Enriching forest resource inventory is important to ensure the sustainable management of forest ecosystems. Obtaining forest inventory data from the field has always been difficult, laborious, time consuming, and expensive. Advances in integrating photogrammetry and computer vision have helped researchers develop some numeric algorithms and methods that can turn 2D (images) into 3D (point clouds) and are highly applicable to forestry. This paper aimed to develop a new, highly accurate methodology that extracts sample plot parameters based on continuous terrestrial photogrammetry. For this purpose, we designed and implemented a terrestrial observation instrument combining real-time kinematic (RTK) and charge-coupled device (CCD) continuous photography. Then, according to the set observation plan, three independent experimental plots were continuously photographed and the 3D point cloud of the plot was generated. From this 3D point cloud, the tree position coordinates, tree DBHs, tree heights, and other plot characteristics of the forest were extracted. The plot characteristics obtained from the 3D point cloud were compared with the measurement data obtained from the field to check the accuracy of our methodology. We obtained the position coordinates of the trees with the positioning accuracy (RMSE) of 0.162 m to 0.201 m. The relative root mean square error (rRMSE) of the trunk diameter measurements was 3.07% to 4.51%, which met the accuracy requirements of traditional forestry surveys. The hypsometrical measurements were due to the occlusion of the forest canopy and the estimated rRMSE was 11.26% to 11.91%, which is still good reference data. Furthermore, these image-based point cloud data also have portable observation instruments, low data collection costs, high field measurement efficiency, automatic data processing, and they can directly extract tree geographic location information, which may be interesting and important for certain applications such as the protection of registered famous trees. For forest inventory, continuous terrestrial photogrammetry with its unique advantages is a solution that deserves future attention in the field of tree detection and ecological construction.

Habitat degradation and species range contraction due to land use/land cover changes (LULCC) is a major threat to global biodiversity. The ever-growing human population has trespassed deep into the natural habitat of many species via the expansion of agricultural lands and infrastructural development. Carnivore species are particularly at risk, as they demand conserved and well-connected habitat with minimum to no anthropogenic disturbance. In Pakistan, the snow leopard (Panthera uncia) is found in three mountain ranges—the Himalayas, Hindukush, and Karakoram. Despite this being one of the harshest environments on the planet, a large population of humans reside here and exploit surrounding natural resources to meet their needs. Keeping in view this exponentially growing population and its potential impacts on at-risk species like the snow leopard, we used geographic information systems (GIS) and remote sensing with the aim of identifying and quantifying LULCC across snow leopard range in Pakistan for the years 2000, 2010, and 2020. A massive expansion of 1804.13 km² (163%) was observed in the built-up area during the study period. Similarly, an increase of 3177.74 km² (153%) was observed in agricultural land. Barren mountain land increased by 12,368.39 km² (28%) while forest land decreased by 2478.43 km² (28%) and area with snow cover decreased by 14,799.83 km² (52%). Drivers of these large-scale changes are likely the expanding human population and climate change. The overall quality and quantity of snow leopard habitat in Pakistan has drastically changed in the last 20 years and could be compromised. Swift and direct conservation actions to monitor LULCC are recommended to reduce any associated negative impacts on species preservation efforts. In the future, a series of extensive field surveys and studies should be carried out to monitor key drivers of LULCC across the observed area.

The unchecked and unplanned expansion of urban areas has led to the conversion of millions of green areas to gray areas. The recent urban growth patterns of Pakistan’s metropolitan twin cities, Islamabad and Rawalpindi, is a matter of concern for the surrounding green areas. The present study aimed to categorize and quantify the land-use and land-cover change (LULCC) patterns and the corresponding impacts on the forest carbon dynamics around Islamabad and Rawalpindi. Multispectral satellite images for the year 1990 (Landsat 5 TM) and 2020 (Landsat 8 OLI) were used to determine, quantify, and compare the LULCC inside and around the twin metropolitan cities. Field inventory surveys in the reserved forests of Rawalpindi and Islamabad were also conducted to determine the amount of stored carbon in these forests. Our results showed an accelerated annual urban expansion (i.e., an increase in the built-up area) of 16.49% and 26.72% in Rawalpindi and Islamabad, respectively, during the study period. Similarly, the amount of barren land and agricultural land was reduced at an annual rate of 2.08% and 2.18%, respectively, in Rawalpindi and 0.25% and 1.04% in Islamabad. A reduction in the area of barren mountains also occurred at an annual of 2.26% in Islamabad, while it increased by 4.16% in Rawalpindi. The amount of carbon stored in the reserved forests of Islamabad stood at 139.17 ± 12.15 Mg C/ha while that of Rawalpindi was 110.4 ± 13.79 Mg C/ha. In addition, total stored forest carbon was found to have decreased from 544.70 Gg C to 218.05 Gg C in Rawalpindi, while in Islamabad it increased from 2779.64 Gg C to 3548.16 Gg C. Investment in ecological urban planning, sustainable cities, and appropriate land-use planning is recommended to curb the degradation and conversion of the surrounding green areas of Rawalpindi and Islamabad.

Establishing the tree volume table is an important aspect of forest inventory for managing the forest ecosystem. The traditional volume models used to build tree volume tables are time consuming and expensive, demanding huge labor and material resources. Aiming at the improvement of the current destructive, costly and time-consuming volume model, we propose a new non-destructive, low-cost and efficient method for calculating the tree volume model with high precision by using the electronic theodolite. For testing and comparing the accuracy of our model with the traditional model, we collected data of three main tree taxa including Platycladus orientalis (L.) Franco, Larix principis-rupprechtii Mayr and Populus spp. L. from different districts and counties of Beijing, China. We collected a total of 1750 tree samples (250 Platycladus orientalis, 300 Larix principis-rupprechtii, and 1200 Populus spp.) to establish our models; 721 pieces of accurate data (94 Platycladus orientalis, 149 Larix principis-rupprechtii, and 478 Populus spp.) were used as test samples, to evaluate the accuracy of the newly established volume models of three tree species (group). After that, the established volume models (unary/binary models) were compared and analyzed with the corresponding ministerial models for applicability and accuracy. The results showed that the difference between the data observed by the new method and the measurement data of parsing trees was not significant. The total relative error (TRE) and the mean system error (MSE) of the newly established unary/binary volume models were all within ±3%, satisfying the accuracy standard specified by the technical indicators. Compared with the ministerial models applied to the same data, our models’ performance and accuracy were higher (close to the field measurements). Our results also showed that the accuracy of ministerial models was lower than the required standards. It is a promising methodology to use the electronic theodolite non-destructive observation method to establish tree volume tables in the future, especially in areas where cutting is prohibited or restricted and there is a lack of tree volume tables. In addition, this method has also shown a great potential of applicability in forest ecology and environmental protection.

Pastoralist–wolf conflict over livestock depredation is the main factor affecting conservation of grey wolf worldwide. Very limited research has been carried out to evaluate the pattern and nature of livestock depredation by wolf. This study aims to determine the status and nature of human–wolf conflict across different villages in the Hind Kush region of Pakistan during the period January 2016–December 2016. For this purpose, a total of 110 local male respondents from all walks of life were interviewed using a semi-structured questionnaire. The grey wolf was declared as a common species in the area by 51.3% of the locals with an annual sighting rate of 0.46 each. During the year (2016), a total of 358 livestock were lost to grey wolf predation and disease. Of the total livestock loss, grey wolf was held responsible for a total 101 livestock losses. Goat and sheep were the most vulnerable prey species as they accounted for 80 (79.2%) of the total reported depredations. Out of the total economic loss (USD 46,736, USD 424.87/household), grey wolf was accountable for USD 11,910 (USD 108.27 per household), while disease contributed 34,826 (USD 316.6 per household). High depredation was observed during the summer season 58.42% (n = 59) followed by spring and autumn. Unattended livestock were more prone to grey wolf attack during free grazing in forests. Most of the respondents (75.45%) showed aggressive and negative attitudes towards grey wolf. The herders shared more negative attitude (z = −3.21, p = 0.001) than businessman towards the species. Herders having larger herd size displayed more deleterious behavior towards wolves than those having smaller herd size. Active herding techniques, vaccinating livestock, educating locals about wildlife importance, and initiating compensating schemes for affected families could be helpful to decrease negative perceptions.

Species conservation actions are guided by available information on the biogeography of the protected species. In this study, we integrated the occurrence data of Siberian musk deer (Moschus moschiferus L.) collected from 2019 to 2021 with species distribution models to estimate the species’ potential distribution in Northeast China. We then identified conservation priority areas using a core-area zonation algorithm. In addition, we analyzed core patch fragmentation using FRAGSTATS. Lastly, we identified potential connectivity corridors and constructed a potential protection network based on the least-cost path and the circuit theory. The results showed concentrations of M. moschiferus in the northern Greater Khingan Mountains, the southeastern Lesser Khingan Mountains, and the eastern Changbai Mountains, with a potential distribution area of 127,442.14 km². Conservation priority areas included 41 core patches with an area of 106,306.43 km². Patch fragmentation mainly occurred in the Changbai Mountains and the Lesser Khingan Mountains. We constructed an ecological network composed of 41 core patches and 69 linkages for M. moschiferus in Northeast China. The results suggest that the Greater Khingan Mountains represent the most suitable area to maintain the stability of M. moschiferus populations in Northeast China. Considering the high habitat quality requirements of M. moschiferus and its endangered status, we propose that the Chinese government accelerates the construction of the Greater Khingan Mountains National Park and the Lesser Khingan Mountains National Park and enlarges the Northeast China Tiger and Leopard National Park to address the fragmentation of protected areas and the habitat of M. moschiferus.