An improved forest management planning method based on “3S” technology

In recent years, there has been a rapid advancement in the use of unmanned aerial vehicles (UAVs) in various aspects of life, especially in the area of automated data collection. These advancements have brought about numerous possibilities. The article describes the use of an unmanned aerial vehicle, specifically the PHANTOM 4 pro, to gather remote sensing data and create digital topographic plans at a scale of 1:2000 in Phu Tho province's Thanh Son district. A method was suggested for improving the current systems for obtaining remote sensing data for cartography using UAVs. The ease of controlling the UAVs and the quality and timeliness of the data they transmit to control points confirm the value of using them to create topographic maps. In addition to UAVs, fieldwork also involves the use of the GNSS brand CHCNAV I50. The high precision GNSS system enables the camera's 3D position to be detected within a few centimeters at the time of each capture. A digital topographic map was compiled of Thanh Son district, covering 165 hectares, and processed using software such as Agisoft and Global Mapper. The digital topographic map that was produced satisfies the documentation requirements of government organizations. The maximum error in height is 4.7 cm, the error of coordinates north and east are 1.8 cm and 1.4 cm respectively. This was achieved by using 590 raster images, which had a resolution of 2.3 cm and a size of 5472x3648 pixels. Based on the findings, the map's accuracy is within an acceptable range of less than 2 cm, which is suitable for a map scale of 1:2000.

Abstract. This research examines the problem of automation of quality control of digital topographic maps at the scale 1:50 000 of the Main State Topographic Map in Ukraine for the creation and maintaining the seamless topographic database for national needs, which is located on the Geoportal to ensure the development of the National Spatial Data Infrastructure (NSDI) in Ukraine. The authors determined stages of quality control of digital topographic maps at the scale of 1:50 000 for the automation one according to ISO 19157:2013 Geographic Information – Data quality. The realized automated quality control of geodatabases of updated digital topographic maps at a scale of 1:50 000 provides high speed and quality of validation: validation of quality data elements; validation of quality metadata; validation of positional accuracy. The software package “Validate” was developed for automation quality control geodatabases of digital topographic maps at the scale of 1:50 000 using the Python programming language to verify logical consistency, compliance with the rules of topological relationships between features on the map, availability and content of metadata. The “Validate” can be used for automation quality control geodatabase for any map scale, performing setting according to the requirements of the geospatial models.

With the development of modern small satellite technology, it is potential and significant to use the new technology for the earth observation. The stereo mapping micro-satellite—“Experiment Satellite 1”, designed and built by Harbin Institute of Technology, is taken as an example for discussion of the relevant technologies and corresponding principle of the satellite payload and its ground application system. The gr ound application system recei ves the stereo photographs data from the “Experiment Satellite 1” and then produces surveying products such as digital elevation map, digital orthoimage and digital topographic map, etc. “Experiment Satellite 1” is the first satellite where three-line-array CCD camera is used triumphantly in world for stereo mapping. It will have profound influence on the earth observation technique. Keywords: Stereo mapping micro-satellite, Payload, Photogrammet ry, Ground application system, Ground station, Data processing center, Accessorial photogrammetry data, Digital orth oimage, Digital topographic map.

Lake surface water temperature (LSWT) is an important factor of water ecological environment. As global warming, LSWT is also on the rise. Research on the main reasons of LSWT rising is the basis for controlling and improving the regional ecological environment. However, it is difficult for the existing remote sensing images to take into account the temporal and spatial resolution. Low-resolution images have a serious impact on data accuracy and even produce incorrect results. Therefore, obtaining high temporal and spatial resolution images by downscaling is of great significance to more accurately analyze the temporal and spatial characteristics of LSWT. In this article, Dianchi Lake is selected as research area, and the high spatial resolution image (Landsat) and high temporal resolution image (MODIS) are taken as data. Based on the downscaling algorithm of statistics and learning, DisTrad– super-resolution convolutional neural network (SRCNN) downscaling model is proposed, and the monthly average dataset of LSWT with 50 m resolution is constructed. The results showed 1) DisTrad–SRCNN can reflect the most distribution characteristics of LSWT (SSIMday = 0.96, PSNRday = 23.97; SSIMnight = 0.95, PSNRnight = 24.99). 2) LSWT had an overall upward trend (CRday = 0.22 °C/10a, CRnight = 0.21 °C/10a), showing a cyclical change of cold–warm–cold about 4 years. 3) The northern and lakeshore area were basically in the high temperature, and the whole lake presents a 4–5-year warm–cold–warm periodic change; the LSWT closer to the urban and residential areas and its change rate were relatively high, which indirectly reflected the serious impact of human activities on LSWT.

By labelling high spatial resolution (HSR) images with specific semantic classes according to geographical properties, scene classification has been proven to be an effective method for HSR remote sensing image semantic interpretation. Deep learning is widely applied in HSR remote sensing scene classification. Most of the scene classification methods based on deep learning assume that the training datasets and the test datasets come from the same datasets or obey similar feature distributions. However, in practical application scenarios, it is difficult to guarantee this assumption. For new datasets, it is time-consuming and labor-intensive to repeat data annotation and network design. The neural architecture search (NAS) can automate the process of redesigning the baseline network. However, traditional NAS lacks the generalization ability to different settings and tasks. In this paper, a novel neural network search architecture framework—the spatial generalization neural architecture search (SGNAS) framework—is proposed. This model applies the NAS of spatial generalization to cross-domain scene classification of HSR images to bridge the domain gap. The proposed SGNAS can automatically search the architecture suitable for HSR image scene classification and possesses network design principles similar to the manually designed networks. To obtain a simple and low-dimensional search space, the traditional NAS search space was optimized and the human-the-loop method was used. To extend the optimized search space to different tasks, the search space was generalized. The experimental results demonstrate that the network searched by the SGNAS framework with good generalization ability displays its effectiveness for cross-domain scene classification of HSR images, both in accuracy and time efficiency.

With the application of Geographic entities for the expression of remote sensing information model, Analysis of remote sensing image was improved from pixels to the object layers. Combined with rules and reasoning mechanism, identification of geographical entities had been improved with intelligence from the image objects using more multiple factors. The traditional pixel-based methods that could not been effectively carried out information extraction on the high spatial resolution image was broken. A result of comparison was Provided about information extraction of remote sensing image in a mining area of Guangxi. The classification accuracy of two methods based on pixels and objects was evaluated. The difficulty of integration had been reduced between remote sensing information and GIS.

Aim of the study: The objective is to assess the influences of the tree stand age and other forestry management practices on species richness, composition, and distribution of the Patagonian pine plantation bird assemblages. Area of Study: The work was carried out in forested plots of Ponderosa pine located at the Lanín National Park (Patagonia, Argentina).Material and Methods: Birds were sampled using 25 m fixed radius point counts, at four plots varying in age, management, and forest structure. Main Results: A total of 2090 individuals belonging to 34 bird species were observed, their numbers vary significantly depending on the different modes of plantation management. The population density of the 14 most abundant bird species was compared among the four plantation plots and ten species don’t show statistically significant differences in their population density among the different forest plots. The California Quail, the White-Crested Elaenia and the Southern House Wren showed higher densities in pine plantations with lower tree densities and fewer cutting treatments. The Diuca Finch had high densities in the younger plantations not subjected to any treatment. Research highlights: Most of these bird species are opportunistic and a few are found more regularly in these non-native woods than in other native forested or afforested areas. Our data suggest that a mixed scenario based on a mosaic of plantation with patches of native deciduous forest may help maximize the bird diversity in the management of northwestern Patagonian plantation landscapes.Keywords: Bird population; diversity; exotic plantations; Patagonia; tree-age.

BackgroundDetermining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research. The geometric-optical mutual shadowing (GOMS) model can be used to invert the forest canopy structural parameters at the regional scale. However, this method can obtain only the ratios among the horizontal canopy diameter (CD), tree height, clear height, and vertical CD. In this paper, we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale. We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale.ResultsThe semi-variance model can be used to calculate the CD at the regional scale that closely matches (mainly with in a range from − 1 to 1 m) the CD derived from the canopy height model (CHM) data. The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small, with a root mean square error (RMSE) of 1.96 for a 500-m area with high fractional vegetation cover (FVC) (i.e., forest area coverage index values greater than 0.8). Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height. And the error caused by the unmatched spatial resolution is small in dense forest.ConclusionsThe semi-variance model can be used to calculate the CD at the regional scale, together with the canopy structure parameters inverted by the GOMS model, the mean tree height at the regional scale can be obtained. Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.

Myanmar is one of the most forested countries of mainland Southeast Asia and is a globally important biodiversity hotspot. However, forest cover has declined from 58% in 1990 to 44% in 2015. The aim of this paper was to understand the patterns and drivers of deforestation and forest degradation in Myanmar since 2005, and to identify possible policy interventions for improving Myanmar’s forest management. Remote sensing derived land cover maps of 2005, 2010 and 2015 were accessed from the Forest Department, Myanmar. Post-classification change detection analysis and cross tabulation were completed using spatial analyst and map algebra tools in ArcGIS (10.6) software. The results showed the overall annual rate of forest cover loss was 2.58% between 2005 and 2010, but declined to 0.97% between 2010 and 2015. The change detection analysis showed that deforestation in Myanmar occurred mainly through the degradation of forest canopy associated with logging rather than forest clearing. We propose that strengthening the protected area system in Myanmar, and community participation in forest conservation and management. There needs to be a reduction in centralisation of forestry management by sharing responsibilities with local governments and the movement away from corruption in the timber trading industry through the formation of local-based small and medium enterprises. We also recommend the development of a forest monitoring program using advanced remote sensing and GIS technologies.

The effects of seasonal business diversification of British Columbia ski resorts on forest management

Social capital in small-scale forestry: A local case study in Southern Sweden

Forest management policies in Pakistan have been generally following conventional approaches without considering the role of local community’s participation. In Khyber Pakhtunkhwa Pakistan, Joint Forest Management (JFM) was introduced in 1996 with the active involvement of local communities for both sustainable forest management and community livelihoods. This study analyzes the impacts of JFM on rural livelihoods in Pakistan using a sample from 10 villages of Siran and Kalam Forest Divisions of Khyber Pakhtunkhwa. Data were collected through interview schedule and focus group discussions from the local community members & forest department personnel. Results of the study revealed that the JFM played a key role in livelihoods improvement and forest development. Also, JFM improved relationship between local communities and forest department. This study recommends for introduction of policies that help in building trust and friendly relationships between forest department and local communities for sustainable forest management.

Unmanned Aerial Vehicles (UAVs), can be controlled automatically or with the help of a remote control system, provide high spatial and temporal resolution images in addition to its advantages in terms of cost and time. Due to these features, UAV’s have been widely used in studies such as map production, 3D modeling and volume calculation in recent years. In this study, it is aimed to investigate the effects of different flight parameters on the accuracy of Orthomosaic and DEM produced with UAV Systems. In addition, the rolling shutter effect and the use of pre-calibrated and automatic camera calibration parameters were also tested. For this purpose, a total of seven UAV flights were carried out at different heights, overlap ratios, angles and grids at a mining area located within the borders of Aksaray province. Agisoft MetaShape and Pix4D Mapper, which are widely used commercial software, were used to process the UAV images. In this study, 27 points were established and 3D coordinates of these points were measured using GNSS technique. 15 of these points were used as reference points and the remaining 12 points were used as check points. Accuracy analysis was performed by comparing the produced Orthomosaics and DEMs with the check points in the study area. CloudCompare and ArcGIS software were used in the comparison of the products as a result of the evaluation of the images. It is concluded that the Orthomosaics produced using 450 angle and DEMs with 600 angle has higher accuracy than the others. Point clouds and DEMs’ produced using automatic and pre-calibrated camera calibration parameters show that the pre-calibrated images provide higher accuracy. Also, it is seen that when the rolling shutter effect is modeled, the horizontal and vertical accuracy is increased in all three flights with different flight parameters in this study.

Through studying the articles published in the proceedings of the Chinese Forestry Association during 2001-2010, the shift trend of the forestry researches in Taiwan in last decade was analyzed. There are a total of 1,042 articles composed of oral and poster presentations. All articles were divided into two groups: as forestry and forest products sectors. The forestry sector was classified into 5 divisions of silviculture, ecology, forest management, forestry economy, and forest protection. The forest products sector as a whole was categorized as ”forest products utilization division”, which actually subdivides into wood physics, wood chemistry, and others (hard to categorize). The contribution rates of the forestry sector and the forest products sector were 66.4 and 33.6%, respectively. The article counts and rates of the silviculture, ecology, forest management, forestry economy, forest protection, and forest products utilization divisions were respectively 240 (23.0%), 139 (13.3%), 258 (24.8%), 27 (2.6%), 28 (2.7%), and 350 (33.6%). In the forest products utilization division, the subdivision articles counts of wood physics, wood chemistry, and others were 87 (8.4%), 222 (21.3%), and 41 (3.9%), respectively. In comparison, more articles were published in the fields of forest products utilization division; followed by forest management and silviculture divisions. The articles published by the forestry economy division were the least. Owing to diversification trend of the research fields, the broad classification mentioned above was hardly adequate in showing the shifting trend. Therefore, for further analyzing the shifts in details, this study made a further classification of the 6 main divisions into 25 subdivisions. The authors' affiliations were also compared. We expect that this study will be useful references to Taiwan's forestry research and education in the future.

The paper deals with compilation of a large-scale soil map for the site of “Kurilsky” nature reserve, located in the southern part of Kunashir island, the Greater Kuril ridge. The islands make a remote and poorly studied area, and the soil is one of the least examined components of the local geosystems. The zonal soils here are cambisoil, but the variety of soil-forming processes characteristic of the island provides a comparative diversity of the cover. Modern volcanism affects the formation process significantly, although its role is sometimes overestimated. The study is based on field surveys performed in the considered area in 2014–2019, during which descriptions of soils were compiled according to standard methods. The map was compiled using a multispectral image of high spatial resolution from the Pleiades-1B satellite and topographic maps. Thus, the abundance of soils from different levels and divisions does not make a significant effect on the general heterogeneity of the territory soil cover due to the small area of intrazonal soils.