The effect of protected areas on forest disturbance in the Carpathian Mountains 1985-2010

The synergy between protected area effectiveness and economic growth

Protected areas conserve biodiversity and ecosystem functions but might impede local economic growth. Understanding the global patterns and predictors of different relationships between protected area effectiveness and neighboring community economic growth can inform better implementation of the Kunming-Montreal Global Biodiversity Framework. We assessed 10,143 protected areas globally with matched samples to address the non-random location of protected areas. Our results show that protected areas resist human-induced land cover changes and do not limit nightlight increases in neighboring settlements. This result is robust, using different matching techniques, parameter settings, and selection of covariates. We identify four types of relationships between land cover changes and nightlight changes for each protected area: "synergy," "retreat," and two tradeoff relationships. About half of the protected areas (47.5%) retain their natural land cover and do so despite an increase of nightlights in the neighboring communities. This synergy relationship is the most common globally but varies between biomes and continents. Synergy is less frequent in the Amazon, Southeast Asia, and some developing areas, where most biodiversity resides and which suffer more from poverty. Smaller protected areas and those with better access to cities, moderate road density, and better baseline economic conditions have a higher probability of reaching synergy. Our results are promising, as the expansion of protected areas and increased species protection will rely more on conserving the human-modified landscape with smaller protected areas. Future interventions should address local development and biodiversity conservation together to achieve more co-benefits.

The role of nature reserves in conservation effectiveness of ecosystem services in China

Establishing nature reserves (NRs) is a common method to avoid biodiversity loss and degradation of ecosystem services (ESs). The evaluation of ESs in NRs and the exploration of associated influencing factors are the basis for improving ESs and management. However, the ES effectiveness of NRs over time remains questionable, namely due to the heterogeneity of landscape characteristics inside and outside of NRs. This study (i) quantifies the role of 75 NRs in China in maintaining ESs (i.e., net primary production (NPP), soil conservation, sandstorm prevention and water yield) from 2000 to 2020, (ii) reveals the trade-offs/synergies, and (iii) identifies the main influencing factors of the ES effectiveness of NRs. The results show that more than 80% of NRs had positive ES effectiveness, which was greater in older NRs. For different ESs, effectiveness over time increases for NPP (E_NPP), soil conservation (E_SC) and sandstorm prevention (E_SP) but declines for water yield (E_WY). There is a clear synergistic relationship between E_NPP and E_SC. Moreover, the effectiveness of ESs is closely correlated with elevation, precipitation, and perimeter area ratio. Our findings can provide important information to support site selection and management of reserves to improve the delivery of critical ecosystem services.

The Winding Road towards Sustainable Forest Management in Romania, 1989–2022: A Case Study of Post-Communist Social–Ecological Transition

Forest ecosystems are a prime example of the heated debates that have arisen around how forests should be managed, and what services and benefits they should deliver. The European transitions in governance to and from communist regimes have had significant impacts on forests and their management. Unstable legislative and institutional changes prior to, during, and after a communist regime, combined with unique remnant areas of high-conservation-value forests, make Romania an ideal case study to explore the social–ecological transitions of forest landscapes. The aim of this paper is two-fold. First, we present the origins of, the evolution of, and the current state of forest management and ownership in Romania during transitions between the pre-communist (-1945), communist (1945–1989), and EU periods (2007-). Second, we focus on the enablers and barriers in Romania towards sustainable forest management as defined by pan-European forest policies. We used a semi-systematic, five-step scientific literature review on forest ownership, governance, and management in Romania. The analysis shows that both enablers (e.g., forest certification) and barriers (e.g., redundancy and the questionable effectiveness of the network of protected areas; illegal, unsustainable, and unreported logging; loopholes in the legislative framework) have contributed to the current approaches to interpreting forests, forestry, and forest management. The installation of the communist regime translated into sustained wood yield forest management under singular forest ownership, which opposed the previous system and forest ownership pluralism. In the post-communist period, forestland restitution led to significant legislative changes, but forest management must still confront remnant elements of the communist approach. Both communist and post-communist policies related to forests have shaped the evolution of forest landscape management in Romania, thus stressing the need to learn from the past towards securing sustainable forest management into the future. These lessons provide insights on both positive and negative drivers of forest management, which can contribute to smooth future transition towards more sustainable forest management practices.

Using historical spy satellite photographs and recent remote sensing data to identify high-conservation-value forests

High-conservation-value forests (HCVFs) are critically important for biodiversity and ecosystem service provisioning, but they face many threats. Where systematic HCVF inventories are missing, such as in parts of Eastern Europe, these forests remain largely unacknowledged and therefore often unprotected. We devised a novel, transferable approach for detecting HCVFs based on integrating historical spy satellite images, contemporary remote sensing data (Landsat), and information on current potential anthropogenic pressures (e.g., road infrastructure, population density, demand for fire wood, terrain). We applied the method to the Romanian Carpathians, for which we mapped forest continuity (1955-2019), canopy structural complexity, and anthropogenic pressures. We identified 738,000 ha of HCVF. More than half of this area was identified as susceptible to current anthropogenic pressures and lacked formal protection. By providing a framework for broad-scale HCVF monitoring, our approach facilitates integration of HCVF into forest conservation and management. This is urgently needed to achieve the goals of the European Union's Biodiversity Strategy to maintain valuable forest ecosystems.

Strictly protected areas are not necessarily more effective than areas in which multiple human uses are permitted

The International Union for Conservation of Nature (IUCN) classifies protected areas into six categories, ranging from strict nature reserves to areas where multiple human uses are permitted. In the past, many researchers have questioned the effectiveness of multiple-use areas, fueling an unresolved debate regarding their conservation value. The literature so far has been inconclusive: although several studies have found that strictly protected areas are more effective, others have found the opposite, and yet others that the two types do not differ. To help resolve this debate, we reviewed the literature on protected areas and conducted our own analysis using > 19 000 terrestrial protected areas worldwide. We found that the differences between strictly protected areas and areas in which multiple human uses are permitted are often small and not statistically significant. Although the effectiveness of protected areas worldwide varies, other factors, besides their assigned IUCN category, are likely to be driving this pattern.

Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation

In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q2–Q3: 0.63–0.71; NDMI Q2–Q3: 0.10–0.19) and the TCW index had negative values (Q2–Q3: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.

Old-Growth Forest Disturbance in the Ukrainian Carpathians

Human activity has greatly reduced the area of old-growth forest in Europe, with some of the largest remaining fragments in the Carpathian Mountains of south-western Ukraine. We used satellite image analysis to calculate old-growth forest disturbance in this region from 2010 to 2019. Over this period, we identified 1335 ha of disturbance in old-growth forest, equivalent to 1.8% of old-growth forest in the region. During 2015 to 2019, the average annual disturbance rate was 0.34%, varying with altitude, distance to settlements and location within the region. Disturbance rates were 7–8 times lower in protected areas compared to outside of protected areas. Only one third of old-growth forest is currently within protected areas; expansion of the protected area system to include more old-growth forests would reduce future loss. A 2017 law that gave protection to all old-growth forest in Ukraine had no significant impact on disturbance rates in 2018, but in 2019 disturbance rates reduced to 0.19%. Our analysis is the first indication that this new legislation may be reducing loss of old-growth forest in Ukraine.

A Comparison of WorldView-2 and Landsat 8 Images for the Classification of Forests Affected by Bark Beetle Outbreaks Using a Support Vector Machine and a Neural Network: A Case Study in the Sumava Mountains

The objective of this paper is to assess WorldView-2 (WV2) and Landsat OLI (L8) images in the detection of bark beetle outbreaks in the Sumava National Park. WV2 and L8 images were used for the classification of forests infected by bark beetle outbreaks using a Support Vector Machine (SVM) and a Neural Network (NN). After evaluating all the available results, the SVM can be considered the best method used in this study. This classifier achieved the highest overall accuracy and Kappa index for both classified images. In the cases of WV2 and L8, total overall accuracies of 86% and 71% and Kappa indices of 0.84 and 0.66 were achieved with SVM, respectively. The NN algorithm using WV2 also produced very promising results, with over 80% overall accuracy and a Kappa index of 0.79. The methods used in this study may be inspirational for testing other types of satellite data (e.g., Sentinel-2) or other classification algorithms such as the Random Forest Classifier.