scholarly journals Landscape Development Types in Central Lika 1980−2012 − Applying Spatial and Process-Oriented GIS Model

2021 ◽  
Vol 20 (35) ◽  
pp. 4-29
Author(s):  
Marta Hamzić ◽  
Borna Fuerst Bjeliš

This paper presents an analysis and definition of development types and subtypes in the landscape of Central Lika, based on processes of change in the period 1980−2012 CORINE Land Cover database data for 1980 and 2012 were used to establish the landscape types in Central Lika in those years. The landscape types in Central Lika were determined according to land cover/land use. Based on the mutual relations between the established landscape types in the two observed years, we established six landscape development types and three subtypes in Central Lika. The spatial distribution of landscape development types and subtypes in Central Lika was determined using the Standard Deviational Ellipse (Directional Distribution) spatial analysis method. The results obtained showed that in the observation period (1980−2012), most of the area of Central Lika (89.46%) belonged to the Stagnation landscape type. Other development types were present to a much lesser extent (about 5.5%) and were found to be Vegetation succession, Agrarisation, Vegetation degradation and Built-up land. We established a spatial gradation of three phases in the process of vegetation succession, that is, development subtypes from the centre to the margins of the research area. At the same time, in the observation period, the process and trend of extensification of land use in Central Lika was twice as present as intensification.

2021 ◽  
Author(s):  
Rasha Abou Samra

Abstract Land surface temperature (LST) is a significant environmental variable that is appreciably influenced by land use /land cover changes. The main goal of this research was to quantify the impacts of land use/land cover change (LULC) from the drying of Toshka Lakes on LST by remote sensing and GIS techniques. Landsat series TM and OLI satellite images were used to estimate LST from 2001 to 2019. Automated Water Extraction Index (AWEI) was applied to extract water bodies from the research area. Optimized Soil-Adjusted Vegetation Index (OSAVI) was utilized to predict the reclaimed land in the Toshka region until 2019. The results indicated a decrease in the lakes by about 1517.79 km2 with an average increase in LST by about 25.02 °C between 2001 and 2019. It was observed that the dried areas of the lakes were converted to bare soil and are covered by salt crusts. The results indicated that the land use change was a significant driver for the increased LST. The mean annual LST increased considerably by 0.6 °C/y between 2001 and 2019. A strong negative correlation between LST and Toshka Lakes area (R-square = 0.98) estimated from regression analysis implied that Toshka Lakes drying considerably affected the microclimate of the study area. Severe drought conditions, soil degradation, and many environmental issues were predicted due to the rise of LST in the research area. There is an urgent need to develop favorable strategies for sustainable environmental management in the Toshka region.


2010 ◽  
Vol 18 (3) ◽  
pp. 234-241 ◽  
Author(s):  
Margarita Jankauskaitė ◽  
Darijus Veteikis

Today landscape change monitoring becomes important in the field of sustainable development planning. Real changes of landscape have to be observed in a large scale (not smaller than 1:10,000) in order to avoid generalization of small landscape elements. In such a scale it is rational to perform the monitoring in sample areas that would be enough statistically abundant. The paper offers an original method of distributing the landscape sample areas in Lithuanian territory, differing from most methods based on random choose of sample areas though thorough analysis of the analogous methods abroad was performed. The work was sponsored by the Environmental Agency at the Lithuanian Ministry of Environment. In accordance to the spread of different natural landscape types (like clayey plains, morainic hills, sandy plains, etc.), a set of 100 sample areas (2.5 km2 each) was distributed in Lithuanian territory. To increase the sample area number in smaller landscape types (spit, coastal sandy plain, delta), some proportional corrections were made. Thus, the largest number of the sample areas was assigned to the most spread clayey plains (22), the smallest number – to sandy coastal plain (3). In order to find a concrete place for each sample area inside the landscape type a computer program was employed and the highest representation principle applied. Several tens of thousands possible positions of the sample areas were tested in order to find the best in representing land cover structure. This was achieved by calculating relative remoteness of tested samples’ land cover structure from the respective landscape type structure, further selecting the most patchy samples. Selecting the position of a sample area was also influenced by the buffer capacity (resistance to the chemical impact) of landscape, mostly concentrating on the areas with less buffer capacity (more sensitive to chemical pollution). Santrauka Tvariajai pletrai planuoti tampa aktualia kraštovaizdžio kaitos stebesena. Realūs kraštovaizdžio pokyčiai Lietuvos mastu turi būti fiksuojami stambiuoju masteliu (ne smulkesniu nei 1:10 000), vengiant nepageidautino smulkiu kraštovaizdžio elementu generalizavimo. Tokiu masteliu racionalu būtu pokyčiu stebejimus atlikti etalonuose, ju skaičius turetu būti statistiškai patikimas. Pateikiama originali kraštovaizdžio monitoringo etalonu išdestymo Lietuvos teritorijoje metodika. Darbas buvo atliktas remiant Aplinkos apsaugos agentūrai prie Lietuvos aplinkos ministerijos. Metodika parengta atsižvelgiant ir i užsienio šaliu patirti. Pagal kraštovaizdžio tipu paplitima proporcingai buvo išdalyta 100 2,5 km2 ploto etalonu, papildomai koreguojant (padidinant) etalonu skaičiu mažai paplitu‐siuose kraštovaizdžio tipuose (nerijoje, pajūrio lygumoje, deltoje). Taigi daugiausia etalonu (22) buvo skirta plačiausiai paplitusioms molingosioms lygumoms, mažiausiai (3) – pajūrio lygumai. Etalonams konkrečios vietos buvo parenkamos kompiuterine programa ir vadovautasi didžiausio reprezentatyvumo principu. Kiekvieno kraštovaizdžio tipo buvo išbandyta nuo keliu šimtu iki keliasdešimties tūkstančiu galimu etalonu padečiu, nustatyta pagal žemes dangos struktūra reprezentuojančios geriausiai. Etalonu vietu parinkimas buvo siejamas ir su kraštovaizdžio buferiškumo cheminei taršai arealais, daugiau koncentruojant mažesnio buferiškumo (jautresniuose cheminei taršai) plotuose. Резюме В настоящее время мониторинг изменений ландшафта становится актуальным для планировки сбалансированного развития. Реальные изменения ландшафта в Литве должны быть прослеживаемы в крупном масштабе (не мельче чем 1:10.000) во избежание нежелательной генерализации мелких структурных элементов ландшафта. В таком масштабе рационально осуществлять наблюдения на специально выделенных эталонных территориях, число которых должно быть статистически достаточным. В статье приведена методика расположения названных эталонов на территории Литвы. Работа выполнена при поддержке Агентства по охране окружающей среды при Министерстве окружающей среды. Методика разработана с учетом опыта зарубежных стран. С учетом распределения ландшафтных типов пропорционально было поделено сто эталонов площадью 2,5 км2 каждый. Дополнительно корректировалось (увеличивалось) число эталонов в мало распространенных ландшафтных типах (на косе, приморской равнине, в дельте). Наибольшее число эталонов (22) было отдано глинистым (наиболее распространенным) равнинам, а наименьшее (3) – приморской равнине. С целью подбоpa для эталонов конкретных мест была применена компьютерная программа, а также следовали принципу наивысшей репрезентативности. В каждом ландшафтном типе было испробовано от нескольких сот до нескольких десятков тысяч возможных положений эталонов с целью определить лучшее положение по репрезентативности земельно покровной структуры. Подбор мест для эталонов был осуществлен с учетом сопротивляемости ландшафта химическому загрязнению. Больше эталонов размещалось в наименее устойчивых ареалах.


Author(s):  
L. Cohen ◽  
O. Almog ◽  
M. Shoshany

Abstract. A novel classification technique based on definition of unique spectral relations (such as slopes among spectral bands) for all land cover types named (SSF Significant Spectral Features) is presented in the article.A large slopes combination between spectral band pairs is calculated and spectral characterizations that emphasizes the best spectral land cover separation is sought. Increasing in dimensionality of spectral representations is balanced by the simplicity of calculations. The technique has been examined on data acquired by a flown hyperspectral scanner (AISA). The spectral data was narrowed into the equivalent 8 world-view2 channels. The research area was in the city of “Hadera”, Israel, which included 10 land cover types in an urban area, open area and road infrastructure. The comparison between the developed SSF technique and common techniques such as: SVM (Support Vector Machine) and ML (Maximum Likelihood) has shown a clear advantage over ML technique, while produced similar results as SVM. The poorest results of using SSF technique was achieved in an herbaceous area (70%). However, the simplicity of the method, the well-defined parameters it produces for interpreting the results, makes it intuitive over using techniques such as SVM, which is considered as a not explicit classifier.


Author(s):  
E. B. Silva ◽  
S. H. M. Nogueira ◽  
A. P. S. Matos ◽  
L. L. Parente ◽  
L. G. Ferreira ◽  
...  

Abstract. The present work aims to establish of Visual Interpretation Criterias of the land-use and land-cover (LULC) classes of the Brazilian biomes. The process relies on the efforts of experts from each biome, Ph.D. and Master's students, and undergraduate students in research. Due to the particularities, the criterias were elaborated individually for each biome. The classes correspond to MapBiomas collection 04 legend. In each LULC class, the user has the following information: class definition, patterns (e.g., color, texture, roughness), and historical Landsat images (RGB 564) from the dry and rainy periods, as well as high-resolution images and field photos of the class. These visual interpretation criterias was used to generate data of samples for MapBiomas mapping validation. With the help of Visual Interpretation Criterias, experienced and inexperienced interpreters were able to produce high-quality sample data without visual inspection. This initiative, a pioneer in Brazil, is a tool to support future interpretations of Brazilian biomes. The results can be found on Lapig website.


2014 ◽  
Vol 5 (1) ◽  
pp. 177-195 ◽  
Author(s):  
J. Pongratz ◽  
C. H. Reick ◽  
R. A. Houghton ◽  
J. I. House

Abstract. Reasons for the large uncertainty in land use and land cover change (LULCC) emissions go beyond recognized issues related to the available data on land cover change and the fact that model simulations rely on a simplified and incomplete description of the complexity of biological and LULCC processes. The large range across published LULCC emission estimates is also fundamentally driven by the fact that the net LULCC flux is defined and calculated in different ways across models. We introduce a conceptual framework that allows us to compare the different types of models and simulation setups used to derive land use fluxes. We find that published studies are based on at least nine different definitions of the net LULCC flux. Many multi-model syntheses lack a clear agreement on definition. Our analysis reveals three key processes that are accounted for in different ways: the land use feedback, the loss of additional sink capacity, and legacy (regrowth and decomposition) fluxes. We show that these terminological differences, alone, explain differences between published net LULCC flux estimates that are of the same order as the published estimates themselves. This has consequences for quantifications of the residual terrestrial sink: the spread in estimates caused by terminological differences is conveyed to those of the residual sink. Furthermore, the application of inconsistent definitions of net LULCC flux and residual sink has led to double-counting of fluxes in the past. While the decision to use a specific definition of the net LULCC flux will depend on the scientific application and potential political considerations, our analysis shows that the uncertainty of the net LULCC flux can be substantially reduced when the existing terminological confusion is resolved.


2013 ◽  
Vol 4 (2) ◽  
pp. 677-716 ◽  
Author(s):  
J. Pongratz ◽  
C. H. Reick ◽  
R. A. Houghton ◽  
J. House

Abstract. Reasons for the high uncertainty in land use and land cover change (LULCC) emissions go beyond recognized issues to do with available data on land cover change and the fact that model simulations rely on a simplified and incomplete description of the complexity of biological and LULCC processes. The large range across published LULCC emission estimates is also fundamentally to do with the exact definition of the net land use flux with respect to the way it is calculated by models. We introduce a conceptual framework that allows us to compare the different types of models and simulation setups used to derive land use fluxes. We find that published studies are based on at least 9 different definitions of the net land use flux. Our analysis reveals three key processes that are accounted for in different ways: the land use feedback, the loss of additional sink capacity, and legacy (regrowth and decomposition) fluxes. We show that these terminological differences, alone, explain differences between published net land use flux estimates that are of the order of published estimates. While the decision to use a specific definition will depend on the scientific application and potential political considerations, our analysis shows that the uncertainty of the net land use flux can be substantially reduced when the existing terminological confusion is resolved.


2020 ◽  
Vol 9 (4) ◽  
pp. 232 ◽  
Author(s):  
Yongqing Zhao ◽  
Rendong Li ◽  
Mingquan Wu

Current land cover research focuses primarily on spatial changes in land cover and the driving forces behind these changes. Among such forces is the influence of policy, which has proven difficult to measure, and no quantitative research has been conducted. On the basis of previous studies, we took Hubei Province as the research area, using remote sensing (RS) images to extract land cover change data using a single land use dynamic degree and a comprehensive land use dynamic degree to study land cover changes from 2000 to 2015. Then, after introducing the Baidu Index (BDI), we explored its relationship with land cover change and built a tool to quantitatively measure the impact of changes in land cover. The research shows that the key search terms in the BDI are ‘cultivated land occupation tax’ and ‘construction land planning permit’, which are closely related to changes in cultivated land and construction land, respectively. Cultivated land and construction land in all regions of Hubei Province are affected by policy measures with the effects of policy decreasing the greater the distance from Wuhan, while Wuhan is the least affected region.


Author(s):  
E. A. Kaiser ◽  
R. d. M. Linn ◽  
S. B. A. Rolim ◽  
P. S. Käfer ◽  
N. S. Rocha ◽  
...  

Abstract. The objective of this study was to verify the evolution of surface temperature associated with land use and land cover from 1985 to 2019 in Porto Alegre, RS, Brazil. The methodological procedures were performed in five steps: 1. Definition of the study area; 2. Land use and land cover classification from images of Landsat 5 satellite Thematic Mapper (TM) and Operational Land Imager (OLI) from Landsat 8 satellite; 3. Calculation of surface temperature from TM sensor band 6 and OLI sensor band 10; 4. Analysis of temperature evolution over the historical series; and 5. Temporal relation between surface temperature and land use and land cover classes. The results demonstrated that higher temperatures were associated to the evolution of two classes of land use and land cover: urban area and exposed soil, with the former occupying 31% in 1989 to 75% in 2018 of the study area. When comparing the first and last decade of the historical series for each season, there was an average increase of 4.18 °C in the surface temperature of the districts. Thus, adopting policies that mitigate the effects caused by densification and urban sprawl are necessary, mainly through the conservation of vegetated areas and water reservoirs, as these are crucial for the maintenance of air humidity and evapotranspiration.


2021 ◽  
Vol 8 (4) ◽  
pp. 773
Author(s):  
Lidia Injiliana ◽  
Tri Widiastuti ◽  
Joko Nugroho Riyono

Land degradation will result in widespread land damage, especially damage to forest land. Changes in land use from permanent vegetation to intensive agricultural land make the soil more easily eroded. One of the determining factors of erosion is soil erosion and soil cover vegetation. Soil erodiability is the average amount of soil lost each year per unit of the index, while the land cover is useful to protect the soil from the threat of damage by erosion and improve soil conditions. The research area located in Silat Hilir Sub-District of Kapuas Hulu Regency is a strategic area of the district from the point of economic importance. The Upper Kapuas Statistics Agency says there is a change in land use from year to year. The changes occurred not only in the increase in land use but also on changes in land use. The purpose and benefit of this research are to know the class of soil erodiability as well as to provide information on the class of land erodiability in The New Village of Silat Hilir District of Kapuas Hulu Regency. The value of soil erodiability is calculated using the Wischmeier and Smith formulas (1978) and determined based on the overlay of two maps, namely the land type map and the land cover map. Soil erodibility on various land cover in Silat Hilir Sub-District of Kapuas Hulu sub-district consists of 4 classes of land erodiability, among others: class 1 (very low), class 2 (low), class 4 (somewhat high), and class 5 (high).Keywords: Erodibility, Land Cover, Soil


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