Petri Pellikka

Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements

Leaf area index (LAI) is one of the surface parameters that has importance in climate, weather, and ecological studies, and has been routinely estimated from remote sensing measurements. Canada-wide LAI maps are now being produced using cloud-free Advanced Very High-Resolution Radiometer (AVHRR) imagery every 10 days at 1-km resolution. The archive of these products began in 1993. LAI maps at the same resolution are also being produced with images from the SPOT VEGETATION sensor. To improve the LAI algorithms and validate these products, a group of Canadian scientists acquired LAI measurements during the summer of 1998 in deciduous, conifer, and mixed forests, and in cropland. Common measurement standards using the commercial Tracing Radiation and Architecture of Canopies (TRAC) and LAI-2000 instruments were followed. Eight Landsat Thematic Mapper (TM) scenes at 30-m resolution were used to locate ground sites and to facilitate spatial scaling to 1-km pixels. In this paper, examples of Canada-wide LAI maps are presented after an assessment of their accuracy using ground measurements and the eight Landsat scenes. Methodologies for scaling from high- to coarse-resolution images that consider surface heterogeneity in terms of mixed cover types are evaluated and discussed. Using Landsat LAI images as the standard, it is shown that the accuracy of LAI values of individual AVHRR and VEGETATION pixels was in the range of 50–75%. Random and bias errors were both considerable. Bias was mostly caused by uncertainties in atmospheric correction of the Landsat images, but surface heterogeneity in terms of mixed cover types were also found to cause bias in AVHRR and SPOT VEGETATION LAI calculations. Random errors come from many sources, but pixels with mixed cover types are the main cause of random errors. As radiative signals from different vegetation types were quite different at the same LAI, accurate information about subpixel mixture of the various cover types is identified as the key to improving the accuracy of LAI estimates. D 2002 Elsevier Science Inc. All rights reserved.

Airborne remote sensing of spatiotemporal change (1955-2004) in indigenous and exotic forest cover in the Taita Hills, Kenya.

We studied changes in area and species composition of six indigenous forest fragments in the Taita Hills, Kenya using 1955 and 1995 aerial photography with 2004 airborne digital camera mosaics. The study area is part of Eastern Arc Mountains, a global biodiversity hot spot that boasts an outstanding diversity of flora and fauna and a high level of endemism. While a total of 260 ha (50%) of indigenous tropical cloud forest was lost to agriculture and bushland between 1955 and 2004, large-scale planting of exotic pines, eucalyptus, grevillea, black wattle and cypress on barren land during the same period resulted in a balanced total forest area. In the Taita Hills, like in other Afrotropical forests, indigenous forest loss may adversely affect ecosystem services.

Delimiting tropical mountain ecoregions for conservation

��������� � �� ���������� SUMMARY Ecological regions aggregate habitats with similar biophysical characteristics within well-defined boundaries, providing spatially consistent platforms for monitoring, managing and forecasting the health of interrelated ecosystems. A major obstacle to the implementation of this approach is imprecise and inconsistent boundary placement. For globally important mountain regions such as the Eastern Arc (Tanzania and Kenya), where qualitative definitions of biophysical affinity are well established, rulebased methods for landform classification provide a straightforward solution to ambiguities in region extent. The method presented in this paper encompasses the majority of both contemporary and estimated preclearance forest cover within strict topographical limits. Many of the species here tentatively considered ‘near-endemic’ could be reclassified as strictly endemic according to the derived boundaries. LandScan and census data show population density inside the ecoregion to be higher than in rural lowlands, and lowland settlement to be most probable within 30 km. This definition should help to align landscape scale conservation strategies in the Eastern Arc and promote new research in areas of predicted, but as yet undocumented, biological importance. Similar methods could work well in other regions where mountain extent is poorly resolved. Spatial data accompany the online version of this article.

Dynamic modeling of forest conversion: Simulation of past and future scenarios of rural activities expansion in the fringes of the Xingu National Park, Brazilian Amazon

Abstract The present work is committed to simulate the expansion of agricultural and cattle raising activities within a watershed located in the fringes of the Xingu National Park, Brazilian Amazon. A spatially explicit dynamic model of land cover and land use change was used to provide both past and future scenarios of forest conversion into such rural activities, aiming to identify the role of driving forces of change in the study area. The employed modeling platform – Dinamica EGO – consists in a cellular automata environment that embodies neighborhood-based transition algorithms and spatial feedback approaches in a stochastic multi-step simulation framework. Biophysical variables and legal restrictions drove this simulation model, and statistical validation tests were then conducted for the generated past simulations (from 2000 to 2005), by means of multiple resolution fitting methods. Based on optimal calibration of past simulations, future scenarios were conceived, so as to figure out trends and spatial patterns of forest conversion in the study area for the year 2015. In all simulated scenarios, pasturelands remained nearly stable throughout the analyzed period, while a large expansion in croplands took place. The most optimistic scenario indicates that more than 50% of the natural forest will be replaced by either cropland or pastureland by 2015. This modeling experiment revealed the suitability of the adopted model to simulate processes of forest conversion. It also indicates its possible further applicability in generating simulations of deforestation for areas with expanding rural activities in the Amazon and in tropical forests worldwide.

Quantification and reduction of bidirectional effects in aerial cir imagery of deciduous forest using two reference land surface types

Bidirectional effects in airborne remote sensing data are governed by land surface reflectance characteristics, atmospheric scattering and the sun‐object‐sensor angular relationship. These factors introduce brightness variations in the data that render quantitative analysis more difficult. On the other hand, bidirectional reflectance distribution functions (BRDF) of land surfaces provide information about the physical characteristics of the surface. This study has two objectives. The first is to reduce brightness variations in aerial false colour imagery using empirical methods. The second is to test if bidirectional effects must be modelled using sample data only for the land surface type under analysis or can another surrogate land cover type, which may be easier to sample in the imagery, be used. The method is based on multiple view angle imaging of two reference land surfaces using highly overlapping stereo frame format imagery. Reduction of BRDF effects includes derivation of a quantification factor used in a formula based on the Rayleigh scattering function. The performance of the method is determined by evaluating the residual variations in mean digital number (DN) of several test plots located in two overlapping images. The study area is a temperate deciduous forest damaged to varying degrees by a severe ice storm in 1998. For sixteen test plots throughout the forest, it was found that the mean DN values of the plots in the two images became more similar when the correction model was derived from samples of the same deciduous forests. However, a BRDF model derived from samples of vegetated fields nearby reduced the variations between images for some of the sixteen forest plots more than the forest BRDF model. These plots were less damaged, had high canopy cover, and were located on steep slopes oriented towards the sun. They had scattering characteristics more similar to the fields than to the deciduous forest as a whole. Two principal conclusions were derived. First, it is essential that the model used to derive the BRDF correction be from the same land cover as the land cover type under study. Second, the suitability of the quantification factors for the BRDF correction provided additional information about the forest structure and damage level.

Remote sensing and GIS for detecting changes in the aquatic vegetation of a rehabilitated lake

Remote sensing and geographical information system (GIS) methods combined with ground estimations were used to assess the effects of rehabilitation on the aquatic vegetation of a shallow, eutrophic lake in Finland. Aerial photograph interpretation was used to study the distribution of aquatic vegetation before (1953, 1996) and after (2001) rehabilitation in 1997. A digital elevation model was derived to relate the change in the aquatic vegetation to water depth. In addition, changes in the biomass of the most abundant species of the lake, Common Club-rush (Schoenoplectus lacustris), were studied by means of a regression analysis relating the ground estimations to the reflectance values (R 2=0.889, p@lt;0.001). The results indicated that the objective of the rehabilitation--to stop the overgrowth process--has at least temporarily been achieved. After rehabilitation the most noticeable change had taken place in the area covered by floating-leaved vegetation. Greater proportional changes in the aquatic vegetation areas had occurred in the deep rather than in the shallow areas. A decrease in biomass of Common Club-rush was estimated to be 30% due to rehabilitation. The use of remote sensing and GIS provided valuable information on temporal and spatial changes in the aquatic vegetation, and the methods could be applied more extensively for lake monitoring purposes.

Digital false colour aerial photographs for discrimination of aquatic macrophyte species

Digital false colour aerial photographs of four areal samples of three lakes in the Vuoksi drainage basin, Finland, that differ in trophic state and water quality were used to clarify the reflectance characteristics of various life forms and species of aquatic macrophytes at green, red and near-infrared (NIR) wavelengths. The results indicated that the classification of aquatic macrophytes is affected by the density of the vegetation, the openness of the canopies and the amounts, forms and orientations of the leaves. A dense helophyte vegetation differed from nymphaeids in having a higher reflectance in the near-infrared wavelength area than at green or red wavelengths, whereas a sparse helophyte vegetation eventually merged with nymphaeids, and the reflectance properties of the sparsest vegetation of all the life forms did not differ from those of unvegetated water areas. In general, the best classification accuracies (80–91%) were achieved when aquatic macrophytes were categorized according to life forms or phenotype groups and not species. The macrophyte categories differed from one area to another due to variation in species composition and density. In lakes with good Secchi disc transparency, classification was disturbed by reflectance from the lake bottom, while in lakes with a low Secchi disc depth, the substances contained in the water had an effect on the total reflectance.

Tree species diversity, richness, and similarity between exotic and indigenous forests in the cloud forests of Eastern Arc Mountains, Taita Hills, Kenya.

Biodiversity assessment for tree species was conducted in three forest fragments ofthe Taita Hills, southeastern Kenya to compare species diversity between and within three exotic forest plantations of pine, eucalyptus, cypress and the indigenous forests. The study sites were: Ngangao (120 ha), Chawia (86 ha), and Mbololo (185 ha). A Y-plot design was used to sample 32 plots comprising of 65 subplots. At each subplot, all juvenile trees of 5 cm and above in diameter at breast height (DBH) were enumerated and recorded by species. Tree regeneration (seedlings and saplings) was tallied by species. The Shannon-Weiner Index was used to calculate species diversity and evenness. The derived Shannon’s indices were further converted into effective numbers to show the magnitude of differences in species biodiversities. To evaluate differences in species diversities, a one way ANOVA was conducted and to separate the means, Tukey’s HSD and Duncan’s tests were used for even and uneven number of samples respectively. Jaccard’s similarity index was used to assess species similarities. There were more than 58 species whose stem densities varied between 10 and 2 000 trees per hectare. There were significant differences in species diversities between forest types and sites; the indigenous forests showed higher diversities than the exotic forests. Similarly, Chawia sites had higher species diversity than both Ngangao and Mbololo. Chawia also had a higher number of regenerated species than the two other sites, including species such as Xymalos monospora, Rapanea melanophloeos, and Syzygium guineense, which are associated with low levels of disturbance. These findings indicate that the indigenous forest is more diverse in species as would be expected in the tropics. The high species diversity in Chawia could be accounted for by the higher levels of disturbance it underwent, unlike the two other sites. The regeneration of species associated with low levels of disturbance found in the exotic plots of Chawia show the likelihood of presence of long-term soil seed banks. The low regeneration in the exotics plots observed in Ngangao and Mbololo are likely due to the absence of seed banks since some of the plantations were established on bare land (in Ngango), or the inherent physiology (allelopathy) of some of species repelling the regeneration of others.

Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data

Abstract Land use practices are changing at a fast pace in the tropics. In sub-Saharan Africa forests, woodlands and bushlands are being transformed for agricultural use to produce food for the rapidly growing population. The objective of this study was to assess the prospects of mapping the common agricultural crops in highly heterogeneous study area in south-eastern Kenya using high spatial and spectral resolution AisaEAGLE imaging spectroscopy data. Minimum noise fraction transformation was used to pack the coherent information in smaller set of bands and the data was classified with support vector machine (SVM) algorithm. A total of 35 plant species were mapped in the field and seven most dominant ones were used as classification targets. Five of the targets were agricultural crops. The overall accuracy (OA) for the classification was 90.8%. To assess the possibility of excluding the remaining 28 plant species from the classification results, 10 different probability thresholds (PT) were tried with SVM. The impact of PT was assessed with validation polygons of all 35 mapped plant species. The results showed that while PT was increased more pixels were excluded from non-target polygons than from the polygons of the seven classification targets. This increased the OA and reduced salt-and-pepper effects in the classification results. Very high spatial resolution imagery and pixel-based classification approach worked well with small targets such as maize while there was mixing of classes on the sides of the tree crowns.

The importance of realistic dispersal models in conservation planning: application of a novel modelling platform to evaluate management scenarios in an Afrotropical biodiversity hotspot.

Summary As biodiversity hotspots are often characterized by high human population densities, implementation of conservation management practices that focus only on the protection and enlargement of pristine habitats is potentially unrealistic. An alternative approach to curb species extinction risk involves improving connectivity among existing habitat patches. However, evaluation of spatially explicit management strategies is challenging, as predictive models must account for the process of dispersal, which is difficult in terms of both empirical data collection and modelling. Here, we use a novel, individual‐based modelling platform that couples demographic and mechanistic dispersal models to evaluate the effectiveness of realistic management scenarios tailored to conserve forest birds in a highly fragmented biodiversity hotspot. Scenario performance is evaluated based on the spatial population dynamics of a well‐studied forest bird species. The largest population increase was predicted to occur under scenarios increasing habitat area. However, the effectiveness was sensitive to spatial planning. Compared to adding one large patch to the habitat network, adding several small patches yielded mixed benefits: although overall population sizes increased, specific newly created patches acted as dispersal sinks, which compromised population persistence in some existing patches. Increasing matrix connectivity by the creation of stepping stones is likely to result in enhanced dispersal success and occupancy of smaller patches. Synthesis and applications. We show that the effectiveness of spatial management is strongly driven by patterns of individual dispersal across landscapes. For species conservation planning, we advocate the use of models that incorporate adequate realism in demography and, particularly, in dispersal behaviours.