• Journal of Korea Spatial Information System Society
• /
• v.9 no.1
• /
• pp.1-13
• /
• 2007

The necessity of future index is increasing to predict the future location of moving objects promptly for various location-based services. A representative research topic related to future index is the probability trajectory prediction technique that improves reliability using the past trajectory information of moving objects in the road network environment. However, the prediction performance of this technique is lowered by the heavy load of extensive future trajectory search in long-range future queries, and its index maintenance cost is high due to the frequent update of future trajectory. Thus, this paper proposes the Probability Cell Trajectory-Tree (PCT-Tree), a cell-based future indexing technique for efficient long-range future location prediction. The PCT-Tree reduces the size of index by rebuilding the probability of extensive past trajectories in the unit of cell, and improves the prediction performance of long-range future queries. In addition, it predicts reliable future trajectories using information on past trajectories and, by doing so, minimizes the cost of communication resulting from errors in future trajectory prediction and the cost of index rebuilding for updating future trajectories. Through experiment, we proved the superiority of the PCT-Tree over existing indexing techniques in the performance of long-range future queries.

• Journal of Ecology and Environment
• /
• v.48 no.1
• /
• pp.96-109
• /
• 2024

Background: Khyber Pakhtunkhwa government initiated the Billion Tree Tsunami Afforestation Project including regeneration and afforestation approaches. An effort was made to assess the distribution characteristics of afforested species under present and future climatic scenarios using ecological niche modelling. For sustainable forest management, landscape ecology can play a significant role. A significant change in the potential distribution of tree species is expected globally with changing climate. Ecological niche modeling provides the valuable information about the current and future distribution of species that can play crucial role in deciding the potential sites for afforestation which can be used by government institutes for afforestation programs. In this context, the potential distribution of 8 tree species, Cedrus deodara, Dalbergia sissoo, Juglans regia, Pinus wallichiana, Eucalyptus camaldulensis, Senegalia modesta, Populus ciliata, and Vachellia nilotica was modeled. Results: Maxent species distribution model was used to predict current and future distribution of tree species using bioclimatic variables along with soil type and elevation. Future climate scenarios, shared socio-economic pathways (SSP)2-4.5 and SSP5-8.5 were considered for the years 2041-2060 and 2081-2100. The model predicted high risk of decreasing potential distribution under SSP2-4.5 and SSP5-8.5 climate change scenarios for years 2041-2060 and 2081-2100, respectively. Recent afforestation conservation sites of these 8 tree species do not fall within their predicted potential habitat for SSP2-4.5 and SSP5-8.5 climate scenarios. Conclusions: Each tree species responded independently in terms of its potential habitat to future climatic conditions. Cedrus deodara and P. ciliata are predicted to migrate to higher altitude towards north in present and future climate scenarios. Habitat of D. sissoo, P. wallichiana, J. regia, and V. nilotica is practiced to be declined in future climate scenarios. Eucalyptus camaldulensis is expected to be expanded its suitability area in future with eastward shift. Senegalia modesta habitat increased in the middle of the century but decreased afterwards in later half of the century. The changing and shifting forests create challenges for sustainable landscapes. Therefore, the study is an attempt to provide management tools for monitoring the climate change-driven shifting of forest landscapes.

• Journal of Korea Spatial Information System Society
• /
• v.7 no.1 s.13
• /
• pp.77-89
• /
• 2005

Recently, application area on the Location Based System(LBS) is increasing because of development of mobile-communication and GPS technique. Previous studies on the index of moving objects are classified as either index for past trajectories or current/future positions. It is necessary to develop a unified index because many applications need to process queries about both past trajectories and current/future positions at the same time. In this paper, the past trajectories of moving objects are represented as line segments and the current and future positions are represented as the function of time. We propose a new index called PCR-tree(Past, Current R-tree) for unification of past, current and future positions. Nodes of the index have bounding boxes that enclose all position data and entries in the nodes are accessed with only one interface. We implement the proposed index and show a feasibility of processing the queries about temporal-spatial domain with the query tool which we develop.

• Korean Journal of Remote Sensing
• /
• v.27 no.3
• /
• pp.389-401
• /
• 2011

Most nations around the world have expressed significant concern in the climate change due to a rapid increase in green-house gases and thus reach an international agreement to control total amount of these gases for the mitigation of global warming. As the most important absorber of carbon dioxide, one of major green-house gases, forest resources should be more tightly managed with a means to measure their total amount, forest biomass, efficiently and accurately. Forest biomass has close relations with forest areas and tree height. Airborne LiDAR data helps extract biophysical properties on forest resources such as tree height more efficiently by providing detailed spatial information about the wide-range ground surface. Many researchers have thus developed various methods to estimate tree height using LiDAR data, which retain different performance and characteristics depending on forest environment and data characteristics. In this study, we attempted to investigate such various techniques to estimate tree height, elaborate their advantages and limitations, and suggest future research directions. We first examined the characteristics of LiDAR data applied to forest studies and then analyzed methods on filtering, a precedent procedure for tree height estimation. Regarding the methods for tree height estimation, we classified them into two categories: individual tree-based and regression-based method and described the representative methods under each category with a summary of their analysis results. Finally, we reviewed techniques regarding data fusion between LiDAR and other remote sensing data for future work.

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.10b
• /
• pp.43-45
• /
• 2003

Nowadays, with numerous emerging applications (e.g., traffic control, meteorology monitoring, mobile computing, etc.), access methods to process current and future queries for moving objects are becoming increasingly important. Among these methods, the time-parameterized R-tree (TPR-tree) seems likely the most flexible method in one, two, or three-dimensional space. A key point of TPR-tree is that the (conservative) bounding rectangles are expressed by functions of time. In this paper, we propose a new method, which takes into account positions of its moving objects against the rectangle's bounds. In proposed method, the size of bounding rectangle is significantly smaller than the traditional bounding rectangle in many cases. By this approach, we believe that the TPR-tree can improve query performance considerably.

• Journal of Korea Spatial Information System Society
• /
• v.8 no.1 s.16
• /
• pp.17-25
• /
• 2006

TPR*-tree is the most widely-used index structure for effectively predicting the future positions of moving objects. The TPR*-tree, however, has the problem that both of the dead space in a bounding region and the overlap among hounding legions become larger as the prediction time in the future gets farther. This makes more nodes within the TPR*-tree accessed in query processing time, which incurs the performance degradation. In this paper, we examine the performance problem quantitatively with a series of experiments. First, we show how the performance deteriorates as a prediction time gets farther, and also show how the updates of positions of moving objects alleviates this problem. Our contribution would help provide Important clues to devise strategies improving the performance of TPR*-trees further.

• 한국공간정보시스템학회:학술대회논문집
• /
• 2004.12a
• /
• pp.113-116
• /
• 2004

Assisted by high technologies of information and communication in storing and collecting moving object information, many applications have been developing technical methods to exploit databases of moving objects effectively and variously. Among them, today, Current and Anticipated Future Position Indexing methods manage current positions of moving objects in order to anticipate future positions of them or more complex future queries. They, however, strongly demand update performance as fast enough to guarantee certainty of queries as possible. In this paper, we propose a new indexing mettled derived from the TPR-tree that should has update performance considerably improved, we named it BUR-tree. In our method, index structure can be inserted, deleted, and updated with a number (or bulk) of objects simultaneously rather than one object at a time as in conventional methods. This method is intended to be applied to a traffic network in which vast number of objects, such as cars, pedestrians, moves continuously.

• Journal of KIISE:Databases
• /
• v.31 no.3
• /
• pp.252-260
• /
• 2004

Recently, the TPR-tree has been proposed to support spatio-temporal queries for moving objects. Subsequently, various methods using the TPR-tree have been intensively studied. However, although the TPR-tree is one of the most popular access methods in spatio-temporal databases, any cost model for the TPR-tree has not yet been proposed. Existing cost models for the spatial index such as the R-tree do not accurately ostinato the number of disk accesses for spatio-temporal queries using the TPR-tree, because they do not consider the future locations of moving objects. In this paper, we propose a cost model of the TPR-tree for moving objects for the first time. Extensive experimental results show that our proposed method accurately estimates the number of disk accesses over various spatio-temporal queries.

• Journal of Forest and Environmental Science
• /
• v.34 no.5
• /
• pp.412-417
• /
• 2018

Agroforestry is emerged as climate smart agriculture system and known to help in maintaining soil nutrient sustainability but its rate of expansion is still not appreciable. The present paper incorporates the different species under various agroforestry practices its density, growth and growing stock. The most dominated agroforestry practices in Bilaspur district identified as boundary tree based agri- silviculture (32%) followed with inside field tree based agri-silviculture (21%). Agri-horti-silvicultural system found merely in 5% farmer's field while silvo-pastoral practice in 8% fields. The result depicts that the most prevailing agroforestry tree species in non-forest area of Bilaspur comprises Acacia nilotica 36%, Butea monosperma 22%, Albizia spp 16%, Terminalia arjuna 7%, Azadirachta indica 3.5% and other species 15.5%. More than 90% farmer allows tree species growing naturally in their fields mainly for fuel wood, timber and as source of additional income as these species need not require special attention and care, while only 5% farmer's has adopted Tectona grandis, Dalbergia sissoo etc commercially for higher future return. The paper also discusses the constraints on agroforestry for enabling development of agroforestry in future.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.5
• /
• pp.23-31
• /
• 2010

With the advances in the information technology and mobile communications, we now face increasing demands for various services based on both of position tracking of moving objects and their efficient index scheme. Accordingly, the $TPR^*$-tree, which were proposed for efficiently tracking moving objects and predicting their positions in the future time, has drawn much intention. As the $TPR^*$-tree came from the R-tree that is suitable for indexing static objects, it does not support cheap update costs. Therefore, it seems to be very costly to index moving objects if there are frequent occurrences of node updates caused by continuously changing velocities and positions. If some moving objects with high velocities have node updates, in particular, then the $TPR^*$-tree may suffer from many unnecessary updates in the wide range of tree regions. To avoid such a problem, we propose a method that can keep fast-moving objects in the child nodes of the root node, thereby saving node update costs in the $TPR^*$-tree. To show our performance advantages and retaining $TPR^*$-tree features, we performed some performance experiments using a simulation technique.