scholarly journals Ландшафтно-екологічне різноманіття території Полтавської області

2019 ◽  
pp. 127-133
Author(s):  
П.В. Писаренко ◽  
М.С. Самойлік ◽  
О.Ю. Диченко
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Arable Land ◽  
Landscape Diversity ◽  
Practical Significance ◽  
Ecological Diversity ◽  
Global Land Cover ◽  
The Earth ◽  
Global Land

Мета статті – дослідження і визначення типів землекористування  та показників ландшафтно-екологічного різноманіття території Полтавської області  за даними дистанційного зондування Землі (Global Land Cover 2000 Project). Методика дослідження. Для визначення типів використання земель у межах досліджуваної території був використаний метод аналізу даних дистанційного зондування Землі (GLC 2000). Результати дослідження. У статті наведені дані щодо ландшафтно-екологічного різноманіття типів покриву Полтавської області за даними дистанційного зондування Землі. В результаті проведених досліджень та відповідних розрахунків було встановлено, що найбільше ландшафтно-екологічне різноманіття характерне для східних і центральних районів Полтавської області. Найбільше воно є характерним для Решетилівського та Великобагачанського районів, які знаходяться в центрі досліджуваної області, а найменші показники ландшафтного різноманіття були характерні для Чорнухінського, Семенівського, Глобинського та Кобеляцького районів. Рівень ландшафтного різноманіття в умовах Полтавської області передусім визначається співвідношенням агроекосистем до ландшафтних комплексів інших типів. Елементи наукової новизни. Здійснено оцінку ландшафтно-екологічного різноманіття Полтавської області та досліджено його динаміку на основі даних дистанційного зондування поверхні Землі MODIS. Практична значущість. Аналіз типів покриву земної поверхні у межах Полтавської області показав значну розораність території та її зайнятість агроекосистемами. Ландшафтне різноманіття формувалося за рахунок ріллі, територій з мозаїкою ріллі й трав’янистого покриву та територій з розрідженим рослинним покривом. Компоненти природних екосистем були зосереджені у заплавах річок регіону та представлені заплавними лісами, лугами та болотами. The purpose of the article is to study and determine the types of land use and indicators of landscape and ecological diversity of the territory of Poltava region according to the data of the remote sensing of the Earth (Global Land Cover 2000 Project). Methods of research. The method of analysis of the Earth remote sensing data (GLC 2000) was used to determine the types of land use within the studied area. The research results. The data on the landscape-ecological diversity of the cover types in Poltava region according to the data of remote sensing of the Earth have been given in the article. As a result of the conducted research and corresponding calculations it has been established that the largest landscape and ecological diversity is characteristic for the eastern and central districts of Poltava region. It is the most characteristic of Reshetylivka and Velyka Bahachka districts located in the center of the studied area, while the lowest indicators of landscape diversity were found in Chornukhy, Semenivka, Hlobyno and Kobeliaky districts. The level of landscape diversity in Poltava region is primarily determined by the ratio of agro-ecosystems to landscape complexes of other types. The elements of scientific novelty. The assessment of the landscape and ecological diversity of Poltava region has been carried out and its dynamics based on the data of MODIS remote sensing of the Earth's surface has been studied. Practical significance. The analysis of the cover types of the Earth's surface within Poltava region has shown significant plowing of the territory and its occupation with agro-ecosystems. Landscape diversity was formed at the expense of arable land, territories with a mosaic of arable land, grassland and the areas with spaced vegetation cover. The components of natural ecosystems were concentrated in river floodplains of the region and are represented by floodplain forests, meadows and swamps.

A remote sensing based vegetation classification logic for global land cover analysis

1995 ◽  
Vol 51 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Steven W. Running ◽  
Thomas R. Loveland ◽  
Lars L. Pierce ◽  
R.R. Nemani ◽  
E.R. Hunt
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Classification ◽  
Global Land Cover ◽  
Global Land

scholarly journals Geospatial assessment of land use and land cover dynamics in the mid-zone of Ghana

2020 ◽  
Vol 62 (4) ◽  
pp. 288-305
Author(s):  
Addo Koranteng ◽  
Isaac Adu-Poku ◽  
Emmanuel Donkor ◽  
Tomasz Zawiła-Niedźwiecki
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Forest Cover ◽  
Anthropogenic Activities ◽  
Arable Land ◽  
Rapid Urbanization ◽  
The Past ◽  
Open Forest

AbstractLand use and land cover (LULC) terrain in Ghana has undergone profound changes over the past years emanating mainly from anthropogenic activities, which have impacted countrywide and sub-regional environment. This study is a comprehensive analysis via integrated approach of geospatial procedures such as Remote Sensing (RS) and Geographic Information System (GIS) of past, present and future LULC from satellite imagery covering Ghana’s Ashanti regional capital (Kumasi) and surrounding districts. Multi-temporal satellite imagery data sets of four different years, 1990 (Landsat TM), 2000 (Landsat ETM+), 2010 (Alos and Disaster Monitoring Constellation-DMC) and 2020 (SENTINEL), spanning over a 30-year period were mapped. Five major LULC categories – Closed Forest, Open Forest, Agriculture, Built-up and Water – were delineated premised on the prevailing geographical settings, field study and remote sensing data. Markov Cellular Automata modelling was applied to predict the probable LULC change consequence for the next 20 years (2040). The study revealed that both Open Forest and Agriculture class categories decreased 51.98 to 38.82 and 27.48 to 20.11, respectively. Meanwhile, Built-up class increased from 4.8% to 24.8% (over 500% increment from 1990 to 2020). Rapid urbanization caused the depletion of forest cover and conversion of farmlands into human settlements. The 2040 forecast map showed an upward increment in the Built-up area up to 35.2% at the expense of other LULC class categories. This trend from the past to the forecasted future would demand that judicious LULC resolutions have to be made to keep Ghana’s forest cover, provide arable land for farming activities and alleviate the effects of climate change.

Global land cover classification by remote sensing: present capabilities and future possibilities

1991 ◽  
Vol 35 (2-3) ◽  
pp. 243-255 ◽  
Author(s):  
John Townshend ◽  
Christopher Justice ◽  
Wei Li ◽  
Charlotte Gurney ◽  
Jim McManus
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Classification ◽  
Global Land Cover ◽  
Global Land

scholarly journals Towards global land-cover and land-use reconstructions over the Holocene

2017 ◽  
Vol 25 (2) ◽  
pp. 121-121
Author(s):  
Marie-José Gaillard ◽  
P Gonzales ◽  
S Harrison ◽  
K Klein Goldewijk ◽  
F Li ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
The Holocene ◽  
Global Land Cover ◽  
Global Land

scholarly journals THE ANALYSIS OF OBJECT-BASED CHANGE DETECTION IN MINING AREA: A CASE STUDY WITH PINGSHUO COAL MINE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1017-1023 ◽  
Author(s):  
M. Zhang ◽  
W. Zhou ◽  
Y. Li
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Arable Land ◽  
Mining Area ◽  
Remote Sensing Image ◽  
Change Vector Analysis ◽  
Object Based ◽  
Change Vector

Accurate information on mining land use and land cover change are crucial for monitoring and environmental change studies. In this paper, RapidEye Remote Sensing Image (Map 2012) and SPOT7 Remote Sensing Image (Map 2015) in Pingshuo Mining Area are selected to monitor changes combined with object-based classification and change vector analysis method, we also used R in highresolution remote sensing image for mining land classification, and found the feasibility and the flexibility of open source software. The results show that (1) the classification of reclaimed mining land has higher precision, the overall accuracy and kappa coefficient of the classification of the change region map were 86.67 % and 89.44 %. It’s obvious that object-based classification and change vector analysis which has a great significance to improve the monitoring accuracy can be used to monitor mining land, especially reclaiming mining land; (2) the vegetation area changed from 46 % to 40 % accounted for the proportion of the total area from 2012 to 2015, and most of them were transformed into the arable land. The sum of arable land and vegetation area increased from 51 % to 70 %; meanwhile, build-up land has a certain degree of increase, part of the water area was transformed into arable land, but the extent of the two changes is not obvious. The result illustrated the transformation of reclaimed mining area, at the same time, there is still some land convert to mining land, and it shows the mine is still operating, mining land use and land cover are the dynamic procedure.

scholarly journals The Use of Remote Sensing and GIS to Study Land Use Changes in Barka Wilaya, Oman

2019 ◽  
Vol 10 (1) ◽  
pp. 15
Author(s):  
Eyad H Fadda ◽  
Fatemah Al Shebli ◽  
Ayshah Al Kabi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Land Degradation ◽  
Urban Areas ◽  
Land Use Changes ◽  
Arable Land ◽  
The Impact ◽  
Fertile Land

Many studies house indicated the increase of the proportion of urban areas over the arable land in many provinces of the Sultanate of Oman. This came as a result of urban growth and development processes taking place since the era of the Renaissance which started in 1970. Consequently, spatial variation in land use is an important issue to be taken into consideration, because lands are being converted to be less productive, due to the lack of raw soil, vegetation, and water as a result of human exploitation of the limited resources in different ways, in addition to the natural factors of droughts and floods and all that will eventually lead to land degradation. Barka province (wilayat) in al Batinah Governorate is one of the provinces, which has been affected by land cover/land use changes due to several reasons. Therefore, this study will focus on the determination of land use changes, whether commercial or residential that have been occurred in the province, in addition to the loss of agricultural areas and fertile land during the period from 1987 to 2015. Remote sensing and geographic information system (GIS) were utilized in order to delineate and to determine the cause of shrinking in the arable land and fertile land. Satellite images were used to detect the change in land use/land cover by applying selective digital image processing techniques such as supervised classification and change detection. Thematic maps were prepared using GIS software with attribute data about the land uses in the study area, which highlights and show the impact of urban growth on land degradation.

scholarly journals OPERATIONAL NEAR REAL TIME RICE AREA MAPPING USING MULTI-TEMPORAL SENTINEL-1 SAR OBSERVATIONS

2018 ◽  
Vol XLII-4 ◽  
pp. 433-438 ◽  
Author(s):  
J. D. Mohite ◽  
S. A. Sawant ◽  
A. Kumar ◽  
M. Prajapati ◽  
S. V. Pusapati ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Real Time ◽  
Classification Accuracy ◽  
Early Season ◽  
Global Land Cover ◽  
Rice Area ◽  
Global Land ◽  
Multi Temporal ◽  
Spatio Temporal

<p><strong>Abstract.</strong> Spatio-temporal crop phenological information helps in understanding trends in food supply, planning of seed/fertilizer inputs, etc. in a region. Rice is one of the major food sources for many regions of the world especially in monsoon Asia and accounts for more than 11<span class="thinspace"></span>% of the global cropland. Accurate, on-time and early information on spatial distribution of rice would be useful for stakeholders (cultivators, fertilizer/pesticide manufacturers and agriculture extension agencies) to effectively plan supply of inputs, market activities. Also, government agencies can plan and formulate policies regarding food security. Conventional methods involves manual surveying for developing spatio-temporal crop datasets while remote sensing satellite observations provide cost effective alternatives with better spatial extent and temporal frequency. Remote sensing is one of the effective technologies to map the areal extent of the crops using optical as well as microwave/Synthetic Aperture RADAR (SAR) sensors. Cloud cover is the major problem faced in using the optical datasets during monsoon (June to Sept. locally called <i>Kharif</i> season). Hence, Sentinel-1 C-band (center frequency: 5.405<span class="thinspace"></span>GHz) RADAR sensor launched by European Space Agency (ESA) which has an Interferometric Wide-swath mode (IW) with dual polarization (VV and VH) has been used for rice area mapping. Limited studies have attempted to establish operational early season rice area mapping to facilitate local governance, agri-input management and crop growers. The key contribution of this work is towards operational near real time and early season rice area mapping using multi-temporal SAR data on GEE platform. The study has been carried out in four districts viz., Guntur, Krishna, East Godavari andWest Godavari from Andhra Pradesh (AP), India during the period of <i>Kharif</i> 2017. The study region is also called as coastal AP where rice transplanting during the <i>Kharif</i> season is carried out during mid Jun. till Aug. and harvesting during Oct. to mid Dec. months. The training data for various classes viz, Rice, NonRice-Agriculture, Waterbodies, Settlements, Forest and Aquaculture have been obtained from GEE, Global Land Cover (GLC) layers developed by ESA and field observations. We have evaluated the performance of Random Forest (RF) classifier by varying the number of trees and incrementally adding the SAR images for model training. Initially the model has been trained considering two images available from mid June 2017. Further, various models have been trained by adding one consecutive image till end of August 2017 and classification performance has been evaluated on validation dataset. The classified output has been further masked with agriculture non-agriculture layer derived from global land-cover layer obtained from ESA. Analysis shows that incremental addition of temporal observations improves the performance of the classifier. The overall classification accuracy ranges between 78.11 to 87.00<span class="thinspace"></span>%. We have found that RF classifier with 30 trees trained on six images available from mid June till end August performed better with classification accuracy of 87.00<span class="thinspace"></span>%. However, accuracy assessment performed using independent stratified random sampling approach showed the classification accuracy of 84.45<span class="thinspace"></span>%. An attempt is being made to follow the proposed approach for current (i.e. 2018) season and provide incremental rice area estimates in near real-time.</p>

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