This paper discusses the continuous field view of representing geography and its role in forest inventory. Describing all forest attributes at any location or point and storing this information in digital databases, as an alternative to store forest compartments as vector polygons and associated attributes, is considered to be important for development of remote sensing and GIS applications for forest inventories. Main results achieved following this approach during the last decade in Lithuanian university of agriculture are described. Special research polygon near Kaunas was developed. Four auxiliary data sources (Spot Xi, Landsat TM, digital aerial photos and stand-wise inventory material), three estimators (two-phase sampling with stratification, the k-nearest neighbours and regression) as well as different methods for auxiliary data integration were examined to get point-wise forest characteristics. The lowest root mean square errors at a level of virtual sample point using optimal implementation tactics are – for mean diameter 24 %, height 18%, age 28%, basal area 37%, volume per 1 ha 40% and percentage of coniferous trees 29% of the mean value of corresponding forest characteristic. Integrating additional auxiliary information – characteristics of forest compartments, estimated during the conventional stand-wise inventory – and satellite images improved the overall estimation accuracy. Pre-stratification of sample plots using the attributes of compartments improved the estimation accuracy for certain stand groups. A new approach of segmentation, aimed to construct conventional forest compartments – estimation of point-wise forest characteristics for every pixel of satellite imagery and using them instead of original image values – was suggested and investigated.

This paper deals with the evaluation of the geometrical accuracy of Lithuanian forests compartments geographical data that has been developed using different forest inventory techniques. Geo-reference background database GDB10LT was used as the standard for comparisons. 2500 control points on clearly identifiable places – crossroads, dikes’ intersections, etc. – were selected randomly. The main finding was that the maximal positional root mean square error of clearly identifiable objects in forestry geographic data was 16.47 m (12.37 m and 10.87 m for X and Y coordinates respectively). However, such rather big errors refer to the techniques of GIS database development using paper topographic maps as a background for forest maps and manual digitizing. Enhancement of techniques for GIS database development was found to lead to significant increase in geometrical accuracy of the information.

The aim of this study is to improve the practice of mechanical forest site preparation (FSP) by identifying typical characteristics of FSP, including the direction of FSP machinery, manoeuvre count depending on configuration of parcel and forest type and width of manoeuvre track; to evaluate the productivity depending on different forest growing conditions; to create schemes of technological corridors for commercial thinning; to improve scheme of FSP according to the scheme of technical corridors for commercial thinning and to evaluate changes in the count of manoeuvres and total distance travelled. In this study, we have developed methods to evaluate the quality of FSP. Methods used in this study include GIS analysis of vector data from FSP machinery tracking devices and LiDAR (Light detecting and ranging) data analysis for terrain information. Study shows that there is a significant difference in productivity when the machinery of FSP is driving in different angles to the longitudinal axis of parcel. Reduced productivity is justified by prioritizing topography of the forest floor. Slope is a decisive factor in the ground water movement and should be considered in FSP planning. Study shows that the developed method could be implemented in practice of forest management in 41% of sampled forest stands.

In Lithuania, forests are managed by Lithuanian State Forest Enterprise, municipalities, ministries, etc. and private forest owners. About 50% of all forest land is State importance, privately owned forests cover 40% of forest land, and about 10% of forest land belongs to forests reserved for restitution. Forest management of private ownership force many challenges, because private forest owners are people, who have purchased or received the property after restitution, and often lacks knowledge about forest resources, its dynamics and sustainable forest management. As remote sensing is a valuable source for forest monitoring, because it provides periodic data on forest resource and condition status, these methods are gaining increased attention worldwide. In this context, more scientific efforts are made at developing remote sensing derived geo-spatial data services for sustainable forest management through a web service platform, which would integrate geo-information into daily decision making processes and operation for private forest owners. This article presents a review of privately owned forests’ statistics, questionnaire-based survey about GIS usage and demand for forest owners in Lithuania and links available sources of open geo-spatial data useful for sustainable forest management.

Wet soils play an important role in hydrological, biological and chemical processes, and knowledge on their spatial distribution is essential in forestry, agriculture and similar fields. Digital elevation models (DEM) and various hydrological indexes are used to perform water runoff and accumulation processes. The prerequisite for the calculation of the hydrological indexes is the most accurate representation of the Earth’s surface in the DEM, which must be corrected as necessary to remove surface artefacts that create a dam effect. In addition, different resolutions for DEM give different results, so it is necessary to evaluate what resolution data is needed for a particular study. The aim of this study is to evaluate the feasibility of using existing ditch vector data for DEM correction and the resulting implications for soil moisture prediction. Applied methodology uses a network of available ditch vectors and creates gaps in the overlapping parts of the DEM. The data were processed using open source GIS software QGIS, GRASS GIS and Whitebox GAT. Ditch vector data were obtained from JSC Latvian State Forests and the Latvian Geospatial Information Agency. The results show that by applying the bottomless ditch approach in forest lands on moraine deposits, depending on the accuracy of the ditch vector data, the values of the prediction of the soil wetness both increase and decrease. On the other hand, in forest lands on graciolimnic sediments it is visible that predicted soil wetness values increase in the close proximity of ditches. For forest lands on glaciofluvial and eolitic sediments there were no visible changes because of lack of ditches.

Every day millions of new data records with spatial component are produced in the world, which provide valuable information to make decisions and solve business-related issues. However, a large part of this data is hardly analysed because of their different structures and schemas. The aim of the paper is to improve the integration, processing and analysis of unstructured spatial data. During the research, the author analysed geospatial data types and sources, explored NoSQL solutions for geospatial data processing and chose the open-source tools which are the most appropriate for the stated goals, as well as analysed the coverage of forest areas with protected zones using MongoDB database capabilities and visualized results in a map, using QGIS software. MongoDB is a useful tool for geospatial data analysis and has a large number of embedded topology analysis functions and has drivers for widespread programming languages like JavaScript, Python, PHP, Java, Scala, CNo., C, C + +, etc. QGIS has extensions that allow to make connections to databases, including a connection with MongoDB. Using these features, the developers can develop geographic information systems to analyse geospatial data – structured, semi-structured and unstructured. Generally MongoDB is used for real-time data analysis; however, complicated analysis of large data sets can take up to hours and even days, so it is still necessary to find the best solution to get results in an acceptable time for users. Using MongoDB together with Apache Hadoop – the framework to support big data applications – could be a possible solution for this problem.

Wet areas in agricultural lands are usually not fully or properly managed due to problematic accessibility by heavy machinery and are associated with lower crop yields. There are neither studies regarding spatial distribution of wet agricultural areas in Latvia nor large scale soil maps. Being aware of these wet areas, it would be possible to plan actions for effective management of these areas, starting with a scale of landscape. A geographic information system model could serve as an assistant for decision-making, such as, a direct support for the management of amelioration systems, change of land use and management patterns or granting support payments. Remote sensing data like Sentinel-2 satellite images and LiDAR (Light detecting and ranging) technology can be used to identify local wet areas. The focus of this article is to evaluate different remote sensing indices and methods that can be used to identify wet areas in agricultural lands using open access data and software. From 52 indices, which were analysed with soil moisture field measurements in 33 sample plots, only two of them showed statistical significance in linear regression model (p is less than 0.05): normalized height model in resolution of 25 meters (r2 =0.45) and visible blue spectral band in April (r2 =0.39). Results from this study help to focus on different aspects of remote sensing data usage and methodology for future improvements in order to fully implement LiDAR and Sentinel-2 data for identification of wet areas in agricultural lands.

Land fragmentation is a problem in Europe, and Estonia is not an exception in this respect. Parcel size is widespread characteristic to describe the level of fragmentation. The aim of the study is to find out if there is difference of fragmentation among different groups of landholdings by size. In order to characterise land fragmentation, were calculated the Januszewski and Schmook indexes, average parcel size and average distance from the gravity centre of each landholding to its parcels. Results showed a high level of fragmentation of Estonian agricultural landholdings. There is a high variety of fragmentation inside and among the investigated groups. The average value of Januszewski index for all groups is 0.626, and the average value of Schmook index for all groups is 0.462. The average parcel size for all groups is 7.02 hectares and average distance from the gravity centre of each landholding to its parcels for all groups is 1.57 kilometres.