Clustering analysis for the hydro-geomorphometric characterization of the George River watershed (Nunavik, Canada)

For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Fortunately, remote sensing approaches can provide considerable information about the structural properties of watersheds, which is useful for the indirect assessment of their hydrological characteristics and behavior. Our main objective is to produce a high-resolution territorial clustering based on key hydrologic landscape metrics for the entire 42 000 km2 George River watershed (GRW), located in Nunavik, northern Qu&#233;bec (Canada). This project is being conducted in partnership with the local Inuit communities of the GRW for the purpose of generating and sharing knowledge to anticipate the impact of climate and socio-environmental change in the GRW.Our clustering approach employs Unsupervised Geographic Object-Based Image Analysis (GeOBIA) applied to the entire GRW with the subwatersheds as our objects of analysis. The landscape metric datasets used to generate the input variables of our GeOBIA classification are raster layers with a 30m x 30m pixel resolution. Topographic metrics are derived from a Digital Elevation Model (DEM) and include elevation, slopes, aspect, drainage density and watershed elongation. Land cover spectral metrics comprised in our analysis are the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI) (Gao, 1996) and the Normalized Difference Water Index (NDWI) (McFeeters, 1996), which are all computed from a Landsat-8 cloud-free surface reflectance mosaic dating from 2015. Rasterized maps of surface deposit distribution and permafrost distribution, both produced by the Minist&#232;re des For&#234;ts, de la Faune et des Parcs of Qu&#233;bec (MFFP), respectively constitute the surface and subsurface metrics of our GeOBIA.The clustering algorithm used in this Unsupervised GeOBIA is the Fuzzy C-Means (FCM) algorithm. The FCM algorithm provides the objects a set of membership coefficients corresponding to each cluster. The greatest membership coefficient is then used to attribute the distinct subwatersheds to a cluster of watersheds with similar hydro-geomorphometric characteristics. The classification returns a Fuzzy Partition Coefficient (FPC), which describes how well-partitioned our dataset is. The FPC can vary greatly depending on the number of clusters we want to produce. Thus, we find the optimal number of clusters by maximizing the FPC.Preliminary clustering results, computed only with topographic and land cover metrics, have identified two distinct watershed classes/clusters. In general, &#8220;Type 1&#8221; subwatersheds are clustered over the southern and northwestern portion of the GRW and are characterized by low to moderate elevation, high vegetation cover, high moisture and high surface water cover. Whereas &#8220;Type 2&#8221; subwatersheds located over the northeastern portion of the GRW are characterized by high elevation, low vegetation cover, low moisture and low surface water cover. These results will be refined with the use of additional metrics and will provide the detailed understanding necessary to assess how the hydrological regime of the river and its tributaries will respond to climate change, and how landscape change and human activities (e.g., planned mining development) may impact the water quality of the George River and its tributaries.

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18031132 ◽

2021 ◽

Vol 18 (3) ◽

pp. 1132

Author(s):

Qijiao Xie ◽

Qi Sun

Keyword(s):

Land Use ◽

Air Quality ◽

Urban Planning ◽

Land Cover ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Water Bodies ◽

Landsat 8 ◽

Land Use Land Cover ◽

The Impact

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

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Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES)

Geoscientific Model Development ◽

10.5194/gmd-12-179-2019 ◽

2019 ◽

Vol 12 (1) ◽

pp. 179-193 ◽

Cited By ~ 11

Author(s):

Chantelle Burton ◽

Richard Betts ◽

Manoel Cardoso ◽

Ted R. Feldpausch ◽

Anna Harper ◽

...

Keyword(s):

Land Use ◽

Land Use Change ◽

Land Cover ◽

Vegetation Cover ◽

Land Surface ◽

Vegetation Dynamics ◽

Fire Disturbance ◽

Substantial Contribution ◽

Surface Model ◽

The Impact

Abstract. Disturbance of vegetation is a critical component of land cover, but is generally poorly constrained in land surface and carbon cycle models. In particular, land-use change and fire can be treated as large-scale disturbances without full representation of their underlying complexities and interactions. Here we describe developments to the land surface model JULES (Joint UK Land Environment Simulator) to represent land-use change and fire as distinct processes which interact with simulated vegetation dynamics. We couple the fire model INFERNO (INteractive Fire and Emission algoRithm for Natural envirOnments) to dynamic vegetation within JULES and use the HYDE (History Database of the Global Environment) land cover dataset to analyse the impact of land-use change on the simulation of present day vegetation. We evaluate the inclusion of land use and fire disturbance against standard benchmarks. Using the Manhattan metric, results show improved simulation of vegetation cover across all observed datasets. Overall, disturbance improves the simulation of vegetation cover by 35 % compared to vegetation continuous field (VCF) observations from MODIS and 13 % compared to the Climate Change Initiative (CCI) from the ESA. Biases in grass extent are reduced from −66 % to 13 %. Total woody cover improves by 55 % compared to VCF and 20 % compared to CCI from a reduction in forest extent in the tropics, although simulated tree cover is now too sparse in some areas. Explicitly modelling fire and land use generally decreases tree and shrub cover and increases grasses. The results show that the disturbances provide important contributions to the realistic modelling of vegetation on a global scale, although in some areas fire and land use together result in too much disturbance. This work provides a substantial contribution towards representing the full complexity and interactions between land-use change and fire that could be used in Earth system models.

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Assessment of Land Cover Changes in the Hinterland of Barranquilla (Colombia) Using Landsat Imagery and Logistic Regression

Land ◽

10.3390/land7040152 ◽

2018 ◽

Vol 7 (4) ◽

pp. 152 ◽

Cited By ~ 6

Author(s):

Henry Schubert ◽

Andrés Caballero Calvo ◽

Markus Rauchecker ◽

Oscar Rojas-Zamora ◽

Grischa Brokamp ◽

...

Keyword(s):

Land Use ◽

Logistic Regression ◽

Land Cover ◽

Rural Areas ◽

Vegetation Cover ◽

Landsat Imagery ◽

Impact Factors ◽

Land Cover Changes ◽

Dynamic Changes ◽

The Impact

Barranquilla is known as a dynamically growing city in the Colombian Caribbean. Urbanisation induces land use and land cover (LULC) changes in the city and its hinterland affecting the region’s climate and biodiversity. This paper aims to identify the trends of land use and land cover changes in the hinterland of Barranquilla corresponding to 13 municipalities in the north of the Department Atlántico. Landsat TM/ETM/OLI imagery from 1985 to 2017 was used to map and analyse the spatio-temporal development of land use and land cover changes. During the investigation period, the settlement areas grew by approximately 50% (from 103.3 to 153.6 km2), while areas with woody vegetation cover experienced dynamic changes and increased in size since 2001. Peri-urban and rural areas were characterized by highly dynamic changes, particularly regarding clearing and recovery of vegetated areas. Regression analyses were performed to identify the impact factors of detected vegetation cover changes. Computed logistic regression models included 20 independent variables, such as relief, climate, soil, proximity characteristics and socio-economic data. The results of this study may act as a basis to enable researchers and decision-makers to focus on the most important signals of systematic landscape transformations and on the conservation of ecosystems and the services they provide.

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Impacts of the Sanmenxia Dam on the Interaction between Surface Water and Groundwater in the Lower Weihe River of Yellow River Watershed

Water ◽

10.3390/w12061671 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1671 ◽

Cited By ~ 2

Author(s):

Dong Zhang ◽

Dongmei Han ◽

Xianfang Song

Keyword(s):

Surface Water ◽

Rural Areas ◽

Yellow River ◽

Early Stage ◽

Fertilizer Application ◽

River Watershed ◽

Surface Water And Groundwater ◽

Dam Operation ◽

Weihe River ◽

The Impact

Sanmenxia Dam, one of the most controversial water conservancy projects in China, has seriously impacted the lower Weihe River of the Yellow River Watershed since its operation. At the Huaxian Station, the dam operation controls the surface water level and leads to the variation of the surface water–groundwater interaction relationship. The river channel switched from a losing reach during the early stage (1959) to a gaining reach in 2010 eventually. The comparison of tracer (Cl−, δ18O and δ2H) characteristics of surface water in successive reaches with that of ambient groundwater shows that the general interaction condition is obviously affected by the dam operation and the impact area can be tracked back to Weinan City, around 65 km upstream of the estuary of the Weihe River. The anthropogenic inputs (i.e., agricultural fertilizer application, wastewater discharge, and rural industrial sewage) could be responsible for the deterioration of hydro-environment during the investigation periods of 2015 and 2016, as the population and fertilizer consumption escalated in the last 60 years. The use of contaminated river water for irrigation, along with the dissolved fertilizer inputs, can affect the groundwater quality, in particular resulting in the NO3− concentrations ranging from 139.4 to 374.1 mg/L. The unregulated industrial inputs in some rural areas may increase the Cl− contents in groundwater ranging from 298.4 to 472.9 mg/L. The findings are helpful for the improved comprehensive understanding of impacts of the Sanmenxia Dam on the interaction between surface water and groundwater, and for improving local water resources management.

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Land Use/Land Cover (LU/LC) Changes and its impact on Soil Organic Carbon Stock in Killiar River Basin, Kerala, India: A Geospatial Approach

Current World Environment ◽

10.12944/cwe.16.3.2 ◽

2021 ◽

Vol 16 (3) ◽

pp. 662-664

Author(s):

Sabu Joseph ◽

Rahul R ◽

Sukanya S

Keyword(s):

Land Use ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Land Cover ◽

River Basin ◽

Carbon Stock ◽

Landsat 8 ◽

Soil Organic Carbon Stock ◽

The Impact ◽

Organic Carbon Stock

The changes in the pattern of land use and land cover (LU/LC) have remarkable consequences on ecosystem functioning and natural resources dynamics. The present study analyzes the spatial pattern of LU/LC change detection along the Killiar River Basin (KRB), a major tributary of Karamana river in Thiruvananthapuram district, Kerala (India), over a period of 64 years (1957-2021) through Remote Sensing and GIS approach. The rationale of the study is to identify and classify LU/LC changes in KRB using the Survey of India (SOI) toposheet (1:50,000) of 1957, LISS-III imagery of 2005, Landsat 8 OLI & TIRS imagery of 2021 and further to scrutinize the impact of LU/LC conversion on Soil Organic Carbon stock in the study area. Five major LU/LC classes, viz., agriculture land, built-up, forest, wasteland and water bodies were characterized from available data. Within the study period, built-up area and wastelands showed a substantial increase of 51.51% and 15.67% respectively. Thus, the general trend followed is the increase in built-up and wastelands area which results in the decrease of all other LU/LC classes. Based on IPCC guidelines, total soil organic carbon (SOC) stock of different land-use types was estimated and was 1292.72 Mt C in 1957, 562.65 Mt C in 2005 and it reduced to 152.86 Mt C in 2021. This decrease is mainly due to various anthropogenic activities, mainly built-up activities. This conversion for built-up is at par with the rising population, and over-exploitation of natural and agricultural resources is increasing every year.

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Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PA

Anuário do Instituto de Geociências - UFRJ ◽

10.11137/2020_2_07_19 ◽

2020 ◽

Vol 43 (2) ◽

Author(s):

Eduardo Da Silva Margalho ◽

Madson Tavares Silva ◽

Letícia Karyne Da Silva Cardoso ◽

Ricardo Alves de Olinda ◽

José Felipe Gazel Menezes

Keyword(s):

Land Cover ◽

Surface Temperature ◽

Urban Area ◽

Vegetation Cover ◽

Spectral Indices ◽

Urban Center ◽

Territorial Expansion ◽

Entire Study ◽

Remote Sensing Techniques ◽

The Impact

The objective of this work is to examine the spatial distribution of Continental Surface Temperature (CST) of the urban area of Belem / PA and the influence of the change of use and soil cover from remote sensing techniques. Products from Thematic Mapper (TM) and Thermal Infrared Sensor (TIRS) sensors coupled, respectively, to Landsat 5 and 8 satellites were used. The images acquired from the years 1994, 2008 and 2017 were processed, resampled (spatial resolution of 120 meters) and, finally, centroids were extracted with a total of 1252 points, using the Quantum GIS software. Subsequently, spectral indices, NDVI, NDBI and albedo were calculated, which represent, respectively, the presence of vegetation, exposed soil or built area and reflectivity rate. The results showed that CST showed an increase in all sectors of the study area, mainly between the years 2008 and 2017. The sector with the highest elevation of the CST was the urban center, as it presented values below 25.0 ºC in the image of 1994 and above 35.0 ºC in the 2017 image. In contrast, the ecological park sector showed the lowest increase in CST, from 20.0 ºC (1994) to 25.0 ºC (2017). According to the analysis of the spectral indices, the intensification of CST is directly associated with the strong territorial expansion, since from the NDVI values the degradation of the vegetation cover was noted. This degradation is observed in the comparisons of the images, in which it is possible to verify the decrease in the NDVI values in the entire study area, whose values represent the decrease in the vegetation cover. The sector with the greatest withdrawal of green areas was the northern zone, as it showed a drop in NDVI values, from 0.7 in 1994 to 0.3 in the 2017 image. It was also observed that the density of the constructed area was intensified, presenting increasing values of NDBI. Added to these NDVI and NDBI values, higher reflectivity rate values were noted, whose values in the urban center of Belem in 1994 were 0.1% and which exceeded 0.5% in the image for the year 2017, ratifying the impact of changes in land cover and the direct association between changes in the environment and CST. In general, the results indicate that the uncontrolled expansion of the urban process and the change in land cover cause the intensification of CST.

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Mapping and Analyzing the Park Cooling Effect on Urban Heat Island in an Expanding City: A Case Study in Zhengzhou City, China

Land ◽

10.3390/land9020057 ◽

2020 ◽

Vol 9 (2) ◽

pp. 57 ◽

Cited By ~ 4

Author(s):

Huawei Li ◽

Guifang Wang ◽

Guohang Tian ◽

Sándor Jombach

Keyword(s):

Urban Heat Island ◽

Vegetation Cover ◽

Landscape Planning ◽

Impervious Surface ◽

Google Earth ◽

Cooling Effect ◽

Landsat 8 ◽

Heat Island ◽

Urban Heat ◽

The Impact

The Urban Heat Island (UHI) effect has been extensively studied as a global issue. The urbanization process has been proved to be the main reason for this phenomenon. Over the past 20 years, the built-up area of Zhengzhou city has grown five times larger, and the UHI effect has become increasingly pressing for the city’s inhabitants. Therefore, mitigating the UHI effect is an important research focus of the expanding capital city of the Henan province. In this study, the Landsat 8 image of July 2019 was selected from Landsat collection to obtain Land Surface Temperature (LST) by using Radiative Transfer Equation (RTE) method, and present land cover information by using spectral indices. Additionally, high-resolution Google Earth images were used to select 123 parks, grouped in five categories, to explore the impact factors on park cooling effect. Park Cooling Intensity (PCI) has been chosen as an indicator of the park cooling effect which will quantify its relation to park patch metrics. The results show that: (1) Among the five studied park types, the theme park category has the largest cooling effect while the linear park category has the lowest cooling effect; (2) The mean park LST and PCI of the samples are positively correlated with the Fractional Vegetation Cover (FVC) and with Normalized Difference Water Index (NDWI), but these are negatively correlated with the Normalized Difference Impervious Surface Index (NDISI). We can suppose that the increase of vegetation cover rate within water areas as well as the decrease of impervious surface in landscape planning and design will make future parks colder. (3) There is a correlation between the PCI and the park characteristics. The UHI effect could be mitigated by increasing of park size and reducing park fractal dimension (Frac_Dim) and perimeter-area ratio (Patario). (4) The PCI is influenced by the park itself and its surrounding area. These results will provide an important reference for future urban planning and urban park design to mitigate the urban heat island effect.

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The urban areas classification methodology according to multi-zone images of Sentinel 2 and Landsat 8 (on the example of the city of Thanh Hoa, Vietnam)

Geodesy and Cartography ◽

10.22389/0016-7126-2020-956-2-40-49 ◽

2020 ◽

Vol 956 (2) ◽

pp. 40-49

Author(s):

Le Hung Trinh ◽

Dinh Sinh Mai ◽

V.R. Zablotskii

Keyword(s):

Land Cover ◽

Urban Areas ◽

Satellite Images ◽

Near Infrared ◽

Landsat 8 ◽

Land Use Classification ◽

Infrared Band ◽

The Status ◽

The Impact ◽

Sentinel 2

In recent years, land cover changes very quickly in urban areas due to the impact of population growth and socio-economic development. The authors present the method of land cover/land use classification based on the combination of Sentinel 2 and Landsat 8 multi-resolution satellite images. A middle infrared band (band 11), a near infrared (band 8) of Sentinel 2 image and a thermal infrared one (band 10) of Landsat 8 image were used to calculate EBBI (Enhanced Built-up and Barreness Index). The EBBI index and Sentinel 2 spectral bands with spatial resolution 10 m (band 2, 3, 4, 8) were used to classify the land cover. The obtained results showed that, the method of land cover classification based on combination of Sentinel 2 and Landsat 8 satellite images improves the overall accuracy by about 5 % compared with the one using only Sentinel 2 data. The results obtained at the study can be used for the management, assessment and monitoring the status and dynamics of land cover in urban areas.

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DETERMINING THE IMPACTS OF LAND COVER/USE CATEGORIES ON LAND SURFACE TEMPERATURE USING LANDSAT8-OLI

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-251-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 251-256 ◽

Cited By ~ 2

Author(s):

F. Bektas Balcik ◽

E. M. Ergene

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Landsat 8 ◽

Tasseled Cap ◽

Tasseled Cap Transformation ◽

The Impact ◽

Use Categories

Due to unplanned and uncontrolled expansion of urban areas, rural land cover types have been replaced with artificial materials. As a result of these replacements, a wide range of negative environmental impacts seriously impacting human health, natural areas, ecosystems, climate, energy efficiency, and quality of living in town center. In this study, the impact of land surface temperature with respect to land cover and land use categories is investigated and evaluated for Istanbul, Turkey. Land surface temperature data was extracted from 21 October 2014 dated Landsat 8 OLI data using mono-window algorithm. In order to extract land use/cover information from remotely sensed data wetness, greenness and brightness components were derived using Tasseled Cap Transformation. The statistical relationship between land surface temperature and Tasseled Cap Transformation components in Istanbul was analyzed using the regression methods. Correlation between Land Surface Temperature and Meteorological Stations Temperature calculated %74.49.

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Impact of Urban Land Cover Types on Surface Temperature

10.5194/egusphere-egu21-10521 ◽

2021 ◽

Author(s):

Marzie Naserikia ◽

Melissa Hart ◽

Negin Nazarian

Keyword(s):

Land Cover ◽

Surface Temperature ◽

Vegetation Cover ◽

Land Surface ◽

Urban Areas ◽

Climatic Conditions ◽

Landsat 8 ◽

Local Climate ◽

Land Cover Types ◽

Cooling Effects

The conversion of natural land to built-up surfaces has been widely documented as the main determinant of warming across urban areas. However, uncertainties remain regarding which primary land cover variables control urban heat in different climatic conditions at a global scale. While there is a very little understanding of how the cooling effects of vegetation cover vary over different cities, there is a deep knowledge gap in realizing how other land covers (such as soil, water, and built-up areas) are associated with urban warming and how this relationship is varied in different background climates. Accordingly, using a high spatial resolution dataset, a global synthetic investigation is needed to find the underlying factors influencing intra-urban temperature variability in various climates. To address this shortcoming, this study focuses on exploring the relationship between land surface temperature and land cover in different cities (using Landsat 8 imagery) and aims to investigate the effects of these land cover types on thermal environments in different climatic backgrounds. Preliminary analysis shows that different land cover types have different roles in different climate classes due to their various surface characteristics and in particular, the performance of green spaces to reduce LST is highly dependent on its background climate. For example, the efficiency of vegetation cover to reduce urban surface warming in temperate and tropical climates is more than that in arid and semi-arid areas. In this climate class, since baren soil is the main contributor to the intensity of LST, increasing the area of a green space presents an effective method to mitigate the adverse effects of local warming. Our findings provide helpful information for future urban climate-sensitive planning oriented at mitigating local climate warming in cities.

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