Geostatistical Tools to Assess Existing Monitoring Network of Forest Soils in a Mountainous National Park

Abstract

Environmental changes in national parks are generally subject to constant observation. A particular case is parks located in mountains, which are more vulnerable to climate change and the binding of pollutants in mountain ranges as orographic barriers. The effectiveness of forest soil monitoring networks based on a systematic grid with a predetermined density has not been analysed so far. This study’s analysis was conducted in the Stolowe Mountains National Park (SMNP), SW Poland, using total Pb concentration data obtained from an initial network of 403 circle plots with centroids arranged in a regular 400 × 400 m square grid. The number and distribution of monitoring plots were analysed using geostatistical tools in terms of the accuracy and correctness of soil parameters obtained from spatial distribution imaging. The analysis also aimed at reducing the number of monitoring plots taking into account the economic and logistic aspects of the monitoring investigations in order to improve sampling efficiency in subsequent studies in the SMNP. The concept of the evaluation and modification of the monitoring network presented in this paper is an original solution and included first the reduction and then the extension of plot numbers. Two variants of reduced monitoring networks, constructed using the proposed procedure, allowed us to develop the correct geostatistical models, which were characterised by a slightly worse mean standardised error (MSE) and root mean squared error (RMSE) compared to errors from the original, regular monitoring network. Based on the new geostatistical models, the prediction of Pb concentration in soils in the reduced grids changed the spatial proportions of areas in different pollution classes to a limited extent compared to the original network.

Geospatial applications as an integral component of wildlife monitoring in the Chor-nobyl Radiation and Ecological Biosphere Reserve

“…Through the integration of geospatial data and traditional methods of study, scientists have achieved a more profound comprehension of wildlife populations, their habitats, and the complex interrelationships between species and their environment (Rossi et al, 1992;Suhardiman et al, 2013;Tsvetkova et al, 2015;Lord et al, 2018). Currently, Geographic Information System (GIS) technologies, which have been recently integrated with remote sensing, are utilized in almost all environmental surveys and monitoring programs (Jezierski & Kabala, 2021;Skydan et al, 2022). Nowadays, geospatial technology is being used for wildlife census and monitoring (Palacio et al, 2021;Wild et al, 2023), detecting drivers and hotspots for disease transmission risk among wildlife (Barroso et al, 2023), wildlife crime investigation (Appleton et al, 2022), and evaluating the effectiveness of managing protected areas (Mileti et al, 2024).…”