKONOS-2 IMAGERY FOR EXTRACTION OF FOREST INVENTORY PARAMETERS , 2006 .

[29]  Roberta E. Martin,et al.  Evapotranspiration and energy balance of native wet montane cloud forest in Hawai‘i , 2009 .

[30]  K. Soudani,et al.  Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands , 2006 .

[31]  G. Asner Biophysical and Biochemical Sources of Variability in Canopy Reflectance , 1998 .

[32]  P. Gong,et al.  Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index , 2008, Sensors.

[33]  Tomoaki Miura,et al.  An assessment of Hawaiian dry forest condition with fine resolution remote sensing , 2008 .

[34]  Dean F. Meason,et al.  Indicators of forest ecosystem productivity and nutrient status across precipitation and temperature gradients in Hawaii , 2007, Journal of Tropical Ecology.

[35]  A. Kadıoǧulları,et al.  Classification and mapping forest sites using geographic information system (GIS): a case study in Artvin Province , 2008, Environmental monitoring and assessment.

[36]  Andrew K. Skidmore,et al.  Integration of classification methods for improvement of land-cover map accuracy , 2002 .

[37]  Alexandre Carleer,et al.  Assessment of Very High Spatial Resolution Satellite Image Segmentations , 2005 .

[38]  J. R. Jensen Remote Sensing of the Environment: An Earth Resource Perspective , 2000 .

[39]  A. Ares,et al.  Water supply regulates structure, productivity, and water use efficiency of Acacia koa forest in Hawaii , 1999, Oecologia.

[40]  T. Idol,et al.  Growth response of Acacia koa trees to thinning, grass control, and phosphorus fertilization in a secondary forest in Hawai‘i , 2007 .

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

[42]  Lorenzo Bruzzone,et al.  An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection , 1995, IEEE Trans. Geosci. Remote. Sens..