Journal articles on the topic 'Land Surface Water Index'
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Christian, Jordan I., Jeffrey B. Basara, Lauren E. L. Lowman, Xiangming Xiao, Daniel Mesheske, and Yuting Zhou. "Flash drought identification from satellite-based land surface water index." Remote Sensing Applications: Society and Environment 26 (April 2022): 100770. http://dx.doi.org/10.1016/j.rsase.2022.100770.
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Li, Li, Qidi Yu, Ling Gao, Bin Yu, and Zhipeng Lu. "The Effect of Urban Land-Use Change on Runoff Water Quality: A Case Study in Hangzhou City." International Journal of Environmental Research and Public Health 18, no. 20 (October 13, 2021): 10748. http://dx.doi.org/10.3390/ijerph182010748.
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The main functions of this research are to guide the proportion of urban land that is used and the layout of the facilities on it, help understand the changes to surface runoff that are caused by land being used in urban development, and thus solve surface runoff pollution. Hangzhou City, China has been selected for the experiment, and the way in which its land is utilized as well as the grading of urban construction projects in the demonstration area are specifically analyzed. This study systematically distinguishes the definitions of impervious area based on the Sutherland equation and analyzes the impact of different impervious area subtypes on surface runoff water quality. Then, we compare the impact of impervious area subtypes with the impact of other land-use patterns on surface runoff water quality. This study shows the relationship between different land-use types and runoff water bodies: Land-use index can affect runoff water quality; Greening activities, impervious surface, and the water quality index are negatively correlated; the effective impervious area rate is positively correlated with the water quality index. The paper suggests that increasing the proportion of green spaces and permeable roads in build-up land reduces the effective impervious area (EIA) and thus controls land runoff pollution and improves runoff water quality.
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Cui, Yaoping, Yiming Fu, Nan Li, Xiaoyan Liu, Zhifang Shi, Jinwei Dong, and Yan Zhou. "A Novel Approach for Automatic Urban Surface Water Mapping with Land Surface Temperature (AUSWM)." Remote Sensing 14, no. 13 (June 25, 2022): 3060. http://dx.doi.org/10.3390/rs14133060.
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The principal difficulty in extracting urban surface water using remote-sensing techniques is the influence of noise from complex urban environments. Although various methods exist, there are still many sources of noise interference when extracting urban surface water, and automatic cartographic methods with long time series are especially scarce. Here, we construct an automatic urban surface water extraction method from the combination of traditional water index, urban shadow index (USI), and land surface temperature (LST) by using the Google Earth Engine cloud computing platform and Landsat imagery. The three principal findings derived from the application of the method were as follows. (i) In comparison with autumn and winter, LST in spring and summer could better distinguish water from high-reflection ground objects, shadows, and roads and roofs covered by asphalt. (ii) The overall accuracy of Automated Water Extraction Index (AWEIsh) in Zhengzhou was 77.5% and the Kappa coefficient was 0.55; with consideration of the USI and LST, the overall accuracy increased to 96.0% and the Kappa coefficient increased to 0.92. (iii) During 1990–2020, the area of urban surface water in Zhengzhou increased, with an evident trend in expansion from 11.51 km2 in 2008 to 49.28 km2 in 2020. Additionally, possible omissions attributable to using 30m-resolution imagery to extract urban water areas were also discussed. The method proposed in this study was proven effective in eliminating the influence of noise in urban areas, and it could be used as a general method for high-accuracy long-term mapping of urban surface water.
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Chowdhury, Tahmid Anam, and Md Saiful Islam. "Assessing and Simulating Impacts of Land Use Land Cover Changes on Land Surface Temperature in Mymensingh City, Bangladesh." Environment and Natural Resources Journal 20, no. 2 (November 26, 2021): 1–19. http://dx.doi.org/10.32526/ennrj/20/202100110.
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Urban developments in the cities of Bangladesh are causing the depletion of natural land covers over the past several decades. One of the significant implications of the developments is a change in Land Surface Temperature (LST). Through LST distribution in different Land Use Land Cover (LULC) and a statistical association among LST and biophysical indices, i.e., Urban Index (UI), Bare Soil Index (BI), Normalized Difference Builtup Index (NDBI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI), this paper studied the implications of LULC change on the LST in Mymensingh city. Landsat TM and OLI/TIRS satellite images were used to study LULC through the maximum likelihood classification method and LSTs for 1989, 2004, and 2019. The accuracy of LULC classifications was 84.50, 89.50, and 91.00 for three sampling years, respectively. From 1989 to 2019, the area and average LST of the built-up category has been increased by 24.99% and 7.6ºC, respectively. Compared to vegetation and water bodies, built-up and barren soil regions have a greater LST each year. A different machine learning method was applied to simulate LULC and LST in 2034. A remarkable change in both LULC and LST was found through this simulation. If the current changing rate of LULC continues, the built-up area will be 59.42% of the total area, and LST will be 30.05ºC on average in 2034. The LST in 2034 will be more than 29ºC and 31ºC in 59.64% and 23.55% areas of the city, respectively.
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Ciężkowski, Wojciech, Sylwia Szporak-Wasilewska, Małgorzata Kleniewska, Jacek Jóźwiak, Tomasz Gnatowski, Piotr Dąbrowski, Maciej Góraj, Jan Szatyłowicz, Stefan Ignar, and Jarosław Chormański. "Remotely Sensed Land Surface Temperature-Based Water Stress Index for Wetland Habitats." Remote Sensing 12, no. 4 (February 14, 2020): 631. http://dx.doi.org/10.3390/rs12040631.
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Despite covering only 2–6% of land, wetland ecosystems play an important role at the local and global scale. They provide various ecosystem services (carbon dioxide sequestration, pollution removal, water retention, climate regulation, etc.) as long as they are in good condition. By definition, wetlands are rich in water ecosystems. However, ongoing climate change with an ambiguous balance of rain in a temperate climate zone leads to drought conditions. Such periods interfere with the natural processes occurring on wetlands and restrain the normal functioning of wetland ecosystems. Persisting unfavorable water conditions lead to irreversible changes in wetland habitats. Hence, the monitoring of habitat changes caused by an insufficient amount of water (plant water stress) is necessary. Unfortunately, due to the specific conditions of wetlands, monitoring them by both traditional and remote sensing techniques is challenging, and research on wetland water stress has been insufficient. This paper describes the adaptation of the thermal water stress index, also known as the crop water stress index (CWSI), for wetlands. This index is calculated based on land surface temperature and meteorological parameters (temperature and vapor pressure deficit—VPD). In this study, an unmanned aerial system (UAS) was used to measure land surface temperature. Performance of the CWSI was confirmed by the high correlation with field measurements of a fraction of absorbed photosynthetically active radiation (R = −0.70) and soil moisture (R = −0.62). Comparison of the crop water stress index with meteorological drought indices showed that the first phase of drought (meteorological drought) cannot be detected with this index. This study confirms the potential of using the CWSI as a water stress indicator in wetland ecosystems.
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Ngandam Mfondoum, A. H., P. G. Gbetkom, R. Cooper, S. Hakdaoui, and M. B. Mansour Badamassi. "IMPROVING THE LAND SURFACE GENERAL DROUGHT INDEX MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W11 (February 14, 2020): 101–8. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w11-101-2020.
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Abstract. Drought affects all human activities and ecosystems. Nearly 40 percent of the world’s population inhabit Drylands, and they depend on agriculture for their food, security and livelihoods. Among the remote sensing indices developed, the Land Surface General Drought Index (LSGDI) was recently proposed. This paper proposes an improved model of LSGDI to face the issue of drought in semi-arid and arid regions. The experiment was conducted for the Maga’s floodplain, in North-Cameroon. The method uses satellite images of Landsat in 1987, 2003 and 2018, for January and March or April, corresponding to the middle and the end of the dry season. A Vegetation Moisture Index (VMI) and a Normalized Difference Soil Drought Index (NDSoDI) are both developed. On an orthogonal plan, their projections give a drought line that expresses the improved LSGDI (LSGDI2) as the root sum square of the NDSoDI and the VMI. The LSGDI2 results are ranged in [0.09 – 0.14] interval, which is used to define the threshold and ease the qualifiers for drought classes. The visual patterns easily match the sandy areas of the original Landsat images with the highest values, while the vegetation and water areas match the lowest values. Compared with the LSGDI and Second Modified Perpendicular drought Index (MPDI1), the new index reflectance values are higher. Finally, although LSGDI2 curve’s evolution follows the NDSoDI one at 94%, the new spectral index values depends on the both components, helping to map highest values of drought and moisture in Maga’s floodplain, for a sustainable rice culture expansion.
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Marthews, T. R., S. J. Dadson, B. Lehner, S. Abele, and N. Gedney. "A high-resolution global dataset of topographic index values for use in large-scale hydrological modelling." Hydrology and Earth System Sciences Discussions 11, no. 6 (June 12, 2014): 6139–66. http://dx.doi.org/10.5194/hessd-11-6139-2014.
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Abstract. Modelling land surface water flow is of critical importance for simulating land-surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL, and the most important parameter of this model is the well-knowntopographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically-conditioned HydroSHEDS data sets using the GA2 algorithm. At 15 arcsec resolution, these layers are 4× finer than the resolution of the previously best-available topographic index layers, the Compound Topographic Index of HYDRO1k (CTI). In terms of the largest river catchments occurring on each continent, we found that in comparison to our revised values, CTI values were up to 20% higher in e.g. the Amazon. We found the highest catchment means were for the Murray-Darling and Nelson-Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. We believe these new index layers represent the most robust existing global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.
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Yang, Liangyan, Jianfeng Li, Zenghui Sun, Jinbao Liu, Yuanyuan Yang, and Tong Li. "Daily actual evapotranspiration estimation of different land use types based on SEBAL model in the agro-pastoral ecotone of northwest China." PLOS ONE 17, no. 3 (March 15, 2022): e0265138. http://dx.doi.org/10.1371/journal.pone.0265138.
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Evapotranspiration (ET) plays a crucial role in hydrological and energy cycles, as well as in the assessments of water resources and irrigation demands. On a regional scale, particularly in the agro-pastoral ecotone, clarification of the distribution of surface ET and its influencing factors is critical for the rational use of water resources, restoration of the ecological environment, and protection of ecological water sources. The SEBAL model was used to invert the regional ET based on Landsat8 images in the agro-pastoral ecotone of northwest China. The results were indirectly verified by monitoring data from meteorological stations. The correlation between ET and surface parameters was analyzed. Thus, the main factors that affect the surface ET were identified. The results show that the SEBAL model determines an accurate inversion, with a correlation coefficient of 0.81 and an average root mean square error of 0.9 mm/d, which is highly suitable for research on water resources. The correlation coefficients of normalized vegetation index, surface temperature, land surface albedo, net radiation flux with daily ET were 0.5830, 0.8425, 0.3428 and 0.9111, respectively. The normalized vegetation index and the net radiation flux positively correlated with the daily ET, while the surface temperature and land surface albedo negatively correlated with the daily ET. The correlation from strong to weak is the net radiation flux > surface temperature > normalized vegetation index > surface albedo. In terms of spatial distribution, the daily ET of water was the highest, followed by woodland, wetland, cropland, built-up land, shrub land, grassland and bare land. However, the SEBAL model overestimates the inversion of daily ET of built-up land.
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Molekoa, Mmasabata, Ram Avtar, Pankaj Kumar, Huynh Thu Minh, Rajarshi Dasgupta, Brian Johnson, Netrananda Sahu, Ram Verma, and Ali Yunus. "Spatio-Temporal Analysis of Surface Water Quality in Mokopane Area, Limpopo, South Africa." Water 13, no. 2 (January 18, 2021): 220. http://dx.doi.org/10.3390/w13020220.
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Considering the well-documented impacts of land-use change on water resources and the rapid land-use conversions occurring throughout Africa, in this study, we conducted a spatiotemporal analysis of surface water quality and its relation with the land use and land cover (LULC) pattern in Mokopane, Limpopo province of South Africa. Various physico-chemical parameters were analyzed for surface water samples collected from five sampling locations from 2016 to 2020. Time-series analysis of key surface water quality parameters was performed to identify the essential hydrological processes governing water quality. The analyzed water quality data were also used to calculate the heavy metal pollution index (HPI), heavy metal evaluation index (HEI) and weighted water quality index (WQI). Also, the spatial trend of water quality is compared with LULC changes from 2015 to 2020. Results revealed that the concentration of most of the physico-chemical parameters in the water samples was beyond the World Health Organization (WHO) adopted permissible limit, except for a few parameters in some locations. Based on the calculated values of HPI and HEI, water quality samples were categorized as low to moderately polluted water bodies, whereas all water samples fell under the poor category (>100) and beyond based on the calculated WQI. Looking precisely at the water quality’s temporal trend, it is found that most of the sampling shows a deteriorating trend from 2016 to 2019. However, the year 2020 shows a slightly improving trend on water quality, which can be justified by lowering human activities during the lockdown period imposed by COVID-19. Land use has a significant relationship with surface water quality, and it was evident that built-up land had a more significant negative impact on water quality than the other land use classes. Both natural processes (rock weathering) and anthropogenic activities (wastewater discharge, industrial activities etc.) were found to be playing a vital role in water quality evolution. This study suggests that continuous assessment and monitoring of the spatial and temporal variability of water quality in Limpopo is important to control pollution and health safety in the future.
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Berhanu, Belete, and Ethiopia Bisrat. "Identification of Surface Water Storing Sites Using Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI)." Journal of Natural Resources and Development 8 (September 7, 2018): 91–100. http://dx.doi.org/10.5027/jnrd.v8i0.09.
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Ethiopia is endowed with water and has a high runoff generation area compared to many countries, but the total stored water only goes up to approximately 36BCM. The problem of water shortage in Ethiopia emanates from the seasonality of rainfall and the lack of infrastructure for storage to capture excess runoff during flood seasons. Based on this premise, a method for a syndicate use of topography, land use and vegetation was applied to locate potential surface water storing sites. The steady-state Topographic Wetness Index (TWI) was used to represent the spatial distribution of water flow and water stagnating across the study area and the Normalized Difference Vegetation Index (NDVI) was used to detect surface water through multispectral analysis. With this approach, a number of water storing sites were identified in three categories: primary sources (water bodies based), secondary sources (Swampy/wetland based) and tertiary sources (the land based). A sample volume analysis for the 120354 water storing sites in category two, gives a 44.92BCM potential storing capacity with average depth of 4 m that improves the annual storage capacity of the country to 81BCM (8.6 % of annual renewable water sources). Finally, the research confirmed the TWI and NDVI based approach for water storing sites works without huge and complicated earth work; it is cost effective and has the potential of solving complex water resource challenges through spatial representation of water resource systems. Furthermore, the application of remote sensing captures temporal diversity and includes repetitive archives of data, enabling the monitoring of areas, even those that are inaccessible, at regular intervals.
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Liuzzo, Lorena, Valeria Puleo, Salvatore Nizza, and Gabriele Freni. "Parameterization of a Bayesian Normalized Difference Water Index for Surface Water Detection." Geosciences 10, no. 7 (July 7, 2020): 260. http://dx.doi.org/10.3390/geosciences10070260.
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The normalized difference water index (NDWI) has been extensively used for different purposes, such as delineating and mapping surface water bodies and monitoring floods. However, the assessment of this index (based on multispectral remote sensing data) is highly affected by the effects of atmospheric aerosol scattering and built-up land, especially when green and near infrared bands are used. In this study, a modified version of the NDWI was developed to improve precision and reliability in the detection of water reservoirs from satellite images. The proposed equation includes eight different parameters. A Bayesian procedure was implemented for the identification of the optimal set of these parameters. The calculation of the index was based on Sentinel-2 satellite images of spectral bands collected over the 2015–2019 period. The modified NDWI was tested for the identification of small reservoirs in a subbasin of the Belice catchment in Sicily (southern Italy). To assess the effectiveness of the index, a reference image, representing the actual reservoirs in the study area, was used. The results suggested that the use of the proposed methodology for the parameterization of the modified NDWI improves the identification of water reservoirs with surfaces smaller than 0.1 ha.
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Rahman, Azbina, Viviana Maggioni, Xinxuan Zhang, Paul Houser, Timothy Sauer, and David M. Mocko. "The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model." Remote Sensing 14, no. 3 (January 18, 2022): 437. http://dx.doi.org/10.3390/rs14030437.
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This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.
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Mwangi, P. W., F. N. Karanja, P. K. Kamau, and S. C. Letema. "CONTRIBUTION INDEX OF LAND COVER AND LAND SURFACE TEMPERATURE CHANGES IN UPPER HILL NAIROBI, KENYA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021 (June 17, 2021): 141–49. http://dx.doi.org/10.5194/isprs-annals-v-3-2021-141-2021.
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Abstract. Urban heat island is the difference in thermal temperature between rural and urban areas. The urbanization process alters the material type with impervious surfaces being absorbers of incoming radiation during the day and emitting it at night. The research involved the use of time-series satellite imagery from Sentinel, Landsat, ASTER and MODIS for the period 1986, 1995, 2000, 2005, 2011, 2015 and 2017 over the Upper Hill, Nairobi. Morning, afternoon and night land surface temperatures (LST) were calculated for each of these years and analyzed together with the land cover. The mean albedo was calculated to determine the relationship between each land cover and mean LST. The contribution index was calculated to determine whether a land contributed positively or negatively to the mean LST in Upper Hill. Results indicated that built-up land cover had increased from 1986 to 2017 by 0.86% per annum while forest land cover had decreased by 0.99% per annum. Sparse grassland had higher albedo and LST values of 0.81 and 27.9 °C respectively, whereas water had lower albedo and LST values of 0.09 and 25.1 °C. Water had the lowest mean LST during the day but highest mean LST in the afternoon and night in each of the years due to its high thermal capacity. Bare ground tends to have a higher contribution index compared to other land covers, while forest land cover has a negative contribution index, indicating the impact land cover types have on LST and the urban heat island effect.
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Bala, R., R. Prasad, V. P. Yadav, and J. Sharma. "A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 389–94. http://dx.doi.org/10.5194/isprs-archives-xlii-5-389-2018.
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<p><strong>Abstract.</strong> The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body.</p>
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Hong, Zhiming, Wen Zhang, Changhui Yu, Dongying Zhang, Linyi Li, and Lingkui Meng. "SWCTI: Surface Water Content Temperature Index for Assessment of Surface Soil Moisture Status." Sensors 18, no. 9 (August 31, 2018): 2875. http://dx.doi.org/10.3390/s18092875.
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The vegetation supply water index (VSWI = NDVI/LST) is an effective metric estimating soil moisture in areas with moderate to dense vegetation cover. However, the normalized difference vegetation index (NDVI) exhibits a long water stress lag and the land surface temperature (LST), sensitive to water stress, does not contribute considerably to surface soil moisture monitoring due to the constraints of the mathematical characteristics of VSWI: LST influences VSWI less when LST value is sufficiently high. This paper mathematically analyzes the characteristics of VSWI and proposes a new operational dryness index (surface water content temperature index, SWCTI) for the assessment of surface soil moisture status. SWCTI uses the surface water content index (SWCI), which provides a more accurate estimation of surface soil moisture than that of NDVI, as the numerator and the modified surface temperature, which has a greater influence on SWCTI than that of LST, as the denominator. The validation work includes comparison of SWCTI with in situ soil moisture and other remote sensing indices. The results show SWCTI demonstrates the highest correlation with in situ soil moisture; the highest correlation R = 0.801 is found between SWCTI and the 0–5 cm soil moisture in a sandy loam. SWCTI is a functional and effective method that has a great potential in surface soil moisture monitoring.
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Youping, Shou, Zhao Junjie, and Qiao Jianzhe. "Analysis of eutrophication trend of surface water in Tianjin coastal area." E3S Web of Conferences 206 (2020): 03002. http://dx.doi.org/10.1051/e3sconf/202020603002.
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In this study, the concentration of COD, inorganic nitrogen（IN） and active phosphate(PO43-) in surface water of Tianjin coastal area in the spring and autumn of 2008, 2010, 2013 and 2016 were collected. The results showed that the COD concentration had no obvious change while the concentration of IN and PO43- had a significant decline after land reclamation projects. As for seasonal changes, COD concentration is generally higher in autumn than in spring, while the concentration of IN and PO43- is generally higher in spring than in autumn. As for eutrophication index (E), it is generally higher in spring than autumn. In the spring of 2008 and 2010, the eutrophication index (E) bigger than 3 showed moderate eutrophication of surface waters. Since the reclamation began in 2010, the eutrophication index (E) of the coastal waters has decreased sharply, the level of eutrophication is mild during the construction period. With the construction of the port area, large-scale dredging and backfilling, the land-based pollutants have been effectively controlled, and the water quality of Tianjin coastal waters has been improved.
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Aneesh, M. R., K. Mani, T. K. Prasad, and Higgins Robert. "Land use effect on water quality in a tropical river basin of Kerala, India." Geo Eye 8, no. 1 (June 15, 2019): 28–36. http://dx.doi.org/10.53989/bu.ge.v8i1.8.
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Water quality deterioration caused by land use changes has become a primary factor limiting the sustainable utilization of water resources. Rapid urbanization led to extensive land use changes which have a profound impact on surface water quality. This study is aimed to investigate the effect of land use on the water quality on a tropical river in Kerala, India. Water quality data for 20 stations were collected from Centre for Water Resources Development and Management (CWRDM), Kozhikode for 12 physicochemical parameters pertaining to three seasons namely pre-monsoon, monsoon and post-monsoon. All the stations were then field verified to identify the land use units to which it belongs to. The grouped data was then incorporated into three indices namely Water Quality Index (WQI), Water Pollution Index (WPI), and Water Pollutants Index to identify the effect of land uses on water quality and water pollution. Results indicated that urbanization has caused severe water quality deterioration compared to forests and settlement with mixed trees (SMT). Keywords: Urbanization; Kerala; WQI; WPI 1
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Marthews, T. R., S. J. Dadson, B. Lehner, S. Abele, and N. Gedney. "High-resolution global topographic index values for use in large-scale hydrological modelling." Hydrology and Earth System Sciences 19, no. 1 (January 7, 2015): 91–104. http://dx.doi.org/10.5194/hess-19-91-2015.
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Abstract. Modelling land surface water flow is of critical importance for simulating land surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL (TOPography-based hydrological MODEL), and the most important parameter of this model is the well-known topographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically conditioned HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) data using the GA2 algorithm (GRIDATB 2). At 15 arcsec resolution, these layers are 4 times finer than the resolution of the previously best-available topographic index layers, the compound topographic index of HYDRO1k (CTI). For the largest river catchments occurring on each continent we found that, in comparison with CTI our revised values were up to 20% lower in, e.g. the Amazon. We found the highest catchment means were for the Murray–Darling and Nelson–Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. For the majority of large catchments, however, the spread of our new GA2 index values is very similar to those of CTI, yet with more spatial variability apparent at fine scale. We believe these new index layers represent greatly improved global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.
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Chandrasekar, K., M. V. R. Sesha Sai, P. S. Roy, and R. S. Dwevedi. "Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product." International Journal of Remote Sensing 31, no. 15 (August 10, 2010): 3987–4005. http://dx.doi.org/10.1080/01431160802575653.
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Lakshmi, Venkat, Seungbum Hong, Eric E. Small, and Fei Chen. "The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing." Hydrology Research 42, no. 2-3 (April 1, 2011): 95–112. http://dx.doi.org/10.2166/nh.2011.071.
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The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.
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Wang, Guiling, Yeonjoo Kim, and Dagang Wang. "Quantifying the Strength of Soil Moisture–Precipitation Coupling and Its Sensitivity to Changes in Surface Water Budget." Journal of Hydrometeorology 8, no. 3 (June 1, 2007): 551–70. http://dx.doi.org/10.1175/jhm573.1.
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Abstract This paper presents a new index to quantify the strength of soil moisture–precipitation coupling in AGCMs and explores how the soil moisture–precipitation coupling in Community Atmosphere Model version 3 (CAM3)–Community Land Model version 3 (CAM3–CLM3) responds to parameterization-induced surface water budget changes. Specifically, this study (a) compares the regions of strong coupling identified by the newly proposed index and the index currently used in the Global Land–Atmosphere Coupling Experiment (GLACE); (b) examines how the surface water budget changes influence the strength of soil moisture–precipitation coupling as measured by the two indexes, respectively; and (c) examines how these changes influence the memory of the coupled land–atmosphere system as measured by the correlation between soil moisture and subsequent precipitation. The new index and the GLACE index are consistent in identifying central North America and West Africa as major regions of strong coupling during June–August (JJA). However, in some areas of western Europe and of subtropical South America where the GLACE index is low, the new index suggests a modest significant coupling during JJA. In response to the surface water budget changes that presumably favor a stronger soil moisture–precipitation coupling, the new index increases, but the GLACE index decreases in a majority of the regions of modest-to-strong coupling, although both show some mixed response. Changes in the land–atmosphere system memory suggest an increase of coupling strength, consistent with results from the new index. The strong dependence of the GLACE index on the relative importance of atmospheric internal variability is identified as a potential cause for the differences between the two indexes. The two indexes emphasize different aspects of soil moisture–precipitation coupling, and one might be more suitable than the other depending on the purpose of individual studies.
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He, Qing, Hui Lu, Kun Yang, L. Ruby Leung, Ming Pan, Jie He, and Panpan Yao. "A simple framework to characterize land aridity based on surface energy partitioning regimes." Environmental Research Letters 17, no. 3 (February 21, 2022): 034008. http://dx.doi.org/10.1088/1748-9326/ac50d4.
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Abstract Land aridity is often characterized by the aridity index (AI), which does not account for land surface water-energy interactions that are crucially important in determining regional climate. Such interactions can be captured by the evaporative fraction (EF, ratio of evapotranspiration to available energy) regimes. As EF is subject to energy and water limitations in humid and dry areas, respectively, EF regimes may be used to characterize land aridity to account for the influence of complex land characteristics and their impact on water availability. Here, we propose a simple framework to characterize land aridity by statistically ranking the coupling strength between EF and surface energy and water terms. The framework is demonstrated using gridded data and compared with AI over the U.S. and China. Results show that regionalization of aridity zones based on EF regimes and a two-tiered classification scheme may provide information such as surface energy and water variability complementary to the background aridity depicted by AI, with implications for extreme events.
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Liu, Chang, Jing Li, Qinhuo Liu, Baodong Xu, Yadong Dong, Jing Zhao, Faisal Mumtaz, Chenpeng Gu, and Hu Zhang. "Global Comparison of Leaf Area Index Products over Water-Vegetation Mixed Heterogeneous Surface Network (HESNet-WV)." Remote Sensing 15, no. 5 (February 27, 2023): 1337. http://dx.doi.org/10.3390/rs15051337.
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Spatial land surface heterogeneities are widespread at various scales and represent a great challenge to leaf area index (LAI) retrievals and product validations. In this paper, considering the mixed water and vegetation pixels prevalent at moderate and low resolutions, we propose a methodological framework for conducting global comparisons of heterogeneous land surfaces based on criterion setting and a global search of high-resolution data. We construct a global network, Heterogeneous Surface Network aiming Water and Vegetation Mixture (HESNet-WV), comprised of three vegetation types: grassland, evergreen broadleaf forests (EBFs), and evergreen needle forests (ENFs). Validation is performed using the MCD15A3H Global 500-m/4-day and GLASS 500-m/8-day LAI products. As the water area fraction (WAF), LAI values and LAI inversion errors increase in the MODIS and GLASS products, the GLASS product errors (relative LAI error (RELAI): 94.43%, bias: 0.858) are lower than the MODIS product errors (RELAI: 124.05%, bias: 1.209). The result indicates that the proposed framework can be applied to evaluate the accuracy of LAI values in mixed water-vegetation pixels in different global LAI products.
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Binarti, Floriberta, Pranowo Pranowo, and Soesilo Boedi Leksono. "Thermal Infrared Images to Identify the Contribution of Surface Materials to the Canopy Layer Heat Island in Hot-Humid Urban Areas." Environmental and Climate Technologies 24, no. 1 (January 1, 2020): 604–23. http://dx.doi.org/10.2478/rtuect-2020-0037.
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Abstract This study presents a combination technique of thermal infrared images captured by infrared camera and satellite thermal images retrieved from Landsat-8 OLI TIRS to identify the contribution of vertical and horizontal surface materials in two hot-humid street canyons with similar sky view factor and street orientation. The infrared camera captures surface temperature images of vertical and inclined surfaces of the street canyons. The images at horizontal scale are derived based on six land cover indices – i.e., Land Surface Temperature (LST), surface albedo, thermal emissivity, Normalized Different Vegetation Index (NDVI), Normalized Different Built Area Index (NDBI), Normalized Different Water Index (NDWI) – using an image processing technique conducted in ArcGIS. This study used two micro weather stations to measure microclimate conditions depicting the Canopy Layer Heat Island (CLHI) of the canyons at the same time. Despite the capability of the combined technique to identify the contribution of surface materials to the LST, different radiative and thermal properties of the surface materials insignificantly modified the CLHI.
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Han, Weixiao, Chunlin Huang, Hongtao Duan, Juan Gu, and Jinliang Hou. "Lake Phenology of Freeze-Thaw Cycles Using Random Forest: A Case Study of Qinghai Lake." Remote Sensing 12, no. 24 (December 15, 2020): 4098. http://dx.doi.org/10.3390/rs12244098.
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Lake phenology is essential for understanding the lake freeze-thaw cycle effects on terrestrial hydrological processes. The Qinghai-Tibetan Plateau (QTP) has the most extensive ice reserve outside of the Arctic and Antarctic poles and is a sensitive indicator of global climate changes. Qinghai Lake, the largest lake in the QTP, plays a critical role in climate change. The freeze-thaw cycles of lakes were studied using daily Moderate Resolution Imaging Spectroradiometer (MODIS) data ranging from 2000–2018 in the Google Earth Engine (GEE) platform. Surface water/ice area, coverage, critical dates, surface water, and ice cover duration were extracted. Random forest (RF) was applied with a classifier accuracy of 0.9965 and a validation accuracy of 0.8072. Compared with six common water indexes (tasseled cap wetness (TCW), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), automated water extraction index (AWEI), water index 2015 (WI2015) and multiband water index (MBWI)) and ice threshold value methods, the critical freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS), and break-up end (BUE) dates were extracted by RF and validated by visual interpretation. The results showed an R2 of 0.99, RMSE of 3.81 days, FUS and BUS overestimations of 2.50 days, and FUE and BUE underestimations of 0.85 days. RF performed well for lake freeze-thaw cycles. From 2000 to 2018, the FUS and FUE dates were delayed by 11.21 and 8.21 days, respectively, and the BUS and BUE dates were 8.59 and 1.26 days early, respectively. Two novel key indicators, namely date of the first negative land surface temperature (DFNLST) and date of the first positive land surface temperature (DFPLST), were proposed to comprehensively delineate lake phenology: DFNLST was approximately 37 days before FUS, and DFPLST was approximately 20 days before BUS, revealing that the first negative and first positive land surface temperatures occur increasingly earlier.
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Li, Lingcheng, Gautam Bisht, and L. Ruby Leung. "Spatial heterogeneity effects on land surface modeling of water and energy partitioning." Geoscientific Model Development 15, no. 14 (July 19, 2022): 5489–510. http://dx.doi.org/10.5194/gmd-15-5489-2022.
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Abstract. Understanding the influence of land surface heterogeneity on surface water and energy fluxes is crucial for modeling earth system variability and change. This study investigates the effects of four dominant heterogeneity sources on land surface modeling, including atmospheric forcing (ATM), soil properties (SOIL), land use and land cover (LULC), and topography (TOPO). Our analysis focused on their impacts on the partitioning of precipitation (P) into evapotranspiration (ET) and runoff (R), partitioning of net radiation into sensible heat and latent heat, and corresponding water and energy fluxes. An initial set of 16 experiments were performed over the continental US (CONUS) using the E3SM land model (ELMv1) with different combinations of heterogeneous and homogeneous datasets. The Sobol' total and first-order sensitivity indices were utilized to quantify the relative importance of the four heterogeneity sources. Sobol' total sensitivity index measures the total heterogeneity effects induced by a given heterogeneity source, consisting of the contribution from its own heterogeneity (i.e., the first-order index) and its interactions with other heterogeneity sources. ATM and LULC are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning, mainly contributed by their own heterogeneity and slightly contributed by their interactions with other heterogeneity sources. Their heterogeneity effects are complementary, both spatially and temporally. The overall impacts of SOIL and TOPO are negligible, except TOPO dominates the spatial variability of R/P across the transitional climate zone between the arid western and humid eastern CONUS. Accounting for more heterogeneity sources improves the simulated spatial variability of water and energy fluxes when compared with ERA5-Land reanalysis dataset. An additional set of 13 experiments identified the most critical components within each heterogeneity source, which are precipitation, temperature, and longwave radiation for ATM, soil texture, and soil color for SOIL and maximum fractional saturated area parameter for TOPO.
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Pei, Liang, Chunhui Wang, Yiping Zuo, Xiaojie Liu, and Yanyan Chi. "Impacts of Land Use on Surface Water Quality Using Self-Organizing Map in Middle Region of the Yellow River Basin, China." International Journal of Environmental Research and Public Health 19, no. 17 (September 2, 2022): 10946. http://dx.doi.org/10.3390/ijerph191710946.
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The Yellow River is one of the most important water sources in China, and its surrounding land use affected by human activities is an important factor in water quality pollution. To understand the impact of land use types on water quality in the Sanmenxia section of the Yellow River, the water quality index (WQI) was used to evaluate the water quality. A self-organizing map (SOM) was used for clustering analysis of water quality indicators, and the relationship between surface water quality and land use types was further analyzed by redundancy analysis (RDA). The results showed that WQI values ranged from 82.60 to 507.27, and the highest value was the sampling site S3, whose water quality grade was “Likely not suitable for drinking”, mainly polluted by agricultural non-point sources ammonia nitrogen pollution. SOM clustered the sampling sites into 4 groups according to the water quality indicators, the main influencing factors for different groups were analyzed and explored in more depth in relation to land use types, suggesting that surface water quality was significantly connected with the proportion of land use types at the watershed scale in the interpretation of water quality change. The negative impact of cropland on surface water quality was greater than that of other land use types, and vegetation showed a greater positive impact on surface water quality than other land uses. The results provide evidence for water environment conservation based on land use in the watershed.
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Ma, Weiqiang, Yaoming Ma, Maoshan Li, Zeyong Hu, Lei Zhong, Zhongbo Su, Hirohiko Ishikawa, and Jiemin Wang. "Estimating surface fluxes over the north Tibetan Plateau area with ASTER imagery." Hydrology and Earth System Sciences 13, no. 1 (January 26, 2009): 57–67. http://dx.doi.org/10.5194/hess-13-57-2009.
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Abstract. Surface fluxes are important boundary conditions for climatological modeling and Asian monsoon system. The recent availability of high-resolution, multi-band imagery from the ASTER (Advanced Space-borne Thermal Emission and Reflection radiometer) sensor has enabled us to estimate surface fluxes to bridge the gap between local scale flux measurements using micrometeorological instruments and regional scale land-atmosphere exchanges of water and heat fluxes that are fundamental for the understanding of the water cycle in the Asian monsoon system. A parameterization method based on ASTER data and field observations has been proposed and tested for deriving surface albedo, surface temperature, Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), vegetation coverage, Leaf Area Index (LAI), net radiation flux, soil heat flux, sensible heat flux and latent heat flux over heterogeneous land surface in this paper. As a case study, the methodology was applied to the experimental area of the Coordinated Enhanced Observing Period (CEOP) Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet), located at the north Tibetan Plateau. The ASTER data of 24 July 2001, 29 November 2001 and 12 March 2002 was used in this paper for the case of summer, winter and spring. To validate the proposed methodology, the ground-measured surface variables (surface albedo and surface temperature) and land surface heat fluxes (net radiation flux, soil heat flux, sensible heat flux and latent heat flux) were compared to the ASTER derived values. The results show that the derived surface variables and land surface heat fluxes in three different months over the study area are in good accordance with the land surface status. Also, the estimated land surface variables and land surface heat fluxes are in good accordance with ground measurements, and all their absolute percentage difference (APD) is less than 10% in the validation sites. It is therefore concluded that the proposed methodology is successful for the retrieval of land surface variables and land surface heat fluxes using the ASTER data and filed observation over the study area.
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Riad Morshed, Syed Riad Morshed, Md Abdul Fattah, Asma Amin Rimi, and Md Nazmul Haque. "SURFACE TEMPERATURE DYNAMICS IN RESPONSE TO LAND COVER TRANSFORMATION." Journal of Civil Engineering, Science and Technology 11, no. 2 (September 30, 2020): 94–110. http://dx.doi.org/10.33736/jcest.2616.2020.
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This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand, the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is the transformation of land cover in buildup areas.
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Lei, Baojie, Kim Myung-Soo, and Nurjahan. "Prediction of the Impact of Land Usage Changes on Water Pollution in Public Space Planning with Machine Learning." Mathematical Problems in Engineering 2022 (May 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/6276909.
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In urban public space planning, changes in land use, structure, and construction impact the urban environment to a certain degree. Land usage changes the urban surface water environment by impacting it through numerous ways. This paper studies about prediction of land use changes on surface water pollution in public space planning. This paper analyzes the characteristics of land use changes in public space planning from the quantitative characteristics of land use types, land use structure characteristics, and land usage degree in different years. The protection of natural resources is important, and water is one of the most important natural resources consumed by human beings. The environmental changes impacting these natural resources are to be studied to preserve the natural resources. The prediction of over-consumption of natural resources using soft computing techniques can certainly provide a solution for appropriate decision making. The prediction of relationship between land use changes and surface water pollution is required. In order to achieve this, the regression analysis on land use changes of different spatial scales with four surface water pollution indicators in the dry and wet seasons is performed to obtain the regression of each water pollution indicator. According to the determination coefficient, the determination coefficient of the model uses the comprehensive pollution index method to predict the impact of land use changes on surface water pollution. The experimental results show that the prediction accuracy of the proposed method is high and it is helpful in studying the impact of land use change on surface water pollution. It can help in decision making on consumption of natural resources to preserve the natural resources for next generations.
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Imran, H. M., Anwar Hossain, A. K. M. Saiful Islam, Ataur Rahman, Md Abul Ehsan Bhuiyan, Supria Paul, and Akramul Alam. "Impact of Land Cover Changes on Land Surface Temperature and Human Thermal Comfort in Dhaka City of Bangladesh." Earth Systems and Environment 5, no. 3 (July 7, 2021): 667–93. http://dx.doi.org/10.1007/s41748-021-00243-4.
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AbstractUrbanization leads to the construction of various urban infrastructures in the city area for residency, transportation, industry, and other purposes, which causes major land use change. Consequently, it substantially affects Land Surface Temperature (LST) by unbalancing the surface energy budget. Higher LST in city areas decreases human thermal comfort for the city dwellers and affects the urban environment and ecosystem. Therefore, a comprehensive investigation is needed to evaluate the impact of land use change on the LST. Remote Sensing (RS) and Geographic Information System (GIS) techniques were used for the detailed investigation. RS data for the years 1993, 2007 and 2020 during summer (March–May) in Dhaka city were used to prepare land cover maps, analyze LST, generate hazard maps and relate the land cover change with LST by using GIS. The results show that the built-up area in Dhaka city increased by 67% from 1993 to 2020 by replacing lowland mainly, followed by vegetation, bare soil and water bodies. LSTs found in the study area were ranged from 23.26 to 39.94 °C, 23.69 to 43.35 °C and 24.44 to 44.58 °C for the years 1993, 2007 and 2020, respectively. The increases of spatially distributed maximum and mean LST were found 4.62 °C and 6.43 °C, respectively, for the study period of 27 years while the change in minimum LST was not substantial. LST increased by around 0.24 °C per year and human thermal discomfort shifted from moderate to strong heat stress for the total study period due to the increase of built-up and bare lands. This study also shows that normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were negatively correlated with LST while normalized difference built-up Index (NDBI) and normalized difference built-up Index (NDBAI) were positively correlated with LST. The methodology developed in this study can be adapted to other cities around the globe.
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Poedjiastoeti, Hermin, Sudarmadji Sudarmadji, Sunarto Sunarto, and Slamet Suprayogi. "Penilaian Kerentanan Air Permukaan terhadap Pencemaran di Sub DAS Garang Hilir Berbasis Multi-Indeks." Jurnal Wilayah dan Lingkungan 5, no. 3 (December 29, 2017): 167. http://dx.doi.org/10.14710/jwl.5.3.167-180.
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Assessing the surface water vulnerability to pollution in the Garang Downstream Watershed Semarang requires a study concerned with some environmental components/indicators. Vulnerability measurement through surface water susceptibility index formulation on pollution is important considering the absence of surface water pollution effect indicators in an efficient assessment system. Therefore, a multi-indicator vulnerability assessment on surface water pollution is necessary. The Surface Water Vulnerability Index to Pollution (SWVIP) is composed of five components, namely water quality (WQ), rainfall (R), land use and vegetation cover (LVC), river hydrogeometric (RH) and population (P). Regarding index development, the subindex graphs and the weighting of each component are created. The application of composite index measurement yields an equation of SWVIP = 0.29.WQI + 0.23PI + 0.14RI + 0.20.LVCI + 0.14.RHI and an index value of 73.87 including the "rather high" category that represents the "vulnerable"condition in the Garang Downstream Watershed Semarang. This suggests that the five selected components used in the index creation can provide useful information to decision making in the surface water pollution control.
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Poedjiastoeti, Hermin, Sudarmadji Sudarmadji, Sunarto Sunarto, and Slamet Suprayogi. "Penilaian Kerentanan Air Permukaan terhadap Pencemaran di Sub DAS Garang Hilir Berbasis Multi-Indeks." Jurnal Wilayah dan Lingkungan 5, no. 3 (December 29, 2017): 168. http://dx.doi.org/10.14710/jwl.5.3.168-180.
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Assessing the surface water vulnerability to pollution in the Garang Downstream Watershed Semarang requires a study concerned with some environmental components/indicators. Vulnerability measurement through surface water susceptibility index formulation on pollution is important considering the absence of surface water pollution effect indicators in an efficient assessment system. Therefore, a multi-indicator vulnerability assessment on surface water pollution is necessary. The Surface Water Vulnerability Index to Pollution (SWVIP) is composed of five components, namely water quality (WQ), rainfall (R), land use and vegetation cover (LVC), river hydrogeometric (RH) and population (P). Regarding index development, the subindex graphs and the weighting of each component are created. The application of composite index measurement yields an equation of SWVIP = 0.29.WQI + 0.23PI + 0.14RI + 0.20.LVCI + 0.14.RHI and an index value of 73.87 including the "rather high" category that represents the "vulnerable"condition in the Garang Downstream Watershed Semarang. This suggests that the five selected components used in the index creation can provide useful information to decision making in the surface water pollution control.
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Deng, Sihe, Cheng Li, Xiaosan Jiang, Tingting Zhao, and Hui Huang. "Research on Surface Water Quality Assessment and Its Driving Factors: A Case Study in Taizhou City, China." Water 15, no. 1 (December 21, 2022): 26. http://dx.doi.org/10.3390/w15010026.
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It is necessary to assess and analyze the factors that influence surface water since they are crucial to human activities such as agriculture, raising livestock, and industry. Previous research has mostly focused on how land use and landscape patterns affect the quality of surface waters; it has seldom addressed the industrial and agricultural production activities that are directly connected to human society. Therefore, the research area’s surface water quality was assessed by single factor index (SFI) and composite water quality index (WQI), divided into flood and non-flood periods, and water quality indicators with severe pollution and significant seasonal variations were selected; A total of 28 indicators were selected from three main factors-topography, socio-economic, and land use type-and analyzed using the Spearman correlation coefficient model. (1) SFI data reveal substantial seasonal changes in pH, DO, NH3-N, TN, and TP water quality indicators. The well-developed agricultural and aquaculture in the studied region is the primary cause of the excess TN and NH3-N concentrations; (2) The sample points’ water quality index (WQI) scores range from 50 to 80, with 62% of them having “medium” water quality; (3) The study area’s seasonal variation in water quality is primarily caused by human socio-economic activities (GDP, industrial effluent discharge, COD discharge, aquatic product quality, and the proportion of primary, secondary, and tertiary industries), as well as land use type (forest, shrubland, and cropland). Topography has little effect on the study area’s surface water quality. This study offers a fresh viewpoint on surface water quality management and driver analysis, and a new framework for managing and safeguarding aquatic ecosystems.
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Sun, Zishu, Zhigang Li, and Jialong Zhong. "Analysis of the Impact of Landscape Patterns on Urban Heat Islands: A Case Study of Chengdu, China." International Journal of Environmental Research and Public Health 19, no. 20 (October 15, 2022): 13297. http://dx.doi.org/10.3390/ijerph192013297.
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The urbanization process, such as population growth and the expansion of roads, railways, residential areas, and industrial areas, causes severe landscape fragmentation and changes in the surface temperature balance, resulting in the heat island effect. This study used Landsat data to study the impact of landscape patterns on urban heat islands (UHIs) and temporal-spatial change characteristics. In addition, spatial correlation analysis was employed to detect the relationships between land surface temperature (LST) and landscape patterns. The results showed that the impervious surfaces landscape area increased significantly, and the Woodland landscape area increased. However, the bare land, cropland, and water body area decreased. The cohesion of cropland and woodland landscape in the suburb decreased, and there was a high degree of fragmentation. The difference between the contributions of the central city and suburbs to the whole region is narrowing, and the expansion of urban heat islands is shifting from the central city to the suburbs. The percentage of landscape index (PLAND) and the patch cohesion index (COHESION) of woodland, water body, and cropland were negatively correlated with LST. Meanwhile, the PLAND and COHESION of impervious surface and bare land were positively correlated with LST, and the splitting index (SPLIT) was the opposite of the PLAND and COHESION. The fragmentation of impervious surfaces and bare land landscapes reduces the UHI effect. Based on these results, countermeasures to mitigate the heat island effect are proposed. These measures will play an essential role in improving urban ecology and the environmental quality of human settlements.
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Hu, Xiaolong, Liangsheng Shi, Lin Lin, and Yuanyuan Zha. "Nonlinear boundaries of land surface temperature–vegetation index space to estimate water deficit index and evaporation fraction." Agricultural and Forest Meteorology 279 (December 2019): 107736. http://dx.doi.org/10.1016/j.agrformet.2019.107736.
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Aslan, Nagihan, and Dilek Koc-San. "The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries." ISPRS International Journal of Geo-Information 10, no. 6 (June 16, 2021): 416. http://dx.doi.org/10.3390/ijgi10060416.
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The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.
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Singh, Sheilja, and Rabidyuti Biswas. "Analysis of Land Use Change Effects/Impacts on Surface Water Resources in Delhi." Urban Science 6, no. 4 (December 7, 2022): 92. http://dx.doi.org/10.3390/urbansci6040092.
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Rapid urbanization and haphazard development derive the changes in land uses and affect the naturally available resources which are essential for human development and other lives. Land use changes can undermine the environment and ecology of an urban area. Although many studies on the land use changes, trends, status, directions, and the relationship between them have been conducted for Chinese cities, none of them have been completed for Indian cities and also not for NCT Delhi. The aim of the study is to analyze the impact of land use changes on surface water resources. So, this study aims to analyze the effects of land use changes on surface water resources in NCT Delhi, one water-stressed city in India. The analysis is comprised of changes, trends, status, and directions for surface water resources and other types of land use for showing the effects. Comprehensive tools such as remote sensing, GIS, and the cross-tabulation method are used for the assessment of land use changes, trends, and status. Four decadal (1990, 2000, 2010, 2020) satellite maps have been used to study the temporal-spatial data of several land uses and to calculate the index of land use changes for investigating the trends and status. In the form of results, the comprehensive net change (18.28%) and total change (49.28%) with a trend value of 0.37 show the quasi-balanced, two-way transition and positive changes in the whole area. This metrics-based study shows that surface water resources land use type is decreasing, and built-up land use type is increasing since 1990. Population growth, economic and industrial development were the major factors for the variations in built-up, green, and other land uses. This metrics-based analysis study is an important perspective for protecting urban water bodies from effects of land use changes. These understandings on land use changes and temporal-spatial relationships are important for present and future land use development and surface water resource planning. This study will help the Delhi Government’s initiatives for the rejuvenation of urban water bodies by endorsing the land use regulations on surrounding land uses.
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Kolanuvada, Srinivasa Raju, and Sivkumar S. "Assessing Adverse Impacts of Aquaculture Activities on Groundwater Quality in Ponneri Taluk of Tiruvallur District, Tamilnadu using Drinking Water Quality Index." Research Journal of Chemistry and Environment 27, no. 2 (January 15, 2023): 73–80. http://dx.doi.org/10.25303/2702rjce073080.
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Tamilnadu is a State that is deprived of perennial water sources like major rivers and streams. Agricultural activities in Tamilnadu are mainly dependent on seasonal rainfall and ground water. The shortages of water resources drive many coastal farmers to convert their fertile agricultural lands to aquaculture. Groundwater suitability for human consumption is determined by its physical, chemical and bacteriological properties. Irrational conversion of agricultural lands to aquaculture is found to be unsustainable leading to wastage of sparse natural resource fertile land and groundwater. The aquaculture practices started very extensively since year 2012 where fertile agriculture land, land with scrub and wasteland area have been converted for the aquaculture ponds. The use of temporal remote sensing helps in understanding the patterns of land use conversion from agricultural practice to aquaculture. In addition, indiscriminate use of fertilizers and chemicals in aquaculture farming has led to soil salinization and ground water pollution in many parts of study area. Use of salinity indices was tried in identification of salinity levels in lands in and around the current and abandoned aquaculture ponds. The study indicates that the use of Remote Sensing and GIS has helped in identification of landuse conversion and its adverse effects on characteristics of land due to extensive aquaculture activities in Ponneri Taluk of Tamilnadu. Using satellite data of Landsat OLI 8 (2015), IRS LISS IV, Sentinel II 2017, the changes have been estimated. The water quality parameters include pH, TDS, cations such as Ca, Mg, Na and K, anions such as Cl, SO4, HCO3, NO3 and PO4. Surface and groundwater quality results are cross-checked at the field areas which clearly indicate that almost entire Ponneri Taluk groundwater is unsuitable for drinking purposes. The classification for drinking water is obtained by DWQI. The piper plot has been studied by the geochemistry methods.
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Mohammadi, Amir Mansour, Mehdi Vafakhah, and Mohammad Reza Javadi. "The Relationship between Surface Water Quality and Watershed Characteristics." Journal of Civil Engineering and Construction 8, no. 3 (August 15, 2019): 107–11. http://dx.doi.org/10.32732/jcec.2019.8.3.107.
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The healthy water resources are necessary and essential prerequisite for environmental protection and economic development, political, social and cultural rights of Iran. In this research, water quality parameters i.e. total dissolved solids (TDS), sodium absorption rate (SAR), electrical conductivity (EC), Na+, Cl-, CO32-, K+, Mg2+, Ca2+, pH, HCO3- and SO42- during 2010-2011 were obtained from Iranian Water Resources Research Institute in water quality measurement stations on Mazandaran province, Iran. Then, the most important catchment characteristics (area, mean slope, mean height, base flow index, annual rainfall, land cover, and geology) were determined on water quality parameters using stepwise regression via backwards method in the 63 selected rivers. The results showed that sodium absorption rate (SAR), total dissolved solids (TDS), electrical conductivity (EC), Na+ and Cl- parameters are strongly linked to geology characteristics, while K+, Mg2+ and Ca2+ cations is linked to rainfall and geology characteristics. pH and HCO3- are related to area, rainfall, land cover and geology characteristics, CO32- is related to area, rainfall, rangeland area and geology characteristics and SO42- is related to area, rainfall, range and bar land area and geology characteristics. Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for modeling the selected catchment characteristics and water quality parameters. The ANFIS models have a high Nash–Sutcliffe model efficiency coefficient (NSE) and low root mean squares error (RMSE) to estimate water quality parameters.
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Chandrasekar, K., M. V. R. Sesha Sai, and G. Behera. "ASSESSMENT OF EARLY SEASON AGRICULTURAL DROUGHT THROUGH LAND SURFACE WATER INDEX (LSWI) AND SOIL WATER BALANCE MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-8/W20 (August 31, 2012): 50–55. http://dx.doi.org/10.5194/isprsarchives-xxxviii-8-w20-50-2011.
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Mushore, Terence Darlington, John Odindi, and Onisimo Mutanga. "Controls of Land Surface Temperature between and within Local Climate Zones: A Case Study of Harare in Zimbabwe." Applied Sciences 12, no. 24 (December 13, 2022): 12774. http://dx.doi.org/10.3390/app122412774.
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Urban growth-related changes in land use and land cover have segmented urban areas into zones of distinct surface and air temperatures (i.e., Local Climate Zones—LCZ). While studies have revealed inter-LCZ temperature variations, understanding controls of variations in Land Surface Temperature (LST) within LCZs has largely remained uninvestigated. In view of the need for LCZ-specific heat mitigation strategies, this study investigated factors driving LST variations within LCZs. To achieve this, an LCZ map for Harare was developed and correlated with LST, both derived using Landsat 8 data. The contribution index (CI) was then used to determine the relative contribution of LCZs to cooling and warming of the city. The contribution of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Urban Index (UI), and Aspect and Elevation as quantitative measures of surface controls of LST were investigated between and within LCZs. LST generally increased with built-up density and reduced with increases in surface water and vegetation. The study showed that the cooling effect of water bodies was reduced in contribution to their insignificant proportion of the study area. At the city scale, NDVI, MNDWI, NDBI, and UI had the strongest influence on LST (correlation coefficient > 0.5). At the intra-LCZ scale, the contribution of these surface properties remained significant, though to varied extents. The study concluded that surface wetness is a significant cooling determinant in densely built-up LCZs, while in other LCZs, it combines with vegetation abundance and health to mitigate elevated surface temperature. Aspect and elevation had low but significant correlations with LST in most LCZs. The study recommends that intra-LCZ controls of LST must be considered in heat mitigation efforts.
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Deng, Yawen, Weiguo Jiang, Zhifeng Wu, Ziyan Ling, Kaifeng Peng, and Yue Deng. "Assessing Surface Water Losses and Gains under Rapid Urbanization for SDG 6.6.1 Using Long-Term Landsat Imagery in the Guangdong-Hong Kong-Macao Greater Bay Area, China." Remote Sensing 14, no. 4 (February 12, 2022): 881. http://dx.doi.org/10.3390/rs14040881.
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As one of the most open and dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has been urbanizing rapidly in recent decades. The surface water in the GBA also has been suffering from urbanization and intensified human activities. The study aimed to characterize the spatiotemporal patterns and assess the losses and gains of surface water caused by urbanization in the GBA via long time-series remote sensing data, which could support the progress towards sustainable development goals (SDGs) set by the United Nations, especially for measuring SDG 6.6.1 indicator. Firstly, utilizing 4750 continuous Landsat TM/ETM+/OLI images during 1986–2020 and the Google Earth Engine cloud platform, the multiple index water detection rule (MIWDR) was performed to extract surface water extent in the GBA. Secondly, we achieved surface water dynamic type classification based on annual water inundation frequency time-series in the GBA. Finally, the spatial distribution and temporal variation of urbanization-induced water losses and gains were analyzed through a land cover transfer matrix. Results showed that (1) the average minimal and maximal surface water extents of the GBA during 1986–2020 were 2017.62 km2 and 6129.55 km2, respectively. The maximal surface water extent fell rapidly from 7897.96 km2 in 2001 to 5087.46 km2 in 2020, with a loss speed of 155.41 km2 per year (R2 = 0.86). (2) The surface water areas of permanent and dynamic types were 1529.02 km2 and 2064.99 km2 during 2000–2020, accounting for 42.54% and 57.46% of all water-related areas, respectively. (3) The surface water extent occupied by impervious land surfaces showed a significant linear downward trend (R2 = 0.98, slope = 36.41 km2 per year), while the surface water restored from impervious land surfaces denoted a slight growing trend (R2 = 0.86, slope = 0.99 km2 per year). Our study monitored the long-term changes in the surface water of the GBA, which can provide valuable information for the sustainable development of the GBA urban agglomeration. In addition, the proposed framework can easily be implemented in other similar regions worldwide.
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Bach, H., M. Braun, G. Lampart, and W. Mauser. "Use of remote sensing for hydrological parameterisation of Alpine catchments." Hydrology and Earth System Sciences 7, no. 6 (December 31, 2003): 862–76. http://dx.doi.org/10.5194/hess-7-862-2003.
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Abstract. Physically-based water balance models require a realistic parameterisation of land surface characteristics of a catchment. Alpine areas are very complex with strong topographically-induced gradients of environmental conditions, which makes the hydrological parameterisation of Alpine catchments difficult. Within a few kilometres the water balance of a region (mountain peak or valley) can differ completely. Hence, remote sensing is invaluable for retrieving hydrologically relevant land surface parameters. The assimilation of the retrieved information into the water balance model PROMET is demonstrated for the Toce basin in Piemonte/Northern Italy. In addition to land use, albedos and leaf area indices were derived from LANDSAT-TM imagery. Runoff, modelled by a water balance approach, agreed well with observations without calibration of the hydrological model. Keywords: PROMET, fuzzy logic based land use classification, albedo, leaf area index
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Späth, Florian, Verena Rajtschan, Tobias K. D. Weber, Shehan Morandage, Diego Lange, Syed Saqlain Abbas, Andreas Behrendt, Joachim Ingwersen, Thilo Streck, and Volker Wulfmeyer. "The land–atmosphere feedback observatory: a new observational approach for characterizing land–atmosphere feedback." Geoscientific Instrumentation, Methods and Data Systems 12, no. 1 (January 25, 2023): 25–44. http://dx.doi.org/10.5194/gi-12-25-2023.
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Abstract. Important topics in land–atmosphere (L–A) feedback research are water and energy balances and heterogeneities of fluxes at the land surface and in the atmospheric boundary layer (ABL). To target these questions, the Land–Atmosphere Feedback Observatory (LAFO) has been installed in southwestern Germany. The instrumentation allows comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped into three components, atmosphere, soil and land surface, and vegetation, the LAFO observation strategy aims for simultaneous measurements in all three compartments. For this purpose the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables of humidity, temperature and wind. At the land surface, eddy covariance stations are operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. Together with a water and temperature sensor network, the soil water content and temperature are monitored in the agricultural investigation area. As for vegetation, crop height, leaf area index and phenological growth stage values are registered. The observations in LAFO are organized into operational measurements and intensive observation periods (IOPs). Operational measurements aim for long time series datasets to investigate statistics, and we present as an example the correlation between mixing layer height and surface fluxes. The potential of IOPs is demonstrated with a 24 h case study using dynamic and thermodynamic profiles with lidar and a surface layer observation that uses the scanning differential absorption lidar to relate atmospheric humidity patterns to soil water structures. Both IOPs and long-term observations will provide new insight into exchange processes and their statistics for improving the representation of L–A feedbacks in climate and numerical weather prediction models. The lidar component in particular will support the investigation of coupling to the atmosphere.
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Sharmin Siddika, Md Nazmul Haque, and Mizbah Ahmed Sresto. "Assessing the Relationship among the Land Surface Features: A Geographic Information System (GIS) and Remote Sensing (RS) Based Approach for City Area." Journal of Applied Science & Process Engineering 8, no. 2 (October 31, 2021): 935–52. http://dx.doi.org/10.33736/jaspe.3616.2021.
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Due to climate change and urbanization, it is important to monitor and evaluate the components of the environment. For this reason, ward-22 and ward-27 of the Khulna City Corporation (KCC) area have been selected for the study. This research seeks to identify the existing land use profile and assess the land surface components such as topography, Normalized Difference Buildup Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Salinity Index (NDSI) and Land Surface Temperature (LST) to measure the relationships among the land surface components. The land use land cover map shows that about 59% of ward-22 and 71.5% area of ward-27 are built-up areas. Both of the wards contain little amount of water body, vegetation and open space. Both of the wards have residential land use types with commercial purposes on the periphery. Accordingly, 63.32% and 65% of structures of ward-22 and 27 are pucca. The land surface components reveal that both areas contain lower slopes, less vegetation, less moisture, severe salinity, highly built-up areas, and high land surface temperature. The relationships among the land surface components show that NDVI has a negative relation with LST and NDBI whereas NDVI represents a positive correlation with NDMI. On the other hand, NDBI shows a positive correlation with LST whereas NDMI negatively correlates with LST. NDSI and topography reflect no meaningful relationship between NDBI, NDVI, LST, and NDMI. However, the research findings may be essential to city planners and decision-makers for incorporating better urban management at the micro level concerning climate change.
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Abu-Hashim, Mohamed, Ahmed Sayed, Martina Zelenakova, Zuzana Vranayová, and Mohamed Khalil. "Soil Water Erosion Vulnerability and Suitability under Different Irrigation Systems Using Parametric Approach and GIS, Ismailia, Egypt." Sustainability 13, no. 3 (January 20, 2021): 1057. http://dx.doi.org/10.3390/su13031057.
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Preserving the sustainable agriculture concept requires identifying the plant response to the water regime and rationing the water for irrigation. This research compares different irrigation designs coupled with a parametric evaluation system on soil water erosion and soil suitability to assess the sites vulnerable to soil erosion based on a soil water erosion model (ImpelERO) in an area of 150.0 hectares, Ismailia Governorate, Egypt. Land suitability maps are prepared using the Geographic Information System (GIS), and the soil properties are analyzed and evaluated for the different surface, sprinkler, and drip irrigation methods. The results show that the sprinkler and drip irrigation strategies are more practical irrigation methods and additional environment friendly than surface irrigation for enhancing land productivity. Moreover, the principle acumen for creating use of the surface irrigation on this space is for lowering the soil salinity. Land capability index for surface irrigation ranges from 20.5 to 72.2% (permanently not suitable N2 to moderately suitable S2); and the max capability index (Ci) for drip irrigation was 81.3% (highly suitable-S1), while the mean capability index (Ci) was 42.87% (Currently not suitable-NI). The land suitability of the study area using sprinkler irrigation was ranked as highly suitable (S1), moderately suitable (S2), marginally suitable (S3), and currently not suitable (N1). Thus, the obtained data indicated that applying drip irrigation (trickle irrigation) was the most efficient system compared to the sprinkle and surface irrigation systems. To identify the soil, water erosion vulnerability, and soil optimal management strategies for the agricultural parcel in that region, the ImpelERO model (soil erosion vulnerability/impact/management) was applied. Erosion risk classes ranged from V2 (small) to V3 (moderate), that that region categorized as small-sensitive to water erosion by alfalfa, to moderate-sensitive to water erosion by olive. The results of soil losses varied from 7.1 to 37.9 t ha−1 yr−1 with an average of 17.7 t ha−1 yr−1. Thus, guarantee efficient water use and soil suitability for food production in the future will require the use of an efficient irrigation system.
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Guha, Subhanil, and Himanshu Govil. "A seasonal relationship between land surface temperature and normalized difference bareness index." South African Journal of Geomatics 10, no. 2 (September 4, 2022): 163–80. http://dx.doi.org/10.4314/sajg.v10i2.12.
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The present study analyzes the seasonal variability of the relationship between the land surface temperature (LST) and normalized difference bareness index (NDBaI) on different land use/land cover (LULC) in Raipur City, India by using sixty-five Landsat images of four seasons (pre-monsoon, monsoon, post-monsoon, and winter) of 1991-1992, 1995-1996, 1999-2000, 2004-2005, 2009-2010, 2014-2015, and 2018-2019. The results show that the post-monsoon season indicates the best correlation (0.59), followed by the monsoon (0.56), pre-monsoon (0.47), and winter (0.44) season. The water bodies reflect a strongly positive correlation in all the four seasons (0.65 in pre-monsoon, 0.51 in monsoon, 0.53 in post-monsoon, and 0.62 in winter). On green vegetation, this correlation is also strongly positive in monsoon (0.57), post-monsoon (0.62), and winter (0.55), whereas it is moderate positive in pre-monsoon (0.37) season. The built-up area and bare land build a moderate positive correlation in all the four seasons (0.35 in pre-monsoon, 0.43 in monsoon, 0.48 in post-monsoon, and 0.39 in winter). Among the four seasons, the post-monsoon season builds the best correlation for all LULC types, whereas the pre-monsoon season has the least correlation. This research work is beneficial for land use and environmental planning of any city under similar climatic conditions.
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Zhang, Mingxi, Guangzhi Rong, Aru Han, Dao Riao, Xingpeng Liu, Jiquan Zhang, and Zhijun Tong. "Spatial-Temporal Change of Land Use and Its Impact on Water Quality of East-Liao River Basin from 2000 to 2020." Water 13, no. 14 (July 16, 2021): 1955. http://dx.doi.org/10.3390/w13141955.
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Land use change is an important driving force factor affecting the river water environment and directly affecting water quality. To analyze the impact of land use change on water quality change, this study first analyzed the land use change index of the study area. Then, the study area was divided into three subzones based on surface runoff. The relationship between the characteristics of land use change and the water quality grade was obtained by grey correlation analysis. The results showed that the land use types changed significantly in the study area since 2000, and water body and forest land were the two land types with the most significant changes. The transfer rate is cultivated field > forest land > construction land > grassland > unused land > water body. The entropy value of land use information is represented as Area I > Area III > Area II. The shift range of gravity center is forest land > grassland > water body > unused land > construction land > cultivated field. There is a strong correlation between land use change index and water quality, which can be improved and managed by changing the land use type. It is necessary to establish ecological protection areas or functional areas in Area I, artificial lawns or plantations shall be built in the river around the water body to intercept pollutants from non-point source pollution in Area II, and scientific and rational farming in the lower reaches of rivers can reduce non-point source pollution caused by farming.
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Huda, Nazmul, Toru Terao, Atsuko Nonomura, and Yoshihiro Suenaga. "Time-Series Remote Sensing Study to Detect Surface Water Seasonality and Local Water Management at Upper Reaches of Southwestern Bengal Delta from 1972 to 2020." Sustainability 13, no. 17 (August 31, 2021): 9798. http://dx.doi.org/10.3390/su13179798.
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Bengal delta experiences immense seasonality of surface water due to its geographical position. This study aims to explore the extent and seasonality of surface water in the southwestern part of Bangladesh (SWB) where human intervention has been rapidly changing the land use for several decades. This explorative study relied on a total of 312 high-resolution Landsat images from 1972 to 2020 and interviews to present crucial months, seasons, and periods for surface water in SWB. The study uses a valid threshold point ‘0′ for Normalized Difference Water Index (NDWI) to extract water pixels and confirms that the NIR band has better efficacy to separate water pixels. On average, the SWB has faced around 5.5% of surface water between 1972–2001, which increased to 12.8% between 2002 and 2020. Based on the median value, around 6% of surface water was observed in the 1990s, which increased to 16% in the 2010s. The average surface water was detected around 6% and 7% in December and January between 1972 and 2001, which expanded to 18% and 19% between 2002 and 2020, mainly because of human interventions such as mix-cropping. The study strongly suggests considering December and January months for further land use and land class studies which focus on the southwestern part of Bangladesh.