scholarly journals Accounting for Seasonal Land Use Dynamics to Improve Estimation of Agricultural Irrigation Water Withdrawals

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2471 ◽  
Author(s):  
Anna Msigwa ◽  
Hans C. Komakech ◽  
Boud Verbeiren ◽  
Elga Salvadore ◽  
Tim Hessels ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Irrigation Water ◽  
Reference Points ◽  
Landsat 8 ◽  
Rain Season ◽  
Catchment Areas ◽  
Water Withdrawals ◽  
Short Rains ◽  
Land Use Maps

The assessment of water withdrawals for irrigation is essential for managing water resources in cultivated tropical catchments. These water withdrawals vary seasonally, driven by wet and dry seasons. A land use map is one of the required inputs of hydrological models used to estimate water withdrawals in a catchment. However, land use maps provide typically static information and do not represent the hydrological seasons and related cropping seasons and practices throughout the year. Therefore, this study assesses the value of seasonal land use maps in the quantification of water withdrawals for a tropical cultivated catchment. We developed land use maps for the main seasons (long rains, dry, and short rains) for the semi-arid Kikuletwa catchment, Tanzania. Three Landsat 8 images from 2016 were used to develop seasonal land use land cover (LULC) maps: March (long rains), August (dry season), and October (short rains). Quantitative and qualitative observation data on cropping systems (reference points and questionnaires/surveys) were collected and used for the supervised classification algorithm. Land use classifications were done using 20 land use and land cover classes for the wet season image and 19 classes for the dry and short rain season images. Water withdrawals for irrigated agriculture were calculated using (1) the static land use map or (2) the three seasonal land use maps. Clear differences in land use can be seen between the dry and the other seasons and between rain-fed and irrigated areas. A difference in water withdrawals was observed when seasonal and static land use maps were used. The highest differences were obtained for irrigated mixed crops, with an estimation of 572 million m3/year when seasonal dynamic maps were used and only 90 million m3/year when a static map was used. This study concludes that detailed seasonal land use maps are essential for quantifying annual irrigation water use of catchment areas with distinct dry and wet seasonal dynamics.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals Monitoring the Spatial Variation of Aerosol Optical Depth and Its Correlation with Land Use/Land Cover in Wuhan, China: A Perspective of Urban Planning

2021 ◽  
Vol 18 (3) ◽  
pp. 1132
Author(s):  
Qijiao Xie ◽  
Qi Sun
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Urban Planning ◽  
Land Cover ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Water Bodies ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
The Impact

Aerosols significantly affect environmental conditions, air quality, and public health locally, regionally, and globally. Examining the impact of land use/land cover (LULC) on aerosol optical depth (AOD) helps to understand how human activities influence air quality and develop suitable solutions. The Landsat 8 image and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products in summer in 2018 were used in LULC classification and AOD retrieval in this study. Spatial statistics and correlation analysis about the relationship between LULC and AOD were performed to examine the impact of LULC on AOD in summer in Wuhan, China. Results indicate that the AOD distribution expressed an obvious “basin effect” in urban development areas: higher AOD values concentrated in water bodies with lower terrain, which were surrounded by the high buildings or mountains with lower AOD values. The AOD values were negatively correlated with the vegetated areas while positively correlated to water bodies and construction lands. The impact of LULC on AOD varied with different contexts in all cases, showing a “context effect”. The regression correlations among the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and AOD in given landscape contexts were much stronger than those throughout the whole study area. These findings provide sound evidence for urban planning, land use management and air quality improvement.

Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Technology ◽  
Open Land ◽  
Cover Class ◽  
Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

scholarly journals Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

2018 ◽  
Vol 10 (10) ◽  
pp. 3421 ◽  
Author(s):  
Rahel Hamad ◽  
Heiko Balzter ◽  
Kamal Kolo
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Land Cover ◽  
Land Use Changes ◽  
Landsat 8 ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Kurdistan Region ◽  
The Past ◽  
Past Trend

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

scholarly journals PALSAR-2/ALOS-2 AND OLI/LANDSAT-8 DATA INTEGRATION FOR LAND USE AND LAND COVER MAPPING IN NORTHERN BRAZILIAN AMAZON

2018 ◽  
Vol 24 (2) ◽  
pp. 250-269 ◽  
Author(s):  
João Arthur Pompeu Pavanelli ◽  
João Roberto dos Santos ◽  
Lênio Soares Galvão ◽  
Maristela Xaud ◽  
Haron Abrahim Magalhães Xaud
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Brazilian Amazon ◽  
Landsat 8 ◽  
Array Type ◽  
Complex Landscape ◽  
Hybrid Classification ◽  
L Band ◽  
Thematic Accuracy ◽  
Better Than

Abstract: In northern Brazilian Amazon, the crops, savannahs and rainforests form a complex landscape where land use and land cover (LULC) mapping is difficult. Here, data from the Operational Land Imager (OLI)/Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)/ALOS-2 were combined for mapping 17 LULC classes using Random Forest (RF) during the dry season. The potential thematic accuracy of each dataset was assessed and compared with results of the hybrid classification from both datasets. The results showed that the combination of PALSAR-2 HH/HV amplitudes with the reflectance of the six OLI bands produced an overall accuracy of 83% and a Kappa of 0.81, which represented an improvement of 6% in relation to the RF classification derived solely from OLI data. The RF models using OLI multispectral metrics performed better than RF models using PALSAR-2 L-band dual polarization attributes. However, the major contribution of PALSAR-2 in the savannahs was to discriminate low biomass classes such as savannah grassland and wooded savannah.

scholarly journals ALTERAÇÃO NA COBERTURA FLORESTAL DA REGIÃO NOROESTE DA AMAZÔNIA MATOGROSSENSE

Nativa ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 520
Author(s):  
Luani Rosa de Oliveira Piva ◽  
Rorai Pereira Martins Neto
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Cover ◽  
Forest Conversion ◽  
Landsat 8 ◽  
Amazon Forest ◽  
Landsat 8 Oli ◽  
Mato Grosso ◽  
The Past ◽  
Landsat 5 Tm

Nos últimos anos, a intensificação das atividades antrópicas modificadoras da cobertura vegetal do solo em território brasileiro vem ocorrendo em larga escala. Para fins de monitoramento das alterações da cobertura florestal, as técnicas de Sensoriamento Remoto da vegetação são ferramentas imprescindíveis, principalmente em áreas extensas e de difícil acesso, como é o caso da Amazônia brasileira. Neste sentido, objetivou-se com este trabalho identificar as mudanças no uso e cobertura do solo no período de 20 anos nos municípios de Aripuanã e Rondolândia, Noroeste do Mato Grosso, visando quantificar as áreas efetivas que sofreram alterações. Para tal, foram utilizadas técnicas de classificação digital de imagens Landsat 5 TM e Landsat 8 OLI em três diferentes datas (1995, 2005 e 2015) e, posteriormente, realizada a detecção de mudanças para o uso e cobertura do solo. A classificação digital apresentou resultados excelentes, com índice Kappa acima de 0,80 para os mapas gerados, indicando ser uma ferramenta potencial para o uso e cobertura do solo. Os resultados denotaram uma conversão de áreas florestais principalmente para atividades antrópicas agrícolas, na ordem de 472 km², o que representa uma perda de 1,3% de superfície de floresta amazônica na região de estudo.Palavras-chave: conversão de áreas florestais; uso e cobertura do solo; classificação digital; análise multitemporal. CHANGE IN FOREST COVER OF THE NORTHWEST REGION OF AMAZON IN MATO GROSSO STATE ABSTRACT: In the past few years, the intensification of anthropic activities that modify the soil-vegetation cover in Brazil’s land has been occurring on a large scale. To monitor the forest cover changes, the techniques of Remote Sensing of vegetation are essential tools, especially in large areas and with difficult access, as is the case of the Brazilian Amazon. The aim of this work was to identify the changes in land use and land cover, over the past 20 years, in the municipalities of Aripuanã and Rondolândia, Northwest of Mato Grosso State, in order to quantify the effective altered areas. Landsat 5 TM and Landsat 8 OLI digital classification images techniques were used in three different dates (1995, 2005 and 2015) and, later, the detection to the land use and land cover changes. The digital classification showed excellent results, with kappa index above 0.80 for the generated maps, indicating the digital classification as a potential tool for land use and land cover. Results reflect the conversion of forest areas mainly for agricultural activities, in the order of 472 km², representing a loss of 1.3% of Amazon forest surface in the study region.Keywords: forest conversion; land use and land cover; digital classification; multitemporal analysis.

scholarly journals Transboundary analysis of the land cover in Razdolnaya river basin

2021 ◽  
pp. 70-77
Author(s):  
Т.К. МУЗЫЧЕНКО ◽  
М.Н. МАСЛОВА
Keyword(s):  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Rice Fields ◽  
Landsat 8 ◽  
Agricultural Lands ◽  
Transboundary River ◽  
Russian Part ◽  
Land Use Structure ◽  
Sentinel 2

В статье рассмотрено пространственное распределение типов земель в пределах трансграничного бассейна р. Раздольная. На основе дешифрирования космических снимков Sentinel-2 и Landsat 8 составлена карта пространственного распределения типов земель по состоянию на 2019 г. Исходя из геоэкологической классификации ландшафтов В.А. Николаева в данной работе было выделено 12 типов земель: используемые и неиспользуемые сельскохозяйственные земли, используемые и неиспользуемые рисовые поля, карьеры, леса, лесопосадки, рубки, луга, застроенные земли, водные объекты, а также кустарники и редколесья. Представлены абсолютные и относительные площади для каждого типа земель по трансграничному бассейну в целом, а также отдельно для его российской и китайской частей. По результатам дешифрирования данных дистанционного зондирования установлено, что российская и китайская части бассейна р. Раздольная имеют существенные трансграничные различия в структуре земель. На российской части бассейна лесами покрыто чуть более половины площади, но при этом значительные площади занимают сельскохозяйственные земли и луга. В некоторых местах луга и сельскохозяйственные земли преобладают в большей степени, чем леса. На китайской части лесные территории доминируют над другими типами земель. Сельскохозяйственные земли и луга образуют узкие и длинные полосы и имеют более мозаичное распространение, чем на российской части. Здесь заметно меньше площади застроенных земель, а площади рубок и лесопосадок больше, чем на российской части. Площади карьеров примерно равны в обеих частях бассейна. The transboundary Razdolnaya river basin is nearly evenly split up between Primorsky Krai of Russian Federation and Heilongjiang and Jilin provinces of People’s Republic of China. The Chinese and the Russian parts of the transboundary river have developed independently of each other. Therefore, the two have a different land cover and land use structure. The analysis of land cover and land use structure is of utmost importance for the understanding the modern state of land development and the possibilities of its future development. Using the remote sensing data, such as Sentinel-2 and Landsat 8 satellite imagery, the land cover and land use map of the Razdolnaya transboundary river basin for 2019 has been composed by means of the ArcMap 10.5 software package. According to V.A. Nikolaev’s geoecological classification of landscapes, we have identified 12 land types: forests, meadows, shrubs and woodlands, agricultural lands, unused agricultural lands, rice fields, unused rice fields, built-up areas, reforestation lands, logging, quarries, and bodies of water. We have provided area coverage for each type of land of the whole transboundary basin, and for the Russian and Chinese parts. According to the results of computer-aided visual deciphering and automatic deciphering, forests are the most common land use type in the basin. In the Chinese part of the basin, forests dominate over the other types of land. Agricultural lands and meadows have assumed narrow and linear shapes. Built-up areas have less coverage here than in the Russian part of the basin. However, the coverage of logging and reforestation lands is considerably larger than in the Russian part of the basin. In the Russian part of the basin, forests co-dominate with the agricultural lands and meadows. In some areas of this part of the basin forests disappear almost completely. The Russian part of the basin also has the larger coverage of shrubs and woodlands, unused agricultural lands, rice fields and unused rice fields. The coverage of quarries is roughly equal in both parts of the basin.

scholarly journals IDENTIFYING LAND USE AND LAND COVER (LULC) CHANGE FROM 2000 TO 2025 DRIVEN BY TOURISM GROWTH: A STUDY CASE IN BALI

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1621-1627
Author(s):  
A. B. Rimba ◽  
T. Atmaja ◽  
G. Mohan ◽  
S. K. Chapagain ◽  
A. Arumansawang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Growth Direction ◽  
Landsat 8 ◽  
Open Area ◽  
Landsat Images ◽  
Lulc Change ◽  
Tourism Activity ◽  
Accuracy Of Prediction

Abstract. Bali has been open to tourism since the beginning of the 20th century and is known as the first tourist destination in Indonesia. The Denpasar, Badung, Gianyar, and Tabanan (Sarbagita) areas experience the most rapid growth of tourism activity in Bali. This rapid tourism growth has caused land use and land cover (LULC) to change drastically. This study mapped the land-use change in Bali from 2000 to 2025. The land change modeller (LCM) tool in ArcGIS was employed to conduct this analysis. The images were classified into agricultural land, open area, mangrove, vegetation/forest, and built-up area. Some Landsat images in 2000 and 2015 were exploited in predicting the land use and land cover (LULC) change in 2019 and 2025. To measure the accuracy of prediction, Landsat 8 OLI images for 2019 were classified and tested to verify the LULC model for 2019. The Multi-Layer Perceptron (MLP) neural network was trained with two influencing factors: elevation and road network. The result showed that the built-up growth direction expanded from the Denpasar area to the neighbouring areas, and land was converted from agriculture, open area and vegetation/forest to built-up for all observation years. The built-up was predicted growing up to 43 % from 2015 to 2025. This model could support decision-makers in issuing a policy for monitoring LULC since the Kappa coefficients were more than 80% for all models.

scholarly journals Hydrological Responses to Land Use Land Cover Changes in the Fincha’a Watershed, Ethiopia

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 916
Author(s):  
Urgessa Kenea ◽  
Dereje Adeba ◽  
Motuma Shiferaw Regasa ◽  
Michael Nones
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Local Governments ◽  
Swat Model ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Hydrological Response ◽  
African Countries ◽  
Hydrological Responses ◽  
Basin Scale

Land use land cover (LULC) changes are highly pronounced in African countries, as they are characterized by an agriculture-based economy and a rapidly growing population. Understanding how land use/cover changes (LULCC) influence watershed hydrology will enable local governments and policymakers to formulate and implement effective and appropriate response strategies to minimize the undesirable effects of future land use/cover change or modification and sustain the local socio-economic situation. The hydrological response of the Ethiopia Fincha’a watershed to LULCC that happened during 25 years was investigated, comparing the situation in three reference years: 1994, 2004, and 2018. The information was derived from Landsat sensors, respectively Landsat 5 TM, Landsat 7 ETM, and Landsat 8 OLI/TIRS. The various LULC classes were derived via ArcGIS using a supervised classification system, and the accuracy assessment was done using confusion matrixes. For all the years investigated, the overall accuracies and the kappa coefficients were higher than 80%, with 2018 as the more accurate year. The analysis of LULCC revealed that forest decreased by 20.0% between the years 1994–2004, and it decreased by 11.8% in the following period 2004–2018. Such decline in areas covered by forest is correlated to an expansion of cultivated land by 16.4% and 10.81%, respectively. After having evaluated the LULCC at the basin scale, the watershed was divided into 18 sub-watersheds, which contained 176 hydrologic response units (HRUs), having a specific LULC. Accounting for such a detailed subdivision of the Fincha’a watershed, the SWAT model was firstly calibrated and validated on past data, and then applied to infer information on the hydrological response of each HRU on LULCC. The modelling results pointed out a general increase of average water flow, both during dry and wet periods, as a consequence of a shift of land coverage from forest and grass towards settlements and build-up areas. The present analysis pointed out the need of accounting for past and future LULCC in modelling the hydrological responses of rivers at the watershed scale.

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