1,720,971 research outputs found
A design-based approach for mapping the diversity of forest attributes
No full textForest attributes such as volume or basal area are concentrated at tree locations and are
absent elsewhere. Therefore, it is more meaningful to consider the amount of forest attributes at a
pre-fixed spatial grain, within regular plots of pre-fixed size centered at the points of the study area.
In this way, also the diversity of attributes within plots can be considered and quantified by suitable
indexes, giving rise to a diversity surface defined on the continuum of points constituting the area.
We analyze the estimation of diversity surfaces when a sample of plots is selected by a probabilistic
sampling scheme and diversity within non-sampled plots is estimated using an inverse distance
weighting interpolator. We discuss the design-based asymptotic properties of the resulting maps
when the survey area remains fixed and the number of sampled points increases.
Because diversity surfaces share suitable mathematical properties, if the schemes adopted to select
sample points ensure an even coverage of the study areas avoiding large portions of non- sampled
zones, it can be proven that the estimated maps approach the true maps
Model-Assisted Procedure for Spatially Explicit Maps with an Application to Wild Boar Rooting Impact
No full textSpatially explicit mapping depicting the distribution of an interest attribute is approached in a model-assisted framework using the inverse distance weighting (IDW) interpolator. The procedure also allows for the inclusion of wall-to-wall auxiliary variables. Moreover, a pseudo-population bootstrap procedure to estimate the precision of the resulting map is adopted. This methodology is applied to the Acquerino-Cantagallo Nature Reserve in central Italy to produce the wild boar rooting impact map and the corresponding precision map
Design-based mapping of land use/land cover classes with bootstrap estimation of precision by nearest-neighbour interpolation
Full text linkLand use/land cover mapping is usually performed by classifying satellite imagery (e.g., Landsat, Sentinel) for the whole survey region using classification algorithms implemented with training data. Subsequently, probabilistic samples are usually implemented with the main purpose of assessing the accuracy of these maps by comparing the map class and the ground condition determined for the sampled units. The main proposal of this paper is to directly exploit these probabilistic samples to estimate the land use/land cover class at any location of the survey region in a design-based framework by the well-known nearest-neighbour interpolator. For the first time, the design-based consistency of nearest-neighbour maps (i.e., categorical variables) is theoretically proven and a pseudo-population bootstrap estimator of their precision is proposed and discussed. These nearest-neighbour maps provide the ability to place mapping within a rigorous design-based inference framework, in contrast to most traditional mapping approaches which often are implemented with no inferential basis or by necessity (due to lack of a probabilistic sample) model-based inference. A simulation study is performed on an estimated land use map in Southern Tuscany (Italy)—taken as the true map—to check the finite-sample performance of the proposal as well as the matching of the area coverage estimates arising from the map with those achieved by traditional estimators. The Italian land use map arising from the IUTI surveys and the U.S. land cover map arising from the LCMAP program are considered as case studies. © Institute of Mathematical Statistics, 2023
Joining the incompatible: exploiting purposive lists for the sample-based estimation of species richness
Full text linkThe lists of species obtained by purposive sampling by eld ecol-
ogists can be used to improve the sample-based estimation of species
richness. A new estimator is here proposed as a modication of the
dierence estimator in which the species inclusion probabilities are
estimated by means of the species frequencies from incidence data.
If the species list used to support the estimation is complete the
estimator guesses the true richness without error. In the case of in-
complete lists, the estimator provides values invariably greater than
the number of species detected by the combination of sample-based
and purposive surveys. An asymptotically conservative estimator of
the mean squared error is also provided. A simulation study based
on two articial communities is carried out in order to check the ob-
vious increase in accuracy and precision with respect to the widely
applied estimators based on the sole sample information. Finally, the
proposed estimator is adopted to estimate species richness in the
Maremma Regional Park, Italy
Estimation of plant species richness exploiting probabilistic sampling and purposive lists: Empirical evidence and practical proposal for forest inventories
No full textForest surveys, especially national forest inventories, are evolving towards multipurpose resource strategies, expanding their scope in several directions including biodiversity assessment. This article focuses on the estimation of plant species richness that constitutes one of the most relevant biodiversity indicators in forest ecosystems. In forest inventories, surveys are usually performed by locating plots in the forest area of interest according to a probabilistic scheme, so that the estimation of plant species richness can be approached from a probabilistic perspective by (i) recording a matrix of species presence/absence called incidence data, and then (ii) adopting a class of widely used, automated estimators called nonparametric estimators. The purpose of this article is to raise awareness within the forestry community of the recent findings of Di Biase et al. (2025) who show, both theoretically and through simulations, the inadequacy of nonparametric estimators affected by massive negative bias due to the difficulty of sampling rare species, at the same time highlighting the appeal of a data integration approach that consists in exploiting lists of rare species compiled by purposive surveys to improve the sample-based estimates. A case study performed in the Nature Reserve of Poggio all'Olmo (Central Italy) is considered to confirm the failure of nonparametric estimators and the suitability of the data integration in estimating plant species richness. This approach paves the way for the practical integration of species richness assessment into forest inventories, thus meeting the technical requests to support modern multipurpose forestry
A probabilistic sampling strategy for estimating plant density in Posidonia oceanica meadows
Full text linkMarine and coastal ecosystems, such as seagrasses, mangroves, and coral reefs, provide a range of essential provisioning, regulating and cultural ecosystem services. Recent United Nations guidelines on ecosystem accounting (SEEA EA) emphasise the need for biophysical data as the foundation for compiling ecosystem accounts and conducting economic evaluations for developing indicators and informing policies and interventions. However, data availability on marine ecosystems is limited with respect to terrestrial ones. Moreover, the collection of biophysical data on marine ecosystem extent and condition required for ecosystem accounting (EA) is often not aligned with existing habitat monitoring strategies. This study aims to address the scarcity of spatial data on marine ecosystems and facilitate the integration of current monitoring strategies with the scope of EA. We propose the application of design-based inference for the estimation, mapping, and monitoring of key ecological attributes of marine ecosystems. We focus on the habitat of Posidonia oceanica, an endemic seagrass of the Mediterranean Sea, but the proposed strategy is adaptable to other ecosystems. The benefits of appropriate probabilistic sampling schemes for assessing P. oceanica are explored via simulation testing. The performance of different sample schemes in artificial populations reveals that reliable estimates of density (as well as their precision) can be obtained even with low sample sizes. The empirical viability of our methodology is exemplified using data collected on a meadow located in an Italian Marine Protected Area (Puglia region, Southern Italy)
Per-Pixel Forest Attribute Mapping and Error Estimation: The Google Earth Engine and R dataDriven Tool
Full text linkRemote sensing products are typically assessed using a single accuracy estimate for the entire map, despite significant variations in accuracy across different map areas or classes. Estimating per-pixel uncertainty is a major challenge for enhancing the usability and potential of remote sensing products. This paper introduces the dataDriven open access tool, a novel statistical design-based approach that specifically addresses this issue by estimating per-pixel uncertainty through a bootstrap resampling procedure. Leveraging Sentinel-2 remote sensing data as auxiliary information, the capabilities of the Google Earth Engine cloud computing platform, and the R programming language, dataDriven can be applied in any world region and variables of interest. In this study, the dataDriven tool was tested in the Rincine forest estate study area-eastern Tuscany, Italy-focusing on volume density as the variable of interest. The average volume density was 0.042, corresponding to 420 m3 per hectare. The estimated pixel errors ranged between 93 m3 and 979 m3 per hectare and were 285 m3 per hectare on average. The ability to produce error estimates for each pixel in the map is a novel aspect in the context of the current advances in remote sensing and forest monitoring and assessment. It constitutes a significant support in forest management applications and also a powerful communication tool since it informs users about areas where map estimates are unreliable, at the same time highlighting the areas where the information provided via the map is more trustworthy. In light of this, the dataDriven tool aims to support researchers and practitioners in the spatially exhaustive use of remote sensing-derived products and map validation
Rooting as indicator of wild boar density: environmental drivers and spatial variation across protected areas
Full text linkWild ungulates play crucial roles in food webs and their impacts can propagate across trophic levels. The recent spread of wild boar Sus scrofa is generating concerns worldwide for its potential negative impacts on biodiversity and human activities. Under particular conditions, its foraging activity through digging the topsoil (i.e., rooting) may affect endangered plant/animal species or agriculture. Identifying environmental drivers of rooting is crucial to address spatially-explicit measures to reduce negative impacts, as well as to clarify the often unclear link between rooting and wild boar density. We performed six-year intensive spring-summer surveys (763 sampling plots; 3343 surveys between 2019–2024) in 9 protected areas of central Italy encompassing heterogeneous habitats and a gradient in wild boar densities, to investigate spatial variations in wild boar rooting and relevant drivers. Strong support was obtained for a positive relationship between rooting, at both large (i.e., study area) and fine (i.e., sampling plot) scales, and wild boar density in the area. Results supported lower rooting with increasing landscape diversity. Rooting was affected by topography, being reduced by rock cover and terrain steepness, and increased with distance from the nearest road/railway. Models of fine-scale rooting variation were elaborated to develop high-resolution (10 × 10 m) predictive maps of rooting impact, providing a tool scalable to ecologically comparable areas. Findings support the control of wild boar population density and maintenance of high landscape diversity as measures to reduce wild boar impacts. Finally, the present study also supports that rooting can serve as an effective indicator of within- and between-area variations in wild boar density
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