SPATIOTEMPORAL DYNAMICS OF SURFACE WATER EXTENT FROM THREE DECADES OF SEASONALLY CONTINUOUS LANDSAT TIME SERIES AT SUBCONTINENTAL SCALE

Surface water is a critical resource in semi-arid areas. The Murray-Darling Basin (MDB) of Australia, one of the largest semi-arid basins in the world is aiming to set a worldwide example of how to balance multiple interests (i.e. environment, agriculture and urban use), but has suffered significant water shrinkages during the Millennium Drought (1999-2009), followed by extensive flooding. Baseline information and systematic quantification of surface water (SW) extent and flooding dynamics in space and time are needed for managing SW resources across the basin but are currently lacking. To synoptically quantify changes in SW extent and flooding dynamics over MDB, we used seasonally continuous Landsat TM and ETM+ data (1986 – 2011) and generic machine learning algorithms. We further mapped flooded forest at a riparian forest site that experienced severe tree dieback due to changes in flooding regime. We used a stratified sampling design to assess the accuracy of the SW product across time. Accuracy assessment yielded an overall classification accuracy of 99.94%, with producer’s and user’s accuracy of SW of 85.4% and 97.3%, respectively. Overall accuracy was the same for Landsat 5 and 7 data but user’s and producer’s accuracy of water were higher for Landsat 7 than 5 data and stable over time. Our validated results document a rapid loss in SW bodies. The number, size, and total area of SW showed high seasonal variability with highest numbers in winter and lowest numbers in summer. SW extent per season per year showed high interannual and seasonal variability, with low seasonal variability during the Millennium Drought. Examples of current uses of the new dataset will be presented and include (1) assessing ecosystem response to flooding with implications for environmental water releases, one of the largest investment in environment in Australia; (2) quantifying drivers of SW dynamics (e.g. climate, human activity); (3) quantifying changes in SW dynamics and connectivity for water dependent organisms; (4) assessing the impact of flooding on riparian vegetation health. The approach developed here is globally applicable, relevant to areas with competing water demands (e.g. Okavango River delta, Mekong River Basin). Future work should incorporate Landsat 8 and Sentinel-2 data for continued quantification of SW dynamics.

Download Full-text

The assessment of surface water resources for the semi-arid Yongding River Basin from 1956 to 2000 and the impact of land use change

Hydrological Processes ◽

10.1002/hyp.7566 ◽

2010 ◽

Vol 24 (9) ◽

pp. 1123-1132 ◽

Cited By ~ 27

Author(s):

Lei Wang ◽

Zhongjing Wang ◽

Toshio Koike ◽

Hang Yin ◽

Dawen Yang ◽

...

Keyword(s):

Land Use ◽

Surface Water ◽

Water Resources ◽

Land Use Change ◽

River Basin ◽

Surface Water Resources ◽

The Impact ◽

Semi Arid ◽

Yongding River

Download Full-text

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

10.5194/egusphere-egu21-206 ◽

2021 ◽

Author(s):

Eliot Sicaud ◽

Jan Franssen ◽

Jean-Pierre Dedieu ◽

Daniel Fortier

Keyword(s):

Surface Water ◽

Land Cover ◽

Vegetation Cover ◽

Landsat 8 ◽

Number Of Clusters ◽

River Watershed ◽

Fcm Algorithm ◽

Water Cover ◽

George River ◽

The Impact

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.

Download Full-text

Evaluation of Machine Learning Algorithms for Surface Water Delineation Using Landsat 8 Images

Journal of Advanced Research in Dynamical and Control Systems ◽

10.5373/jardcs/v12i3/20201184 ◽

2020 ◽

Vol 12 (3) ◽

pp. 207-216

Author(s):

Bijeesh T.V.

Keyword(s):

Machine Learning ◽

Surface Water ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Landsat 8

Download Full-text

Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽

10.3390/s19122769 ◽

2019 ◽

Vol 19 (12) ◽

pp. 2769 ◽

Cited By ~ 8

Author(s):

Tri Dev Acharya ◽

Anoj Subedi ◽

Dong Ha Lee

Keyword(s):

Machine Learning ◽

Surface Water ◽

Recursive Partitioning ◽

Learning Algorithms ◽

High Elevation ◽

Machine Learning Algorithms ◽

Water Extraction ◽

Support Vector ◽

Landsat 8 ◽

Water Index

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance.

Download Full-text

TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-8-137-2016 ◽

2016 ◽

Vol III-8 ◽

pp. 137-144 ◽

Cited By ~ 3

Author(s):

Gofamodimo Mashame ◽

Felicia Akinyemi

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Land Degradation ◽

Landsat 8 ◽

Land Use Land Cover ◽

Bare Land ◽

Level I ◽

The Impact ◽

Semi Arid

Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.

Download Full-text

Human activity impact on surface water quality in semi-arid regions: a case study of Aïnzeda lake (North-East Algeria)

Water Science & Technology Water Supply ◽

10.2166/ws.2020.083 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1726-1744

Author(s):

Abdelhafid Mebarkia ◽

Abdelmadjid Boufekane

Keyword(s):

Water Quality ◽

Surface Water ◽

Human Activity ◽

Arid Regions ◽

Surface Water Quality ◽

Unequal Distribution ◽

North East ◽

The Impact ◽

Semi Arid

Abstract Water resources scarcity in Algeria, their fragility and their unequal distribution have resulted in a serious shortage, which, in spite of all the efforts, seems inevitable. This study consists of evaluating the impact of human activity on the water quality of Aïnzeda lake (NE Algeria), a typical case study of the difficulties posed by the problem of surface water quality in semi-arid regions. Principal component analysis (PCA) and the trend method were applied to interpret the physico-chemical data of monthly analyzed samples, over a 25-year period (1988–2012). The trend method results show that most chemical elements have a direct relationship with urbanization and agricultural practices in the area. The change in the watershed climatic conditions (increase of 9% in air temperature, 7% in the lake water temperature, and decrease of 8% in precipitation) is also responsible for the degradation of the water quality. The PCA shows that salinization (51.73%), and anthropogenic and agricultural pollution (13.49%) are the most significant degradation factors. These two approaches have enabled us to prove that aridity and anthropogenic or agricultural activities have a negative impact on the lake's surface water quality.

Download Full-text

TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-8-137-2016 ◽

2016 ◽

Vol III-8 ◽

pp. 137-144 ◽

Cited By ~ 1

Author(s):

Gofamodimo Mashame ◽

Felicia Akinyemi

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Land Degradation ◽

Landsat 8 ◽

Land Use Land Cover ◽

Bare Land ◽

Level I ◽

The Impact ◽

Semi Arid

Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.

Download Full-text

Forecasting US movies box office performances in Turkey using machine learning algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189120 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6579-6590

Author(s):

Sandy Çağlıyor ◽

Başar Öztayşi ◽

Selime Sezgin

Keyword(s):

Machine Learning ◽

Global Economy ◽

Learning Algorithms ◽

Forecast Model ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

High Stakes ◽

Box Office ◽

Industry Forecast ◽

The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Download Full-text

Global seasonal variability

10.29104/wins.l.0108.2018 ◽

2017 ◽

Author(s):

Chloé Meyer

Keyword(s):

Surface Water ◽

Water Supply ◽

Seasonal Variability

Seasonal variability measures variation in water supply between months of the year. Drought Flood Surface water

Download Full-text