Christoph Kleinn

Ecological and socio-economic functions across tropical land use systems after rainforest conversion

Tropical lowland rainforests are increasingly threatened by the expansion of agriculture and the extraction of natural resources. In Jambi Province, Indonesia, the interdisciplinary EFForTS project focuses on the ecological and socio-economic dimensions of rainforest conversion to jungle rubber agroforests and monoculture plantations of rubber and oil palm. Our data confirm that rainforest transformation and land use intensification lead to substantial losses in biodiversity and related ecosystem functions, such as decreased above- and below-ground carbon stocks. Owing to rapid step-wise transformation from forests to agroforests to monoculture plantations and renewal of each plantation type every few decades, the converted land use systems are continuously dynamic, thus hampering the adaptation of animal and plant communities. On the other hand, agricultural rainforest transformation systems provide increased income and access to education, especially for migrant smallholders. Jungle rubber and rubber monocultures are associated with higher financial land productivity but lower financial labour productivity compared to oil palm, which influences crop choice: smallholders that are labour-scarce would prefer oil palm while land-scarce smallholders would prefer rubber. Collecting long-term data in an interdisciplinary context enables us to provide decision-makers and stakeholders with scientific insights to facilitate the reconciliation between economic interests and ecological sustainability in tropical agricultural landscapes.

Comparison of linear and mixed-effect regression models and a k-nearest neighbour approach for estimation of single-tree biomass

Allometric biomass models for individual trees are typically specific to site conditions and species. They are often based on a low number of easily measured independent variables, such as diameter in breast height and tree height. A prevalence of small data sets and few study sites limit their application domain. One challenge in the context of the actual climate change discussion is to find more general approaches for reliable biomass estimation. Therefore, nonparametric approaches can be seen as an alternative to commonly used regression models. In this pilot study, we compare a nonparametric instance-based k-nearest neighbour (k-NN) approach to estimate single-tree biomass with predictions from linear mixed-effect regression models and subsidiary linear models using data sets of Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) from the National Forest Inventory of Finland. For all trees, the predictor variables diameter at breast height and tree height are known. The data s...

Two new density estimators for distance sampling

Two new density estimators for k-tree distance sampling are proposed and their performance is assessed in simulated distance sampling from 22 stem maps representing a wide range of natural to semi-natural forest tree stands with random to irregular (clustered) spatial distribution of trees. The new estimators are model-based. The first (Orbit) computes density as the inverse of the average of the areas associated with each of the k-trees nearest to a sample location. The area of the k-th tree is obtained as a prediction from a linear regression model while the area of the first is obtained via a Poisson probability integral. The second (GamPoi) is based on the expected distribution of distance to the k nearest tree in a forest where the local distribution of trees is random but the stem density varies from sample location to sample location as a gamma distribution. In a comprehensive assessment with 17 promising reference estimators, a subset composed of Morisita’s, Persson’s, Byth’s, Kleinn’s, Orbit, and GamPoi was significantly better, in terms of relative root mean square error (RRMSE), than average. GamPoi emerged as the better estimator for sample sizes larger than or equal to 30. For smaller sample sizes, both Kleinn’s and Morisita’s appear attractive.

Pushing the Limits: The Pattern and Dynamics of Rubber Monoculture Expansion in Xishuangbanna, SW China.

The rapidly growing car industry in China has led to an equally rapid expansion of monoculture rubber in many regions of South East Asia. Xishuangbanna, the second largest rubber planting area in China, located in the Indo-Burma biodiversity hotspot, supplies about 37% of the domestic natural rubber production. There, high income possibilities from rubber drive a dramatic expansion of monoculture plantations which poses a threat to natural forests. For the first time we mapped rubber plantations in and outside protected areas and their net present value for the years 1988, 2002 (Landsat, 30 m resolution) and 2010 (RapidEye, 5 m resolution). The purpose of our study was to better understand the pattern and dynamics of the expansion of rubber plantations in Xishuangbanna, as well as its economic prospects and conservation impacts. We found that 1) the area of rubber plantations was 4.5% of the total area of Xishuangbanna in 1988, 9.9% in 2002, and 22.2% in 2010; 2) rubber monoculture expanded to higher elevations and onto steeper slopes between 1988 and 2010; 3) the proportion of rubber plantations with medium economic potential dropped from 57% between 1988 and 2002 to 47% in 2010, while the proportion of plantations with lower economic potential had increased from 30% to 40%; and 4) nearly 10% of the total area of nature reserves within Xishuangbanna has been converted to rubber monoculture by 2010. On the basis of our findings, we conclude that the rapid expansion of rubber plantations into higher elevations, steeper terrain, and into nature reserves (where most of the remaining forests of Xishuangbanna are located) poses a serious threat to biodiversity and environmental services while not producing the expected economic returns. Therefore, it is essential that local governments develop long-term land use strategies for balancing economic benefits with environmental sustainability, as well as for assisting farmers with the selection of land suitable for rubber production.

Double sampling for stratification for the monitoring of sparse tree populations: the example of Populus euphratica Oliv. forests at the lower reaches of Tarim River, Southern Xinjiang, China.

Desertification is a pressing issue in the dry Tarim River basin, which is under anthropogenic stresses. In this study, double sampling for stratification (DSS) is employed to inventory Populus euphratica Oliv. forests in the lower reaches of the Tarim River Basin in Xinjiang, China. The two objectives were evaluating DSS as a sampling technique for monitoring desertification and generating baseline information for permanent observation. Here, DSS consists of two phases: in phase 1, crown cover is observed on a large sample of plots on a high resolution satellite image, and these photo-plots are stratified into five crown cover strata. Phase 2 is a stratified random sample from these photo-plots and the sampled plots are field observed. Approximately 32% of the study area is without P. euphratica trees. As expected, estimated mean poplar tree density and basal area increase with crown cover. DSS takes advantages of stratification (fieldwork efficiency and statistical precision) without the need for a priori strata delineation. It proves feasible for inventory the sparse poplar population and holds promise for the assessment of trees outside the forest, where density varies considerably and pre-stratification is intractable. It can be integrated into permanent observation systems for monitoring vegetation changes.

Estimating forest edge length from forest inventory sample dataThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time.

Forest edge length is important for landscape ecological analysis, including the analysis of fragmentation. In this paper, we estimate forest edge length using field sample data from the German National Forest Inventory as an example. The complex plot design of many large-area forest inventories allows for the estimation of forest edge length at different spatial resolutions. As expected, estimates depend on the spatial resolution: longer estimated edge lengths resulted from observations at finer spatial resolutions. From the comparison of estimated edge lengths at different spatial resolutions, conclusions about the irregularity of forest edges can be drawn: more irregular forest boundaries resulted in greater differences between the estimated lengths for different spatial resolutions. One conclusion is of particular relevance: reported forest edge length values are meaningless unless their spatial resolution is also reported. The analysis presented is an add-on to the standard estimations from a forest ...

Monitoring trees outside forests: a review

Trees outside forests (TOFs) are an important natural resource that contributes substantially to national biomass and carbon stocks and to the livelihood of people in many regions. Over the last decades, decision makers have become increasingly aware of the importance of TOF, and as a consequence, this tree resource is nowadays often considered in forest monitoring systems. Our review shows that in many cases, TOF are included in national forest inventories, applying traditional methodologies with relatively sparse networks of field sample plots. Only in some countries, such as India, the design of the inventories has considered the special features of how TOFs occur in the landscape. Several research studies utilising remote sensing for monitoring TOF have been conducted lately, but very few studies include comparative studies to optimise sampling strategies for TOF. Our review indicates that methods combining remote sensing and field surveys appear to be very promising, especially when remote sensing techniques that assess both the horizontal and vertical structures of tree resources are applied. For example, two-phase sampling strategies with laser scanning in the first phase and a field survey in the second phase appear to be effective for assessing TOF resources. However, TOFs often exhibit different characteristics than forest trees. Thus, to improve TOF monitoring, there is often a need to develop models, e.g. for biomass assessment, that are specifically adapted to this tree resource. Alternatively, field-based remote sensing methods that provide structural information about individual trees, notably terrestrial laser scanning, could be further developed for TOF monitoring applications. This also would have a potential to reduce the problem of accessing TOF during field surveys, which is a problem, for example, in countries where TOF are present on intensively utilised private grounds like gardens and agricultural fields.

Comparison of the performance of line sampling to other forms of cluster sampling

Abstract Line sampling is a widely applied method in field surveys to assess spatially distributed variables. The values of the variable of interest are recorded along line-shaped survey elements. Line sampling can be viewed as a special case of cluster sampling with fixed cluster form where the number of elements in the cluster is large and the distance between the elements small. In this study the precision of estimators for the following survey elements is examined: line segment, L-shape, cross, circle, square and equilateral triangle, i.e. we estimate lines of limited length c (in contrasts to transects which cross the entire survey region). The precision of a line sample depends on the probability density function (PDF) of interpoint distances on the line. The PDFs are given for the six survey elements listed above. In an example with a simple covariance function cov( d ) = e − d the variance for given line length c increases in the order: line segment, L-shape, circle, square, cross, equilateral triangle. A comparison of line sampling and cluster sampling with the same form and size of survey element shows that, except for the circle, clusters of only a few cluster points give more precise results than line sampling when small survey elements are used, assuming the given covariance function is correct.

Triangulation based inclusion probabilities: a design-unbiased sampling approach

A probabilistic sampling approach for design-unbiased estimation of area-related quantitative characteristics of spatially dispersed population units is proposed. The developed field protocol includes a fixed number of 3 units per sampling location and is based on partial triangulations over their natural neighbors to derive the individual inclusion probabilities. The performance of the proposed design is tested in comparison to fixed area sample plots in a simulation with two forest stands. Evaluation is based on a general approach for areal sampling in which all characteristics of the resulting population of possible samples is derived analytically by means of a complete tessellation of the areal sampling frame. The example simulation shows promising results. Expected errors under this design are comparable to sample plots including a much greater number of trees per plot.

Describing Landscape Pattern by Sampling Methods

Ecosystem fragmentation is being recognized as an important characteristic of landscapes, with numerous implications on its ecological and socio-economic valuation, and for the sustainable management of forest and natural resources in general. Particularly in regions where the population pressure on natural resources is heavy, and where the natural and man-influenced ecosystems are highly vulnerable, monitoring of landscape pattern provides valuable insight in and indicators of ecological landscape development. Pattern is a relatively complicated and multi-dimensional characteristic of landscape. Among the constituting agents are number of vegetation classes considered, number, size, shape, and relative and absolute spatial arrangement of the single patches, as is the scale (spatial resolution, level of detail) with which the landscape pattern is analysed. If an analysis shall go beyond a merely narrative and descriptive approach, indexes are frequently employed to quantitatively characterize the landscape pattern. These indexes are calculated from one or more measurable attributes. Usually, those indexes are derived on the basis of complete maps produced from remote sensing imagery and GIS analysis, and assuming that segmentation and classification of the imagery has been made with a high level of accuracy. Relatively little has been published on how to possibly estimate landscape pattern from sample surveys. This paper discusses the potential of sample surveys to produce estimates of some aspects of the fragmentation status of a landscape, where forest and non-forest are used as example classes. Sample based indexes can be derived from different sampling and plot designs, where area and perimeter length are attributes that can most easily be estimated. However, not all attributes interesting for landscape pattern description can be estimated from samples. We present a method that gives a metric, which is related to mean patch size. Sample-based approaches have some interesting properties: some analysis of pattern can be done in the absence of complete maps, and also for historic inventory data, when neither maps nor imagery are available. The proposed analysis of estimating a mean patch size related metric could easily be done with common forest inventory plot designs, where no additional measurements are required. This may make it interesting for “time series” using old forest inventory data.