HABITAT UTILISATION PATTERN OF Lantana camara IN UDAWALAWE NATIONAL P,ARK IN SRI LANKA

Lantana camara, of the family Verbenaceae is native to the tropical and sub tropicalregions of Central and South America. It grows in a wide range of habitats, from exposeddry hillsides to wet heavily shaded gullies. It was introduced to Sri Lanka in 1926 throughthe Royal Botanic Gardens and currently it has spread across the island significantly andhas become an invasive species in most of the habitats including the Udawalawe NationalPark. Through its invasive nature it has become a threat to fauna and flora of the Park dueto habitat invasion and loss. Therefore, the main objective of this study is to assess thehabitat utilization pattern of L. camara in the Park.For this, the extent of the species in the 3 selected habitats of the Park ie. Scrub grassland,Medium height scrub and Scrub forest transitions was studied using field survey using GPSmstruments. This data was mapped subsequently in order to get an overall idea of thehabitat utilization of the species in the Park. Further, the relative abundance of this speciesin each of the 3 habitats selected was studied using 10 x 10 m quadrates replicated 3 timesfor better accuracy. Field observations were carried out in L. camara trees on the followingparameters; presence of flowers and seeds on the tree, time taken to start flowering, seeddispersal mechanism, seed germination and spreading ability. Seeds were also germinatedin the green house of the University of Sri Jayewardenepura to assess the germinationperiod and germination energy.The results showed significant variation was observed between the habitat types sampled inthe extent of L. camara; the figures being 75% in the Scrub grassland, 65% in Mediumheight scrub and 45°;(, being in the Scrub forest transitions. When these results weremapped, it showed 20% invasion of L. ccmara in the Park especially into the vegetationtypes sampled in the study ..When taken as an overview, the main road and Veheragolla,Seenuggala, Mau ara, Thirnbiriyarnankada and 5th mile post are the most densely andcontinuously distributed areas.The relative abundance and height of L. camara was highest in the Medium height scrubgrassland (92%) while the lowest was observed in the Scrub forest transitional area (87%).The species produced large number of flowers and seeds (20 - 30/cluster). The germinationability of the plant was very high (85 (~,O Therefore, the present study highlights theinvasive nature of the plant especially in open scrub areas and the need to effectivelymange the same to secure the biodiversity of the Park.

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+. Though broad scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass (AGB) at spatial grains ranging from 5 to 250m (0.025–6.25 ha), and we evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that the spatial sampling error in AGB is large for standard plot sizes, averaging 46.3% for 0.1 ha subplots and 16.6% for 1 ha subplots. Topographically heterogeneous sites showed positive spatial autocorrelation in AGB at scales of 100m and above; at smaller scales, most study sites showed negative or nonexistent spatial autocorrelation in AGB. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGB leads to a substantial “dilution” bias in calibration parameters, a bias that cannot be removed with current statistical methods. Overall, our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDDC (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mgha⁻¹) at spatial scales ranging from 5 to 250 m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial “dilution” bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise