Modeling woody vegetation resources using Landsat TM imagery in northern Namibia

In 1995 a forest inventory covering northern Namibia was initiated, based on stratified systematic field sampling of plots with a radius of up to 30 m. In these plots detailed tree parameters were measured. Due to security problems the most important wooded parts of the area could not be covered. This study investigated whether Landsat TM imagery could be used to estimate woody vegetation parameters. As the existing field sampling method did not result in significant relationships between pixel values of different bands and tree cover, two sampling methods of different design were tested. Both resulted in statistically significant relationships between tree cover and pixel values of mainly band 4 reflectance of Landsat TM. Regression equations to estimate cover and volumes were obtained. The increased size of the sample plots in both methods was the main reason for improved correlations. The relation between tree cover and Landsat TM band 4 was influenced by fire scars and patches of heavy grazing. Estimated tree cover and volumes obtained by remote sensing were compared with volume estimates obtained by field inventories. All fell within 95 % confidence limits of the field estimates. The results suggest that Landsat TM imagery is suitable for estimating tree cover, volumes and biomass on a regional scale for dry semi-deciduous Kalahari woodland vegetation. More research is needed to better understand the impacts of fire and heavy grazing on cover estimates and future inventories should use larger sample plots.

[1]  B. Koch,et al.  Spectroradiometer measurements in the laboratory and in the field to analyse the influence of different damage symptoms on the reflection spectra of forest trees , 1990 .

[2]  M. McKittrick,et al.  Living on the Land: Change in Forest Cover in North-Central Namibia 1943-1996 , 2002 .

[3]  Antti Erkkilä,et al.  Forest Cover Change in the Ohangwena Region, Northern Namibia: a Case Study Based on Multitemporal Landsat Images and Aerial Photography , 1999 .

[4]  Andrew T. Hudak,et al.  Textural analysis of high resolution imagery to quantify bush encroachment in Madikwe Game Reserve, South Africa, 1955-1996 , 2001 .

[5]  Steven W. Running,et al.  Remote sensing of temperate coniferous forest leaf area index The influence of canopy closure, understory vegetation and background reflectance , 1990 .

[6]  M. Butera,et al.  A Correlation and Regression Analysis of Percent Canopy Closure Versus TMS Spectral Response for Selected Forest Sites in the San Juan National Forest, Colorado , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Siamak Khorram,et al.  A comparison of SPOT and Landsat-TM data for use in conducting inventories of forest resources , 1992 .

[8]  C. Wessman,et al.  Textural Analysis of Historical Aerial Photography to Characterize Woody Plant Encroachment in South African Savanna , 1998 .

[9]  Douglas Stow,et al.  Assessing the relationship between spectral vegetation indices and shrub cover in the Jornada Basin, New Mexico , 1993 .

[10]  Pe Lemmon A new instrument for measuring forest overstory density , 1957 .

[11]  K. R. McCLOY,et al.  Mapping the density of woody vegetative cover using Landsat MSS digital data , 1991 .

[12]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[13]  S. Khorram,et al.  Modeling And Multitemporal Evaluation Of Forest Decline With Landsat Tm Digital Data , 1990 .

[14]  Janet Franklin,et al.  Thematic mapper analysis of coniferous forest structure and composition , 1986 .

[15]  Andrew T. Hudak,et al.  Rangeland Mismanagement in South Africa: Failure to Apply Ecological Knowledge , 1999 .

[16]  C. Goulding,et al.  Estimation of timber volume in a coniferous plantation forest using Landsat TM , 1997 .

[17]  B. M. Goulevitch,et al.  The Statewide Landcover and Trees Study (SLATS) monitoring land cover change and greenhouse gas emissions in Queensland , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[18]  V. Chewings,et al.  Comparison of crown cover estimates for woody vegetation in arid rangelands , 1988 .

[19]  S. Archer,et al.  Tree-grass dynamics in a Prosopis-thornscrub savanna parkland: Reconstructing the past and predicting the future , 1995 .

[20]  Timo Tokola,et al.  Relative Calibration of Multitemporal Landsat Data for Forest Cover Change Detection , 1999 .

[21]  H. Siiskonen,et al.  Forestry in Namibia, 1850-1990 , 1992 .

[22]  Markku Similä,et al.  Forest inventory by compartments using satellite imagery. , 1987 .

[23]  J. Colwell Vegetation canopy reflectance , 1974 .