Non-forested Areas Research Articles

Multiphasic movement and step-selection patterns of dispersed tigers in the central Indian landscape.

Large carnivores play a crucial role in the ecosystem, though their conservation needs a landscape-level approach due to their wide range of habitats and dispersal events. The study of tigers in a human-dominated landscape matrix and their adaptation and adjustment of movement behaviours during the dispersal phase is essential for long-term conservation planning and management policy. We studied the dispersal event of five VHF/GPS collared individuals during 2009-2020. We investigated movement parameters (step length), and the effects of anthropogenic pressures (distance from village), distance from water and vegetation cover, on behavioural phase under a Hidden Markov Model framework. We also tested the effects of distance from village, vegetation cover, and habitat types on animal movement using an integrated Step Selection Function framework. The mean step length (405.44±10.63 m/hr) varied widely by different time of day. Displacement was high during the night (665.28±21.36 m/hr) compared to day (434.16±17.37 m/hr). Tigers moved fast (872.7m; 95% CI 839.1-906.3m) with longer step length and a directional turning angle in non-forested areas (i.e. the human-dominated landscape), likely to avoid conflict with humans. Individuals distinctly exhibited two behavioural states: encamping (~32% of the time) and travelling (~68% of the time). Further, they avoided the human-dominated landscape and mostly remained in and forested areas, especially during nighttime. Our study is the first attempt to understand behavioural transition of dispersal tigers and their habitat selection. Lesser anthropogenic disturbance and high vegetation cover positively influenced the tiger dispersal, while water availability did not affect their state transitional probability. Additionally, dispersers showed high affinity towards forested land during nighttime for travelling.The findings of this study show the importance of functional corridors and stepping stones (mostly encamping areas), and also provide baseline knowledge for integrated landscape management planning and policymaking for the long-term survival of tigers in metapopulation framework.

Estimation of above-ground biomass in dry temperate forests using Sentinel-2 data and random forest: a case study of the Swat area of Pakistan

Accurate mapping of above-ground biomass (AGB) is essential for carbon stock quantification and climate change impact assessment, particularly in mountainous areas. This study applies a random forest (RF) regression model to predict the spatial distribution of AGB in Usho (site A) and Utror (site B) forests located in the northern mountainous region of Pakistan. The predicted maps elucidate AGB variations across these sites, with non-forest areas excluded based on an normalized difference vegetation index (NDVI) threshold value of <0.4. Three different combinations of input datasets were used to predict the biomass, including spectral bands (SBs) only, vegetation indexes (VIs) only, and a combination of both spectral bands and vegetation indexes (SBVIs). Utilizing SBs, the biomass ranged between 150 and 286 mg/ha in site A and 99 and 376 mg/ha in site B. Meanwhile, using VIs indicated a biomass range of 163 Mg/ha–337 Mg/ha and 131–392 Mg/ha for sites A and B, respectively. The combination of spectral bands and vegetation indexes yielded AGB values of 145–290 Mg/ha in site A and 116–389 Mg/ha in site B. The northern and western regions of site A, characterized by higher altitudes and lower forest density, notably showed lower biomass values than other regions. Conversely, similar regions in site B, situated at lower latitudes, demonstrated different biomass ranges. The RF model exhibited robust accuracy, with R2 values of 0.74 and 0.83 for spectral bands and vegetation indexes, respectively. However, with a combination of both, an R2 of 0.79 was achieved. Furthermore, altitudinal gradients significantly influence the biomass distribution across both sites, with specific elevation ranges yielding optimal results. The AGB variation along the slope further corroborated these findings. In both sites, the western aspects showed the highest biomass across all combinations of input datasets. The variable importance analysis highlighted that ARVI8a, NDI45, Band12, Band11, TSAVI8, and ARVI8a are significant predictors in sites A and B. This comprehensive analysis enhances our understanding of AGB distribution in the mountainous forests of Pakistan, offering valuable insights for forest management and ecological studies.

DETER Monitoring on Non-Forest Vegetation in the Brazilian Amazon

In this study, we present the initial results of the pilot project applying the Near Real-Time Deforestation Detection System (DETER), initially designed for monitoring forest ecosystems (DETER Amazon), recently expanded to surveil an area of approximately 280,000 km² of non-forest natural vegetation (NF) in the Brazilian Amazon. The system issues two types of alerts: 1) NF suppression alerts, categorized into three classes (suppression with exposed soil, suppression with cultivated area, and suppression by mining) and 2) burn scars, based on an adapted methodology from DETER Amazon and images with low spatial resolution (64 m) but high temporal resolution (two or three days to assess the entire biome). The pilot project period was from August 2022 to July 2023, with a total area of suppression alerts covering 575.22 km², predominantly composed of the NF suppression class with exposed soil, and 8,036.99 km² of burn scar areas. The majority of NF suppression alerts occurred in the states of Roraima, Mato Grosso, Rondônia, and Pará. Additionally, Amazonas and Amapá stood out among the states with a higher area of burn scar alerts. Savannas were the most affected non-forest physiognomies, with 5,037 km² (6.5%) of their area impacted by suppression and fire. Comparative analysis with other monitoring systems in the Amazon demonstrated the effectiveness of DETER NF in monitoring non-forest areas in the biome.

Analysis of the Severity of Heterogeneity Protection Forest based on SVM and PCA

Forest heterogeneity indicates the forest condition on producing more carbon into environment. Semidang Bukit Kabu Hunting Park Forest is a nature reserve lies over two districts of Central Bengkulu and Seluma, Bengkulu Province, which should have a heterogeneous forest to protect its natural resources. However, the data showed that the condition of it does not appear to have dense forest heterogeneity anymore, and its rate still remain unknown. Remote sensing as one of tools to help the remote monitoring was believed to be helpful to this question. This study showed changes in the heterogeneity from 2016 to 2021. Sentinel-2 imageries were occupied to help the process of classification of forest and non-forest areas. Support Vector Machine, as one of powerful machine learning tools, was also help the process with the integrating of Principal Component Analysis to optimize forest characteristics. This study indicates that there are significant reductions of forest heterogeneity over the area. The number of forest (heterogeny areas) continues to decline from 8122 ha in 2016 to 4339 ha in 2021. Furthermore, this study had proven that the algorithm of support vector machines showed significant performance to build the model towards the data with overall accuracy rate of 0.9434 and a kappa index of 0.9833.

Spatial Analysis of Regional Planning Related to Moratorium Right to Cultivate (Plantation) in Indonesia

Indonesia is a country that is blessed by Allah Swt with a very large expanse of space and territory and is located on the equator surrounded by the sea. This is a natural resource that is very rarely owned by other countries, where the sun shines every year with high humidity. These resources are controlled by the state and used as much as possible for the welfare of the community. One of the related natural resources is plants that can grow well in the area. Oil palm is a plant originating from West Africa that is very suitable for growing in the equatorial area. Currently, Indonesia is the largest producer of palm oil in the world. The greatest utilization of this blessing are for the welfare of the community and based on sustainable Regional Spatial Planning. On its implementation the Regional Spatial Planning has been divided according to its function and sustainability. This condition is contrary to the current government policy, where the government has implemented a moratorium policy on new business use rights for the Plantation sector. The regulation was Presidential Instruction (Inpres) Number 8 of 2018. The purpose of the Moratorium is to control the deforestation of Primary Forests and also improve the Governance of Oil Palm Plantations. In its implementation, there are indeed successes and weaknesses. Successes are mainly in holding back the rate of deforestation or forest encroachment. The weakness is that in terms of Regional Spatial Planning, there should still be many areas that can be used for developing plantation land, especially unproductive and non-forest areas. So that the ideals of improving the welfare of the community can be truly realized.

Monitoring of Land Cover Changes Between 1990 and 2014 over Cyprus using Multi-Temporal Landsat Imagery

Determining changes in forest resources and, consequently, land cover/land use is crucial for sustainable forest planning. This study aims to determine changes in land use classes, including Forest areas, Agricultural areas, Settlement areas, Other non-forest areas, and Water bodies in the study area located in Cyprus between 1990 and 2014. The study utilized digital management plans, a high-resolution base map, and Landsat satellite data for the relevant years. Necessary preprocessing steps were applied to prepare the satellite data for classification. Initially, unsupervised classification was conducted on the images to determine the number of distinguishable sub-information classes. Subsequently, supervised classification was performed using the maximum likelihood algorithm with the provision of training areas. The sub-information classes obtained from the supervised classification were consolidated into five predetermined main information classes. Following the classification process, the accuracy of the classification for each image was determined. Accordingly, the classification accuracy of the 1990 Landsat 5 TM satellite image was found to be 92% (Overall Classification Accuracy: 92%, Overall Kappa Statistics: 0.9000), while the classification accuracy of the 2014 Landsat 8 OLI satellite image was 89.20% (Overall Classification Accuracy: 89.20%, Overall Kappa Statistics: 0.8650). Subsequently, change analysis was conducted using the post-classification comparison method, and changes in land use within the study area over a twenty-four-year period were examined.

Species Richness Increases with Non-Forested Area: Determinants of Butterfly Diversity in Cincinnati, Ohio Urban Parks

Species Richness Increases with Non-Forested Area: Determinants of Butterfly Diversity in Cincinnati, Ohio Urban Parks

Forest canopy cover affects microclimate buffering during an extreme heat event

Increasing temperatures and extreme heat episodes have become more common with climate change. While forests are known to buffer increasing temperatures (relative to non-forested areas), whether this buffering is maintained under extreme temperature events is relatively unknown. Here we assess whether forests continue to buffer microclimate (specifically temperatures) during an extreme heat event: the Pacific Northwest (PNW) heat dome in June 2021. We use a combination of ground-based and regional climate data and find that forest understories were 3 °C cooler than a clear-cut area and 4 °C cooler than regional temperatures during the PNW heat dome. By examining forests with different levels of canopy cover we also found that the buffering capacity of forests is greater under denser canopies even under extreme heat events. Additionally, we found vertical variation in thermal buffering, with the greatest amount of buffering at the surface of the forest floor. Overall, our findings suggest that temperate coastal forests, that are known to buffer average temperatures, can also act as microclimate buffers during extreme heat events like the heat dome that occurred in the PNW in 2021. This could be good news for forest dwelling organisms that are sensitive to such extreme heat events.

The food-water-climate nexus of green infrastructure: Examining ecosystem services trade-offs of peri-urban agriculture

Emission reduction, heat mitigation, and improved access to water and food provision are increasingly critical challenges for urban areas in the context of global climate change adaptation and mitigation. The revival of local agricultural production is often lauded as a potential nature-based solution. However, an expansion of peri-urban agriculture (peri-UA) may entail significant ecosystem trade-offs. This study explores the impacts on the food-water-climate nexus of different scenarios of peri-urban agricultural expansion in a semi-arid, Mediterranean climate, addressing local food provision, freshwater use, local temperature regulation, global climate change mitigation, and the trade-offs thereof. We estimate food provision and irrigation water requirements based on a georeferenced urban metabolism approach along with atmospheric and biosphere models to examine four land-use scenarios in the Metropolitan Area of Barcelona.Our study reveals that a 31 % (+17.27 km2) and 115 % (+64.25 km2) increase in the current peri-UA in the AMB, results in an increase in local food production of 24 % (+16,503 tons year−1) and 86 % (+58,940 tons year−1), and irrigation water requirements by 10.0 % (+3.2 hm3) and 43.5 % (+14.1 hm3), respectively. The expansion of irrigated peri-UA potentially reduces near-surface temperatures by 0.7 °C, albeit temperature reductions in the densest urban areas are minimal. Since the additional peri-UA is achieved by replacing natural non-forested and forest areas, the simulations predict reductions in the net ecosystem productivity of up to 18.5 % and total carbon stocks by 3.3 %. This integrated approach combining urban metabolism and atmospheric modelling to determine the trade-offs appears to be a promising tool for informing land-use decision-making in the context of urban climate adaptation and mitigation.

How does tourist development influence traditional mountain landscapes? A case study from the Western Beskids, Poland

In many mountainous regions, ongoing reforestation leads to the permanent shrinkage of traditional landscapes connected with past human activity. Tourism is often perceived as a measure for the protection of traditional landscapes. Nevertheless, there is still a lack of in-depth studies exploring the connection between mountain glade conservation and tourism development. This paper aimed to determine whether tourism development can prevent the reforestation of temperate mountain glades and to assess the extent of its influence on traditional glade landscapes. Based on the example of the Western Beskids (Poland), we investigated the change in the range of glades in relation to tourist development, which has not been analysed until now. Cartographic analyses were used to examine how the landscape has changed since 1983. To determine the influence of tourist development on the landscape, the tourism landscape footprint (TLF) index was calculated. Two trends were detected: reforestation in glades that are touristically utilized and the gradual tourist development of the remnants of traditional landscapes within preserved non-forested areas. The study reveals that despite tourism, reforestation within glades is continuing, and simultaneously, the share of tourist elements in the shrinking glades is increasing. In the case of areas intensively used for tourism, the traditional landscape is disappearing as it is transformed into a tourist landscape. Further studies are necessary to assess the level of tourism development that will provide traditional landscape conservation in accordance with the principles of sustainable development.

Применение методов пространственного анализа при оценке степени горимости лесов Хабаровского края

Forest fires are the dominant factor in the transformation of natural landscapes in the boreal zone. The area of the Khabarovsk Territory, 93.6 % of which belongs to the forest fund lands, currently has a forest cover of only 66.5 %, which is largely due to forest fires. This makes urgent the problem of reducing the forest fire frequency in the region, including in terms of assessing the actual fire frequency for predicting the risks of landscape fires. The article shows the possibilities of using spatial analysis methods of the Spatial Analyst module of the ArcGIS 10.8 software package to assess the degree of actual forest fire frequency based on calculating the number of fires per 1 million and 250 thousand ha and the area of fires per 1000 ha for a sliding window of a given size. Based on the data from the Remote Monitoring Information System of the Federal Forestry Agency for 2000–2020 and the data on the number of fires from the Fire Information for Resource Management System website for 2000–2021, an assessment of the actual fire frequency for the Khabarovsk Territory has been carried out. The analysis of the results reflects the high spatial and seasonal heterogeneity of the spread of landscape fires in the territory and their proximity to transport and residential structures. Areas with high fire frequency often do not coincide with the boundaries of forestry enterprises, and the maximum indicators are typical for non-forest areas that are not covered by forestry statistics. For different seasons of the year, along with quantitative differences, spatial shifts of firing points and areas with their high density are also observed. Most of the lands in the region (88.3 %) in terms of fire danger belong to the “low” and “below medium” fire frequency categories, which is primarily due to the low degree of development of these areas. The assessment of the degree of fire frequency in the territory on the basis of annual and seasonal maps of the density of firing points, calculated using the sliding window method, makes it possible to fairly objectively and in detail reflect the average long-term risk of fire occurence outside of reference to forestry boundaries. In combination with a map of the distribution of forests by natural fire hazard classes based on pyrological characteristics, the results of the reseаrch can become the basis for calculating the background fire risk, reflecting the risk of fire occurrence and development.

Climate impacts on high-intensity sapling mortality: Comparative study and future warnings for Larix kaempferi, Abies sachalinensis, and Picea glehnii

Climate change profoundly affects plant viability and forest dynamics, creating uncertainty in future forest management. Post-planting, high-intensity sapling mortality may require costly replanting, disrupting plans, and risking forest management goals. Therefore, this study focused on high-intensity sapling mortality (mortality rates exceeding 30 %) in Larix kaempferi, Abies sachalinensis, and Picea glehnii planted in northern Japan attributed to summer drought. Utilizing a dataset spanning approximately three decades, the correlation between high-intensity mortality occurrence and climatic stress was explored employing a Bayesian approach. Additionally, the probability of high-intensity mortality occurrence by the end of this century was predicted based on two representative climate scenarios (RCP2.6 and RCP8.5). The predictive accuracy of high-intensity mortality occurrence probabilities exceeded 70 % after comprehensive consideration of climatic stress factors, thereby accentuating the robustness of the proposed model. These encompass the antecedent precipitation index (API), which serves as a soil drought indicator; 30-day mean daily maximum temperature (MaxTMP̅); 30-day mean daily total solar radiation (TSR̅); and day of year (DOY). All sapling species manifested a pronounced negative correlation between API and the probability of high-intensity mortality occurrence, whereas TSR̅ exhibited a positive impact on this probability. Additionally, MaxTMP̅ positively influenced this probability for L. kaempferi and A. sachalinensis. Delving into interspecific differentials, API exhibited the most pronounced impact on P. glehnii in comparison to the other two tree species, whereas TSR̅ exerted a substantial influence on A. sachalinensis and P. glehnii. The susceptibility to MaxTMP̅ was hierarchically ordered as L. kaempferi >A. sachalinensis >P. glehnii. DOY, used to assess phenological effects like leaf morphology, negatively affected the probability of high-intensity mortality occurrence exclusively for L. kaempferi. The median probability of high-intensity mortality occurrence in Hokkaido, calculated from the estimated probabilities across all regions of Hokkaido (encompassing non-forested areas but excluding remote islands), at a 1 km resolution, was 0.37 for L. kaempferi, 0.57 for A. sachalinensis, and 0.41 for P. glehnii under RCP2.6 by the century’s end. These probabilities have decreased in comparison to those observed in 2018–2022. In stark contrast, under RCP8.5, the probabilities soared to 0.91, 0.87, and 0.56, representing an increase of 2.12 times for L. kaempferi, 1.36 times for A. sachalinensis, and 1.14 times for P. glehnii in comparison to 2018–2022. Even the least affected species, P. glehnii, witnessed a increase in mortality probability. This study underscores the importance of forest management and adaptation to climate change.

Exploring the Dynamic Invasion Pattern of the Black-Headed Fall Webworm in China: Susceptibility to Topography, Vegetation, and Human Activities.

The fall webworm (FWW), H. cunea (Drury) (Lepidoptera: Erebidae: Arctiidae), is an extremely high-risk globally invasive pest. Understanding the invasion dynamics of invasive pests and identifying the critical factors that promote their spread is essential for devising practical and efficient strategies for their control and management. The invasion dynamics of the FWW and its influencing factors were analyzed using standard deviation ellipse and spatial autocorrelation methods. The analysis was based on statistical data on the occurrence of the FWW in China. The dissemination pattern of the FWW between 1979 and 2022 followed a sequence of "invasion-occurrence-transmission-outbreak", spreading progressively from coastal to inland regions. Furthermore, areas with high nighttime light values, abundant ports, and non-forested areas with low vegetation cover at altitudes below 500 m were more likely to be inhabited by the black-headed FWW. The dynamic invasion pattern and the driving factors associated with the fall webworm (FWW) provide critical insights for future FWW management strategies. These strategies serve not only to regulate the dissemination of insects and diminish migratory tendencies but also to guarantee the implementation of efficient early detection systems and prompt response measures.

Monitoring Forest Dynamics and Conducting Restoration Assessment Using Multi-Source Earth Observation Data in Northern Andes, Colombia

Examining the efficacy of current assessment methodologies for forest conservation and restoration initiatives to align with global and national agendas to combat deforestation and facilitate restoration efforts is necessary to identify efficient and robust approaches. The objective of this study is to understand forest dynamics (1996–2021) and assess restoration implementations at the Urra’s supplying basin hydroelectric reservoir in Colombia. The processing approach integrates optical and radar Earth Observation (EO) data from Sentinel-2 and Landsat for forest mapping and multi-temporal forest change assessment (1996–2021), and a Sentinel-1 backscatter time-series analysis is conducted to assess the state of forest restoration implementations. The processing chain was scaled in a cloud-based environment using the Nebari and SEPPO software and the Python language. The results demonstrate an overall substantial decrease in forested areas in the 1996–2000 period (37,763 ha). An accuracy assessment of multi-temporal forest change maps showed a high precision in detecting deforestation events, while improvements are necessary for accurately representing non-forested areas. The forest restoration assessment suggests that the majority of the 270 evaluated plots are in the intermediate growth state (82.96%) compared to the reference data. This study underscores the need for robust and continuous monitoring systems that integrate ground truth data with EO techniques for enhanced accuracy and effectiveness in forest restoration and conservation endeavors.

Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects

AbstractThis article is devoted to the problem of detection of forest and nonforest areas on Earth images. We propose two statistical methods to tackle this problem: one based on multiple hypothesis testing with parametric distribution families, another one—on nonparametric tests. The parametric approach is novel in the literature and relevant to a larger class of problems—detection of natural objects, as well as anomaly detection. We develop mathematical background for each of the two methods, build self‐sufficient detection algorithms using them and discuss practical aspects of their implementation. We also compare our algorithms with each other and with those from standard machine learning using satellite data.

What are the key factors influencing the dynamics of forest edge? An application of a new forest edge index in the southeast of China

Recent reforestation programs have led to significant increases in forest cover in China; however, they have not been able to halt the process of forest fragmentation. The dynamics of forest fragmentation are still unknown, especially the transitions among forest core (FC), forest edge (FE), and non-forest areas. Taking Fujian Province in southeast China as a case, a new FE index (FEI) was proposed to identify FE, FC, forest patch (FP), matrix edge (ME), matrix core (MC), and forest perforation (FPe) as well. Then the transitions among FC, FE, and non-forest areas was explored; a series of logistic regression models were developed to examine the internal and external, static and dynamic driving patterns of the FE dynamics. The results revealed that the FEI outperforms conventional landscape metrics in identifying FE areas. During the period from 2000 to 2020, FE constituted approximately 41% of the study area. Frequent transition dynamics were observed between FE and other landscape types, accounting for 22% and 19% of the study area during 2000–2010 and 2010–2020, respectively. The internal driving models demonstrated that various landscape types exerted a negative influence on FE expansion. Among these, FPe emerged as a dominant factor, followed by MC, FC, and ME. The external models highlighted the significant influence of elevation, average annual temperature, distance from road, and slope on the mutual transition between FC and FE, while aspect and GDP influenced greatly on the transition from matrix to FE. These findings deepen the understanding of forest fragmentation dynamics and provide valuable insights for forest management.

Reconstructing of historical land cover based on contemporary cartographical materials

Historical land use reconstructions help to assess climate change, interactions between ecosystems and human and strengthen the knowledge about these interactions. They are conducted on the basis of historical maps that only cover certain areas. Hence, there is a need to seek other maps enabling historical land use to be reconstructed. The aim of study is to assess the suitability of contemporary maps for reconstructing a historical non-forest area range in the Western Carpathians. The content of contemporary maps was compared with that of historical maps, including the number of glades, their area and the length of their boundaries. Over 60% of glades were reconstructed, which is 90% of the glade area inventoried based on historical maps. Over 50% glades with an area of less than 1ha were not reconstructed. However, the area they occupy does not account for 2% of the total glade area. Thus, it has no significant impact on the total area of the reconstructed glades. As much as 90% of the boundaries' length was reconstructed. The study shows that contemporary maps may be useful in reconstructing the forest and glades range and may be an additional source of information to verify the content of historical maps.

The Role of the United Nations Environment Program (UNEP) In Overcoming Deforestation In Central Kalimantan 2017-2020

Climate change is a global environmental problem, one of the causes of which is deforestation. As the second largest province in Indonesia, with forest area reaching 50% of the total area, Central Kalimantan has a vital role in environmental problems and deforestation. Government policies that convert forests into non-forest areas increase the rate of deforestation and increase global emissions. Deforestation is a worldwide problem that requires joint attention in handling, especially for the United Nations Environment Program (UNEP) as the international environmental regime. This research is a type of qualitative research that uses analytical descriptive methods to describe the phenomenon of deforestation in Central Kalimantan and UNEP’s role in overcoming it. Library study techniques are used to collect research data in documents, which are compiled, analyzed, and concluded. UNEP’s role in realizing SDG 13 is examined using an international regime approach through global diplomacy. The research results show that UNEP, as an international regime, plays a vital role in making global regulations regarding handling climate change from the deforestation sector through REDD+. In its implementation, UNEP assisted Indonesia in implementing REDD+ in Central Kalimantan and acted as a catalyst, facilitator, advocate, and educator on the issue of deforestation in Central Kalimantan in 2017-2020. This research is essential to show the urgency of the multi-stakeholder role in global diplomacy in dealing with climate change.

Utilizing Multi-Temporal ALOS/PALSAR Backscatter Data to Detect Deforestation in the Indonesian Tropical Forest

Deforestation detection mapping has been analyzed using Synthetic Aperture Radar (SAR) data from the ALOS PALSAR satellite. SAR is a promising tool for deforestation mapping in tropical forest regions because microwave energy can penetrate clouds. This study examined the effect of SAR polarization on the accuracy of deforestation mapping and the seasonal variation of radar backscattering values in Riau Province, Indonesia. Image differencing was used to determine the threshold value for distinguishing between forest and non-forest areas. The difference between HV (horizontal transmit-vertical receive) and HH (horizontal transmit-horizontal receive) polarization was used as the discriminant variable. The correlation between radar backscattering value and accumulated rainfall was also analyzed to assess the effect of seasonal change. The findings demonstrated that the accuracy of deforestation detection mapping using HV polarization outperforms that achieved with HH polarization. Moreover, seasonal variations in rainfall were observed to influence radar backscatter signatures in bare soil, acacia, and oil palm, though no such influence was detected in forested areas. The accuracy of deforestation detection mapping, employing the thresholding method, reached a rate of 92.21%. During the period from November 2007 to July 2009, forested areas in Riau experienced a reduction, decreasing to 6.77%, and further decreasing to 6.08% by October 2010.

Conserving large mammals on small islands: A case study on one of the world’s most understudied pigs, the Togean islands babirusa

Conserving large mammals on small islands poses a great challenge, given their high resource demand within the limited space available. The endangered Togean Islands babirusa (Babyrousa togeanensis) is one of these species, with a distribution range limited to four small islands in the Togean Archipelago, Indonesia. Despite being listed as endangered, very little information is available on the distribution and ecology of this species. To address this critical knowledge gap, we here report the first field-based ecological study of the Togean Islands babirusa across its entire distribution range. Following a stratified random sampling procedure, we distributed camera traps at 103 stations across four islands to collect data on the species distribution from July-October 2022. We performed an occupancy modeling analysis to assess the species’ habitat use, with various habitat features estimated through remote sensing and field measurements as covariates. We found that forest and mangrove availability over a large area positively influenced babirusa habitat selection. Babirusas only made use of agricultural areas when large forest areas were available nearby. Our results highlight the benefits of redesigning the national park area to accommodate babirusa habitat requirements, specifically by reassigning the non-forested park areas (about 30% of the park area) to non-protected forests currently outside the park boundary (about 50% of total forested area). Our case study exemplifies key challenges associated with conserving large mammals on small islands and highlights the importance of following an adaptive management approach, which in this case implies shifting 30% of the current protected area.