A framework based on high-resolution imagery datasets and MCS for forecasting evaporation loss from small reservoirs in groundwater-based agriculture

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

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ASSESSING MICROFOSSIL TAPHONOMY WITH HIGH-RESOLUTION IMAGERY

10.1130/abs/2017se-290363 ◽

2017 ◽

Author(s):

R. Seth Wood ◽

◽

Ashley R. Manning-Berg ◽

Kenneth H. Williford ◽

Linda C. Kah

Keyword(s):

High Resolution ◽

High Resolution Imagery

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Assessing Riyadh’s Urban Change Utilizing High-Resolution Imagery

Land ◽

10.3390/land8120193 ◽

2019 ◽

Vol 8 (12) ◽

pp. 193

Author(s):

Ali Alghamdi ◽

Anthony R. Cummings

Keyword(s):

Maximum Likelihood ◽

High Resolution ◽

Accuracy Assessment ◽

Middle Eastern ◽

Object Oriented ◽

Training Data ◽

High Resolution Imagery ◽

Significant Research ◽

Desert Areas ◽

Spot 5

The implications of change on local processes have attracted significant research interest in recent times. In urban settings, green spaces and forests have attracted much attention. Here, we present an assessment of change within the predominantly desert Middle Eastern city of Riyadh, an understudied setting. We utilized high-resolution SPOT 5 data and two classification techniques—maximum likelihood classification and object-oriented classification—to study the changes in Riyadh between 2004 and 2014. Imagery classification was completed with training data obtained from the SPOT 5 dataset, and an accuracy assessment was completed through a combination of field surveys and an application developed in ESRI Survey 123 tool. The Survey 123 tool allowed residents of Riyadh to present their views on land cover for the 2004 and 2014 imagery. Our analysis showed that soil or ‘desert’ areas were converted to roads and buildings to accommodate for Riyadh’s rapidly growing population. The object-oriented classifier provided higher overall accuracy than the maximum likelihood classifier (74.71% and 73.79% vs. 92.36% and 90.77% for 2004 and 2014). Our work provides insights into the changes within a desert environment and establishes a foundation for understanding change in this understudied setting.

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2nd Edition of Instrumenting Smart City Applications with Big Sensing and Earth Observatory Data: Tools, Methods and Techniques

Remote Sensing ◽

10.3390/rs13071310 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1310

Author(s):

Gabriele Bitelli ◽

Emanuele Mandanici

Keyword(s):

High Resolution ◽

Exponential Growth ◽

Smart City ◽

Earth Observation ◽

Urban Environments ◽

Observation Data ◽

High Resolution Imagery ◽

Observatory Data ◽

Methods And Techniques ◽

Earth Observation Data

The exponential growth in the volume of Earth observation data and the increasing quality and availability of high-resolution imagery are increasingly making more applications possible in urban environments [...]

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Multi-Scene Building Height Estimation Method Based on Shadow in High Resolution Imagery

Remote Sensing ◽

10.3390/rs13152862 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2862

Author(s):

Yakun Xie ◽

Dejun Feng ◽

Sifan Xiong ◽

Jun Zhu ◽

Yangge Liu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Estimation Method ◽

Absolute Error ◽

Large Error ◽

Estimation Methods ◽

Height Estimation ◽

Building Height ◽

Remote Sensing Imagery ◽

High Resolution Imagery

Accurately building height estimation from remote sensing imagery is an important and challenging task. However, the existing shadow-based building height estimation methods have large errors due to the complex environment in remote sensing imagery. In this paper, we propose a multi-scene building height estimation method based on shadow in high resolution imagery. First, the shadow of building is classified and described by analyzing the features of building shadow in remote sensing imagery. Second, a variety of shadow-based building height estimation models is established in different scenes. In addition, a method of shadow regularization extraction is proposed, which can solve the problem of mutual adhesion shadows in dense building areas effectively. Finally, we propose a method for shadow length calculation combines with the fish net and the pauta criterion, which means that the large error caused by the complex shape of building shadow can be avoided. Multi-scene areas are selected for experimental analysis to prove the validity of our method. The experiment results show that the accuracy rate is as high as 96% within 2 m of absolute error of our method. In addition, we compared our proposed approach with the existing methods, and the results show that the absolute error of our method are reduced by 1.24 m-3.76 m, which can achieve high-precision estimation of building height.

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Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery

Remote Sensing ◽

10.3390/rs13132508 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2508

Author(s):

Loredana Oreti ◽

Diego Giuliarelli ◽

Antonio Tomao ◽

Anna Barbati

Keyword(s):

High Resolution ◽

Spatial Scales ◽

Parametric Method ◽

Mixed Forest ◽

Mixed Stands ◽

Mapping Unit ◽

Mixed Forests ◽

High Resolution Imagery ◽

Very High Resolution Imagery ◽

Very High

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

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Cloud Computation Using High-Resolution Images for Improving the SDG Indicator on Open Spaces

Remote Sensing ◽

10.3390/rs12071144 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1144

Author(s):

Rosa Aguilar ◽

Monika Kuffer

Keyword(s):

High Resolution ◽

Open Space ◽

Local Planning ◽

Open Spaces ◽

High Resolution Imagery ◽

Coarse Spatial Resolution ◽

Multi Temporal ◽

High Resolution Images ◽

Cloud Computation

Open spaces are essential for promoting quality of life in cities. However, accelerated urban growth, in particular in cities of the global South, is reducing the often already limited amount of open spaces with access to citizens. The importance of open spaces is promoted by SDG indicator 11.7.1; however, data on this indicator are not readily available, neither globally nor at the metropolitan scale in support of local planning, health and environmental policies. Existing global datasets on built-up areas omit many open spaces due to the coarse spatial resolution of input imagery. Our study presents a novel cloud computation-based method to map open spaces by accessing the multi-temporal high-resolution imagery repository of Planet. We illustrate the benefits of our proposed method for mapping the dynamics and spatial patterns of open spaces for the city of Kampala, Uganda, achieving a classification accuracy of up to 88% for classes used by the Global Human Settlement Layer (GHSL). Results show that open spaces in the Kampala metropolitan area are continuously decreasing, resulting in a loss of open space per capita of approximately 125 m2 within eight years.

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