A Coastal Seawater Temperature Dataset for Biogeographical Studies: Large Biases between In Situ and Remotely-Sensed Data Sets around the Coast of South Africa

Abstract. The aim of this study is to quantify the actual urbanization activity near the catchment area in the urban area of interest located in the vicinity of the Agriokalamin River area of Kissonerga Village in Paphos District. Remotely sensed data such as aerial photos, Landsat-5/7 TM/ETM+ and Quickbird image data have been used to track the urbanization activity from 1963 to 2008. In-situ GPS measurements have been used to locate in-situ the boundaries of the catchment area. The results clearly illustrate that tremendous urban development has taken place ranging from 0.9 to 33% from 1963 to 2008, respectively. A flood risk assessment and hydraulic analysis were also performed.

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In situ soil moisture network for validation of remotely sensed data

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 ◽

10.1109/igarss.2004.1370378 ◽

2004 ◽

Author(s):

D. Bosch ◽

V. Lakshmi ◽

T. Jackson ◽

J. Jacobs ◽

S. Moran

Keyword(s):

Soil Moisture ◽

Remotely Sensed ◽

Remotely Sensed Data

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Recent Trends of Drought Using Remotely Sensed and In-Situ Indices: Towards an Integrated Drought Monitoring System for South Africa

10.1109/igarss47720.2021.9554117 ◽

2021 ◽

Author(s):

Mahlatse Kganvago ◽

Mxolisi B. Mukhawana ◽

Morwapula Mashalane ◽

Aphelele Mgabisa ◽

Simon Moloele

Keyword(s):

South Africa ◽

Monitoring System ◽

Remotely Sensed ◽

Drought Monitoring ◽

Recent Trends

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Integrating field data

Remote Sensing for Ecology and Conservation ◽

10.1093/oso/9780199219940.003.0021 ◽

2010 ◽

Author(s):

Ned Horning ◽

Julie A. Robinson ◽

Eleanor J. Sterling ◽

Woody Turner ◽

Sacha Spector

Keyword(s):

South Africa ◽

Habitat Use ◽

Vegetation Type ◽

Remotely Sensed ◽

Battery Life ◽

Clear Understanding ◽

Remotely Sensed Data ◽

Human Settlements ◽

Ecological Gradient ◽

Landsat 7

While the savannah elephant (Loxodonta africana) is listed by the International Union for Conservation of Nature (IUCN) as “vulnerable” because of declining abundance in some regions of Africa (Blanc 2008), populations in some protected areas of South Africa are growing rapidly (van Aarde and Jackson 2007). These populations can cause extensive modification of vegetation structure when their density increases (Owen-Smith 1996; Whyte et al. 2003; Guldemond and van Aarde 2007). Management methods such as culling, translocation, and birth control have not reduced density in some cases (van Aarde et al. 1999; Pimm and van Aarde 2001). Providing more space for elephants is one alternative management strategy, yet fundamental to this strategy is a clear understanding of habitat and landscape use by elephants. Harris et al. (2008) combined remotely sensed data with Global Positioning System (GPS) and traditional ethological observations to assess elephant habitat use across three areas that span the ecological gradient of historical elephant distribution. They explored influences on habitat use across arid savannahs (Etosha National Park in Namibia) and woodlands (Tembe Elephant Park in South Africa and Maputo Elephant Reserve in Mozambique). The researchers focused on three main variables—distance to human settlements, distance to water, and vegetation type. The authors used Landsat 7 ETMþ imagery to create vegetation maps for each location, employing supervised classification and maximum likelihood estimation. Across all sites, they recorded the coordinates of patches with different vegetation and of vegetation transitions to develop signatures for the maps. Elephants do not use all vegetation types, and it can be expedient to focus on presence rather than both presence and absence. Accordingly, the researchers used GPS to record the locations of elephants with the aim of identifying important land cover types for vegetation mapping. The authors mapped water locations in the wet and dry seasons using remotely sensed data and mapped human settlements using GPS, aerial surveys, and regional maps. They tracked elephants with radiotelemetry collars that communicated with the ARGOS satellite system, sending location data for most of the elephants over 24 h, and then remaining quiescent for the next 48 h to extend battery life.

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Remote Sensing Approaches and Related Techniques to Map and Study Landslides

Landslides - Investigation and Monitoring ◽

10.5772/intechopen.93681 ◽

2020 ◽

Author(s):

Ram L. Ray ◽

Maurizio Lazzari ◽

Tolulope Olutimehin

Keyword(s):

Remote Sensing ◽

Hazard Assessment ◽

Remote Sensing Data ◽

Remotely Sensed ◽

Landslide Monitoring ◽

In Situ Observation ◽

Remotely Sensed Data ◽

Sensing Data ◽

Stability Model

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales.

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