Changes of groundwater levels and land use with the introduction of canal system in Maheshwar block of Narmada basin (Central India)

2020 ◽  
Author(s):  
Sakshi Shiradhonkar ◽  
Tomochika Tokunaga
Keyword(s):  
Land Use ◽  
River Basin ◽  
Vegetation Index ◽  
Water Levels ◽  
Command Area ◽  
Landsat 8 ◽  
Canal System ◽  
Landsat Images ◽  
Groundwater Levels ◽  
Narmada River

<p>Groundwater is said to be depleting at an alarming rate, and is stated as a major concern for agriculturally driven countries like India. Therefore, understanding the dynamics of water system of the country is prerequisite for assuring its sustainability. According to the GRACE (Gravity Recovery and Climate Experiment) satellite data, the declining TWS (terrestrial water storage) trends are apparent in north and south of India during 2003-2016, while the Narmada river basin which is situated in the central west of the country, shows apparent increase of TWS. In this study, part of the Narmada river basin was chosen as the study site. The major occupation in the basin is agriculture, and hence, water is, in principle, consumed for irrigation. Between 2003 and 2016, the two dams (Indira Sagar dam (2005) and Omkareshwar dam (2008)) were constructed, and the resulting canal system was considered to highly influence water resources availability in the area. To understand the possible effects of the canal system on groundwater level behaviour, we chose the Maheshwar block as the study domain because of its simple canal system layout and single basaltic aquifer setting. The groundwater levels were analysed based on two situations, i.e., before and after canal construction. For the analysis, two distinct seasons, i.e., dry pre-monsoon and rainy monsoon seasons were also taken into account. In the block, the first canal was constructed by 2010, and second by 2013. Based on the extent of each Canal Command Area (CCA), the block was divided into two zones, Zone A (CCA under 1<sup>st</sup> canal) and Zone B (CCA under 2<sup>nd</sup> canal). Among the wells studied, five were located within Zone A. After the canal construction, on an average, about 2 m rise was observed in these well water levels, that is, about 2.45 m in pre-monsoon while 1.62 m in monsoon seasons, respectively. Similar analysis was performed for wells not located in CCA, and it was found that no recognizable change of the groundwater levels was observed. The changes in the land use land cover (LULC) pattern were studied using Landsat 5, Landsat 7 ETM+ and Landsat 8 OLI/TIRS imageries in the block. All the LULC maps were cross-checked with maps from National Remote Sensing Centre (NRSC), India, and these were consistent between each other. The expansion of the agricultural area was studied through 2003-2016. The cultivated area increased from about 8% before the operation of the canal to about 27% after operation in Zone A, whereas the increase was smaller in Zone B, that is, from 2% to around 11%. Based on the NDVI (Normalized Difference Vegetation Index) obtained through Landsat images from different seasons, we also observed that cropping patterns have changed from fallow/single cropping to double/triple cropping after the introduction of canal system in both zones. Based on observations, available amount of water and groundwater storage have increased after canal operation compared with before the operation, and this may at least partly explain the reason why TWS has increased in this area.</p>

scholarly journals ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 821-828
Author(s):  
N. Aslan ◽  
D. Koc-San
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Irrigated Agriculture ◽  
Time Interval ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Study Results ◽  
Landsat 7

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88&thinsp;% for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6&thinsp;&deg;C for 2001 and 6.8&thinsp;&deg;C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r<sup>2</sup>&thinsp;=&thinsp;0.7 and r<sup>2</sup>&thinsp;=&thinsp;0.9 for 2001 and 2014, respectively).

scholarly journals SPATIOTEMPORAL MULTI-SATELLITE BIOPHYSICAL DATA ANALYSIS OF THE EFFECT OF URBANIZATION ON LAND SURFACE AND AIR TEMPERATURE IN BAGUIO CITY, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 47-54
Author(s):  
A. Baloloy ◽  
R. R. Sta. Ana ◽  
J. A. Cruz ◽  
A. C. Blanco ◽  
N. V. Lubrica ◽  
...  
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Use Changes ◽  
High Rate ◽  
Landsat 8 ◽  
Landsat Images

Abstract. Urbanization can be observed through the occurrence of land-use changes as more land is being transformed and developed for urban use. One of the Philippine cities with high rate of urbanization is Baguio City, known for having a subtropical highland climate. To understand the spatiotemporal relationship between urbanization and temperature, this study aims to analyze the correlation of urban extent with land surface and air temperature in Baguio City using satellite-based built-up extents, land surface temperature (LST) maps, and weather station-recorded air temperature data. Built-up extent layers were derived from three satellite images: Landsat, RapidEye and PlanetScope. Land-use land cover (LULC) maps were generated from Landsat images using biophysical indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI); while RapidEye and PlanetScope built-up extent maps were generated by applying the visible green-based built-up index (VgNIR-BI). Mean LST values from 1988 to 2018 during the dry and wet seasons were calculated from the Landsat-retrieved surface temperature layers. The result of the study shows that the increase in the built-up extent significantly intensified the LST during the dry season which was observed in all satellite data-derived built-up maps: RapidEye+PlanetScope (2012–2018; r = 0.88), Landsat 8 (2012–2018; r = 0.63) and Landsat 5,7,8 (1988–2018; r = 0.61). The main LST hotspots were detected inside the Central Business District where it expanded gradually from year 1998 (43 ha) to 2011 (83 ha), but have increased extensively within the years 2014 to 2019 (305 ha). On average, 98.5% of the hotspots detected from 1995 to 2019 are within the equivalent built-up area.

scholarly journals ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 821-828 ◽  
Author(s):  
N. Aslan ◽  
D. Koc-San
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Irrigated Agriculture ◽  
Time Interval ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Study Results ◽  
Landsat 7

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88&thinsp;% for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6&thinsp;&deg;C for 2001 and 6.8&thinsp;&deg;C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r&lt;sup&gt;2&lt;/sup&gt;&thinsp;=&thinsp;0.7 and r&lt;sup&gt;2&lt;/sup&gt;&thinsp;=&thinsp;0.9 for 2001 and 2014, respectively).

scholarly journals Geomorphologic Change in Nagavali River Basin: Geospatial Approach

2021 ◽  
pp. 235-250
Author(s):  
Siba Prasad Mishra ◽  
Ashish Patel ◽  
Abhisek Mishra ◽  
Chandan Kumar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Gis And Remote Sensing ◽  
Human Habitation ◽  
Soil Water Resources

The Nagavali river basin (NRB), along east coast of India investigated for its land use and land cover changes (LULCC) in the golden spike period of Anthropocene Epoch. Attempts made to assess the vicissitudes, causes, and consequences of natural resources, and soil/water resources of the NRB in last three decades as significant changes in hydro-climatic variables occurred. The interstate basin is well developed in lower reaches (north Andhra Pradesh) whereas upper stretches, South Odisha is less organized. GIS and remote sensing are efficient tools for an ideal study of LULCC of the area. Present work evaluates the dynamics of LULCC of NRB. LANDSAT-5, LANDSAT-8, of 1990, 2000, 2010 and 2020, respectively, were digitally classified for land use land cover mapping. The changing aspects of LULCC critically analyzed for three span, 1990–2000, 2000–2010 and 2010–2020. Through Normalized Difference Vegetation Index (NDVI) of the NRB examined carefully to assess the recent LULCC pattern. Major changes are sue to exchanges of areas are in between forest and built-up land followed by water body. The transformations are from forest to human habitation; especially built-up area that constitutes major percentage of the total landscape. The study shows that emphasis is necessary on more water consolidation projects in the upper Nagavali Basin considering the long-term LULC trend 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 Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Optimal Time ◽  
Quantitative Detection ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

scholarly journals Agricultural land use and cover change in the Cerrado/Amazon ecotone: A case study of the upper Teles Pires River basin

2018 ◽  
Vol 48 (2) ◽  
pp. 168-177 ◽  
Author(s):  
Ana Paula Sousa Rodrigues ZAIATZ ◽  
Cornélio Alberto ZOLIN ◽  
Laurimar Goncalves VENDRUSCULO ◽  
Tarcio Rocha LOPES ◽  
Janaina PAULINO
Keyword(s):  
Land Use ◽  
River Basin ◽  
Large Scale ◽  
Agricultural Land ◽  
Temporal Dynamics ◽  
Direct Conversion ◽  
Landsat 8 ◽  
Error Matrix ◽  
Mato Grosso ◽  
Land Use And Cover

ABSTRACT The upper Teles Pires River basin is a key hydrological resource for the state of Mato Grosso, but has suffered rapid land use and cover change. The basin includes areas of Cerrado biome, as well as transitional areas between the Amazon and Cerrado vegetation types, with intensive large-scale agriculture widely-spread throughout the region. The objective of this study was to explore the spatial and temporal dynamics of land use and cover change from 1986 to 2014 in the upper Teles Pires basin using remote sensing and GIS techniques. TM (Thematic Mapper) and TIRS (Thermal Infrared Sensor) sensor images aboard the Landsat 5 and Landsat 8, respectively, were employed for supervised classification using the “Classification Workflow” in ENVI 5.0. To evaluate classification accuracy, an error matrix was generated, and the Kappa, overall accuracy, errors of omission and commission, user accuracy and producer accuracy indexes calculated. The classes showing greatest variation across the study period were “Agriculture” and “Rainforest”. Results indicated that deforested areas are often replaced by pasture and then by agriculture, while direct conversion of forest to agriculture occured less frequently. The indices with satisfactory accuracy levels included the Kappa and Global indices, which showed accuracy levels above 80% for all study years. In addition, the producer and user accuracy indices ranged from 59-100% and 68-100%, while the errors of omission and commission ranged from 0-32% and 0-40.6%, respectively.

scholarly journals Effects of Land-Use Practices on Woody Plant Cover Dynamics in Sahelian Agrosystems in Burkina Faso since the 1970s–1980s Droughts

2019 ◽  
Vol 11 (21) ◽  
pp. 5908 ◽  
Author(s):  
Wendpouiré Arnaud Zida ◽  
Babou André Bationo ◽  
Jean-Philippe Waaub
Keyword(s):  
Land Use ◽  
Burkina Faso ◽  
Vegetation Index ◽  
Woody Plant ◽  
Strong Dependence ◽  
Plant Cover ◽  
Social Adaptation ◽  
Landsat Images ◽  
Land Use Practices ◽  
Agroforestry Practices

The 1970s–1980s droughts in the Sahel caused a significant degradation of land and plant cover. To cope with this situation, populations have developed several biophysical and social adaptation practices. Many of these are agroforestry practices and contribute to the maintenance of agrosystems. Unfortunately, they remain insufficiently documented and their contributions to the resilience of agrosystems insufficiently evaluated. Many authors widely link the regreening in the Sahel after droughts to the resumption of rainfall. This study examines the contribution of agroforestry practices to the improvement of woody plant cover in the North of Burkina Faso after the 1970s–1980s droughts. The examination of practices is carried out by integrating the rainfall, soil, and geomorphology variables. Landsat images are used to detect changes in woody plant cover: increasing, decreasing, and no-change in the Enhanced Vegetation Index. In addition, 230 field observations, coupled with interviews conducted on the different categories of change, have allowed to characterize the biophysical environment and identify land-use practices. The results show a variability of vegetation index explained to 9% (R2 = 0.09) by rainfall. However, Chi-Squared independence tests show a strong dependence between changes in woody plant cover and geomorphology (p = 0.0018 *), land use, land cover (p = 0.0001 *), and land-use practices (p = 0.0001 *). Our results show that rainfall alone is not enough to explain the dynamics of agrosystems’ woody plant cover. Agricultural and social practices related to the dynamics of farmer perceptions play a key role.

scholarly journals The Effects of Land Use Zoning and Densification on Changes in Land Surface Temperature in Seoul

2019 ◽  
Vol 11 (24) ◽  
pp. 7056 ◽  
Author(s):  
Jae-Ik Kim ◽  
Myung-Jin Jun ◽  
Chang-Hwan Yeo ◽  
Ki-Hyun Kwon ◽  
Jun Yong Hyun
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Descriptive Analysis ◽  
Landsat 8 ◽  
Land Use Type ◽  
Coverage Ratio

This study investigated how changes in land surface temperature (LST) during 2004 and 2014 were attributable to zoning-based land use type in Seoul in association with the building coverage ratio (BCR), floor area ratio (FAR), and a normalized difference vegetation index (NDVI). We retrieved LSTs and NDVI data from satellite images, Landsat TM 5 for 2004 and Landsat 8 TIRS for 2014 and combined them with parcel-based land use information, which contained data on BCR, FAR, and zoning-based land use type. The descriptive analysis results showed a rise in LST for the low- and medium-density residential land, whereas significant LST decreases were found in high-density residential, semi-residential, and commercial areas over the time period. Statistical results further supported these findings, yielding statistically significant negative coefficient values for all interaction variables between higher-density land use types and a year-based dummy variable. The findings appear to be related to residential densification involving the provision of more high-rise apartment complexes and government efforts to secure more parks and green spaces through urban redevelopment and renewal projects.

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