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Case Studies. SPRINGER VERLAG GMBH, 1325 pp. Kocaman S., Zhang I., Gruen A., & Poli D. (2006). 3D city modeling from high resolution satellite images. In Proceedings of the ISPRS Conference Topographic Mapping From Space (With Special Emphasis on Small Satellites), 6 pp Kurczyński Z. (2014). Fotogrametria. PWN, Warszawa Lein J. (2012). Environmental Sensing: Analytical Techniques for Earth Observation. Springer-Verlag New York, 334p. https://doi.org/10.1007/978-1-4614-0143-8. Lynch H., R. White A.D. Black, & Naveen R. (2012). Detection, differentiation, and

Abstract

Information about the types of land cover and its use is obtained by the visual interpretation of the color composite of satellite images or by the use of automatic classification algorithms. For obvious reasons, the automatic classification methods make it possible to obtain information quicker and much faster than the traditional interpretation method.

The commonly used automatic methods of satellite image classification, based on supervised or unsupervised classification algorithms, are the most accurate when used with low resolution images. In the case of images with 1-meter-sized pixels, showing a diversity of land cover forms, it is not possible to obtain satisfactory results.

New classification techniques, based on object-oriented classification algorithms, have been developing for a couple of years now. In contrast to the traditional methods, the new operating procedure does not involve the classification of single pixels, but of entire objects, into which the content of the satellite image is divided. Aside from the spectral values of the pixels, the shape of the objects created by the pixels and the relationships between the objects, are also considered during the analysis. Similar to visual interpretation, variation in the texture of the image can also be taken into account in this case.

The aim of this article is to present the possibility of using high density satellite images in object-oriented classification. The classification presented is that of a high-rise built area in Wrocław and of bridges on the Vistula River in Warsaw.

lidar data. - Forestry Studies / Metsanduslikud Uurimused, 52, 5-17. Lang, M. Lükk, T., Rähn, A., Sims, A. 2005. Kasvukäiguproovitükkide kaugseire võimalusi. (Change detection on permanent forest growth sample plots using satellite images). - Metsanduslikud Uurimused / Forestry Studies, 43, 24-37. (In Estonian). Lang, M., Kurvits, V. 2007. Restoration of tree crown shape for canopy cover estimation. - Forestry Studies / Metsanduslikud Uurimused, 46, 23-34. Lang, M., Kuusk, A., Mõttus, M., Rautiainen, M., Nilson, T. 2010. Canopy gap fraction estimation from digital

Abstract

The draught phenomena affecting the traditional agricultural areas in south of Romania has been increasing in intensity over the time, leading to the desertification of several thousands of hectares in the south part of the country. In this study we have computed the vegetation fraction cover for the South-West and South - East regions of Romania, based on the minimum and maximum NDVI extracted from MODIS satellite images. The time frame to refer to is 2000 - 2017, perennially, with special significance given the numerous and prolonged draught intervals these areas have been facing and the social economic evolution, from small farms to large agricultural holdings. The resulted vegetation fraction cover (fc) is correlated to the SPI values in order to determine a pattern to be used in anticipating deviations from the seasonal vegetation productivity. As a conclusion, the study presents a fair balance, indicating the most sensitive areas in soil vegetation cover, due to the SPI change.

., Stage A.R. 1995. Most similar neighbor: an improved sampling inference procedure for natural resource planning. Forest Science , 41, 337–359. Nilsson M. 1997. Estimation of forest variables using satellite image data and airborne lidar. PhD thesis, Swedish University of Agricultural Sciences, The Department of Forest Resource Management and Geomatics, Acta Universitatis Agriculturae Sueciae, Silvestria, 17. Pachana P. 2015. Forest stands volume estimation by using Finnish Multi-Source National Forest Inventory. Master thesis, Eberswalde University for Sustainable

., Nilson, T. 2011. Leaf area index mapping with airborne lidar, satellite images and ground measurements in Järvselja VALERI test site. – Forestry Studies / Metsanduslikud Uurimused, 55, 11–32. Korf, V. 1939. A mathematical definition of forest stands volume growth law. [Příspěvek k matematické definici vzrůstového zákona hmot lesních porostů.]. Lesnická práce, 18, 339–379. (In Czeckian). Korhonen, L., Korpela, I., Heiskanen, J., Maltamo, M. 2011. Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index

, Remote Sensing 9 (5): 500. Miao, L., Zang, S., Bing, Z., Li, S. and Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information, European Journal of Remote Sensing 47 (1): 389–411. Noh, H., Hong, S. and Han, B. (2015). Learning deconvolution network for semantic segmentation, IEEE International Conference on Computer Vision, Santiago, Chile, pp. 1520–1528. Peng, D., Zhang, Y. and Guan, H. (2019). End-to-end change detection for high resolution satellite images using improved UNet++, Remote Sensing 11 (11

Abstract

Riparian woodlands significantly affect the water quality of streams and rivers. Thus, we examined whether the presence of woodlands in riparian buffer zones also impacts the thermal characteristics of lowland streams during the summer. Water temperature data were recorded with digital temperature loggers located in eight quasi-natural sites across the Garwolin Plain in central Poland. The mean, maximum, minimum, and mean daily range parameters were calculated for the whole study period from July to September 2017 with a 30 min. resolution. The percentage of woodlands in the catchment area and buffer zone along the streams was estimated based on satellite images from the Sentinel-1 and Sentinel-2 missions. The Random Forest method was used in the classification process with an accuracy of 96%. The similarity of measurement sites in terms of water temperature was determined using cluster analysis (Ward method), while a Spearman correlation coefficient was applied to compare thermal parameters with the percentage of woodland areas.

The results indicate that water temperature patterns across the measurement sites varied, with mean temperatures ranging from 14.4°C in site T8 to 16.3°C in site T1. Furthermore, the correlation analysis confirmed our hypothesis that the presence of woodland areas does not significantly alter the thermal parameters of lowland streams. Moreover, the cluster analysis showed that sites with significantly different percentages of woodland areas were closely linked due to the similar distributions of water temperature values. However, it must be emphasised that the lack of riparian woodlands in buffer zones does not exclude the presence of shade associated with shrubs and grasses, as well as aquatic plants. In consequence, more advanced indicators of riparian shade, such as vegetation mapping using unmanned aerial vehicle (UAV) or hemispherical photography, should be used for small lowland streams.

Abstract.

The merits and possible problems of the light use efficiency-concept based GPP/NPP models applied together with satellite images and meteorological data to quantitatively understand the role of different meteorological factors in forest productivity are analysed. A concept of the complex meteorological limiting factor for plant productivity is introduced. The factor includes the effects of incoming photosynthetically active radiation as well as the temperature and water limiting factors. Climatologically averaged seasonal courses of the complex meteorological limiting factor derived from the records of two contrasting meteorological stations in Estonia - inland Tartu/Tõravere and coastal Sõrve - are shown. Leaf phenology, here described via the seasonal course of leaf area index (LAI), is interpreted as a possible means to maximise the carbon gain under particular meteorological conditions. The equations for the optimum seasonal course of LAI as derived from the NPP model are presented. As the daily adjustment of plant LAI to sudden changes in meteorological conditions is not possible, several approximate strategies for LAI seasonal course to maximise the yearly NPP of vegetation are analysed. Typical optimal courses of LAI show some seasonal asymmetry resulting in lower values of LAI in the second half of the vegetation period due to higher air temperatures and respiration costs. Knowledge about optimum LAI courses has a cognitive value, but can also be used as the simulated LAI courses in several models when the measured LAI values are not available. As the considered GPP/NPP models fail to adequately describe the local trends in forest and agricultural productivity in Estonia, the ways to improve the model’s performance are shown.

Abstract

When talking about land cover, we need to find a proper way to extract information from aerial or satellite images. In the field of photogrammetry, aerial images are generally acquired by optical sensors that deliver images in four bands (red, green, blue and near-infrared). Recent researches in this field demonstrated that for the image classification process is still place for improvement. From satellites are obtained multispectral images with more bands (e.g. Landsat 7/8 has 36 spectral bands). This paper will present the differences between these two types of images and the classification results using support-vector machine and maximum likelihood classifier. For the aerial and the satellite images we used different sets of classification classes and the two methods mentioned above to highlight the importance of choosing the classes and the classification method.