BRDF field studies for remote sensing of urban areas∗

Abstract Monitoring of trees in urban areas can be conducted using remote sensing, but should be supported by field measurements. The article aims to present the research method used to evaluate discolouration and defoliation of trees and tree damage in the city of Białystok in Poland. The analyses were done using AISA hyperspectral images. Field measurements encompassed determining the locations, species and levels of discolouration and defoliation of trees. Remote sensing indices of vegetation were calculated and correlated with the field-measured values of discolouration and defoliation. Based on that, values of discolouration and defoliation were calculated and evaluated against the field studies. The RMSE of the acquired data was around 16%. Using parameter values, a map of tree damage was drawn up. Based on the analysis, it can be stated that a significant number of trees is undamaged, although a large portion of the trees falls into the warning class.

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Application of remote sensing methods for assessment of anthropogenic transformation of rangelands

Geobotanical mapping ◽

10.31111/geobotmap/1998-2000.16 ◽

2000 ◽

pp. 16-25

Author(s):

E. I. Rachkovskaya ◽

S. S. Temirbekov ◽

R. E. Sadvokasov

Keyword(s):

Remote Sensing ◽

New Technology ◽

Field Studies ◽

Digital Data ◽

Aerial Images ◽

Expert Assessment ◽

Automated Classification ◽

Anthropogenic Transformation ◽

Initial Processing ◽

Remote Sensing Methods

Capabilities of the remote sensing methods for making maps of actual and potential vegetation, and assessment of the extent of anthropogenic transformation of rangelands are presented in the paper. Study area is a large intermountain depression, which is under intensive agricultural use. Color photographs have been made by Aircraft camera Wild Heerburg RC-30 and multispectral scanner Daedalus (AMS) digital aerial data (6 bands, 3.5m resolution) have been used for analysis of distribution and assessment of the state of vegetation. Digital data were processed using specialized program ENVI 3.0. Main stages of the development of cartographic models have been described: initial processing of the aerial images and their visualization, preliminary pre-field interpretation (classification) of the images on the basis of unsupervised automated classification, field studies (geobotanical records and GPS measurements at the sites chosen at previous stage). Post-field stage had the following sub-stages: final geometric correction of the digital images, elaboration of the classification system for the main mapping subdivisions, final supervised automated classification on the basis of expert assessment. By systematizing clusters of the obtained classified image the cartographic models of the study area have been made. Application of the new technology of remote sensing allowed making qualitative and quantitative assessment of modern state of rangelands.

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Fuzzy segmentation of urban areas in panchromatic remote sensing images

Journal of Computer Applications ◽

10.3724/sp.j.1087.2008.03112 ◽

2009 ◽

Vol 28 (12) ◽

pp. 3112-3115

Author(s):

Yan CHEN ◽

Shou-hong WAN ◽

Yu-chang GONG

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Remote Sensing Images ◽

Fuzzy Segmentation

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Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽

10.3390/geosciences11080312 ◽

2021 ◽

Vol 11 (8) ◽

pp. 312

Author(s):

Barbara Wiatkowska ◽

Janusz Słodczyk ◽

Aleksandra Stokowska

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Satellite Imagery ◽

Urban Areas ◽

Agricultural Land ◽

Urban Expansion ◽

Landsat 8 ◽

Rate Of Increase ◽

Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

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Semantic segmentation of PolSAR image data using advanced deep learning model

Scientific Reports ◽

10.1038/s41598-021-94422-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rajat Garg ◽

Anil Kumar ◽

Nikunj Bansal ◽

Manish Prateek ◽

Shashi Kumar

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Urban Area ◽

Urban Areas ◽

Learning Algorithms ◽

Semantic Segmentation ◽

Learning Model ◽

Machine Learning Algorithms ◽

Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

Remote Sensing ◽

10.3390/rs13040699 ◽

2021 ◽

Vol 13 (4) ◽

pp. 699

Author(s):

Tingting Zhou ◽

Haoyang Fu ◽

Chenglin Sun ◽

Shenghan Wang

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Mean Shift ◽

Color Space ◽

Compensation Method ◽

Shadow Detection ◽

Processing Unit ◽

Shadow Region ◽

Remote Sensing Images ◽

Shadow Compensation

Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region.

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4. Internationale Tagung “Remote Sensing of Urban Areas” in Regensburg

KN - Journal of Cartography and Geographic Information ◽

10.1007/bf03544991 ◽

2003 ◽

Vol 53 (5) ◽

pp. 234-235

Author(s):

Matthias Möller

Keyword(s):

Remote Sensing ◽

Urban Areas

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Quantitative Recognition and Characteristic Analysis of Production-Living-Ecological Space Evolution for Five Resource-Based Cities: Zululand, Xuzhou, Lota, Surf Coast and Ruhr

Remote Sensing ◽

10.3390/rs13081563 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1563

Author(s):

Yuanyuan Tao ◽

Qianxin Wang

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Large Scale ◽

Remote Sensing Image ◽

Data Availability ◽

Function Evaluation ◽

Characteristic Analysis ◽

Accurate Identification ◽

Living Space ◽

Evolution Characteristics

The accurate identification of PLES changes and the discovery of their evolution characteristics is a key issue to improve the ability of the sustainable development for resource-based urban areas. However, the current methods are unsuitable for the long-term and large-scale PLES investigation. In this study, a modified method of PLES recognition is proposed based on the remote sensing image classification and land function evaluation technology. A multi-dimensional index system is constructed, which can provide a comprehensive evaluation for PLES evolution characteristics. For validation of the proposed methods, the remote sensing image, geographic information, and socio-economic data of five resource-based urbans (Zululand in South Africa, Xuzhou in China, Lota in Chile, Surf Coast in Australia, and Ruhr in Germany) from 1975 to 2020 are collected and tested. The results show that the data availability and calculation efficiency are significantly improved by the proposed method, and the recognition precision is better than 87% (Kappa coefficient). Furthermore, the PLES evolution characteristics show obvious differences at the different urban development stages. The expansions of production, living, and ecological space are fastest at the mining, the initial, and the middle ecological restoration stages, respectively. However, the expansion of living space is always increasing at any stage, and the disorder expansion of living space has led to the decrease of integration of production and ecological spaces. Therefore, the active polices should be formulated to guide the transformation of the living space expansion from jumping-type and spreading-type to filling-type, and the renovation of abandoned industrial and mining lands should be encouraged.

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A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽

10.3390/land10030231 ◽

2021 ◽

Vol 10 (3) ◽

pp. 231

Author(s):

Can Trong Nguyen ◽

Amnat Chidthaisong ◽

Phan Kieu Diem ◽

Lian-Zhi Huo

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Urban Areas ◽

Near Infrared ◽

Urban Landscape ◽

Bare Soil ◽

Critical Element ◽

Landsat 8 ◽

Climate Conditions ◽

Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

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Predicting Aedes aegypti infestation using landscape and thermal features

Scientific Reports ◽

10.1038/s41598-020-78755-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Camila Lorenz ◽

Marcia C. Castro ◽

Patricia M. P. Trindade ◽

Maurício L. Nogueira ◽

Mariana de Oliveira Lage ◽

...

Keyword(s):

Remote Sensing ◽

Aedes Aegypti ◽

Vector Control ◽

Land Surface ◽

Urban Areas ◽

Negative Binomial ◽

Control Strategies ◽

Winter Season ◽

Landsat 8 ◽

Thermal Features

AbstractIdentifying Aedes aegypti breeding hotspots in urban areas is crucial for the design of effective vector control strategies. Remote sensing techniques offer valuable tools for mapping habitat suitability. In this study, we evaluated the association between urban landscape, thermal features, and mosquito infestations. Entomological surveys were conducted between 2016 and 2019 in Vila Toninho, a neighborhood of São José do Rio Preto, São Paulo, Brazil, in which the numbers of adult female Ae. aegypti were recorded monthly and grouped by season for three years. We used data from 2016 to 2018 to build the model and data from summer of 2019 to validate it. WorldView-3 satellite images were used to extract land cover classes, and land surface temperature data were obtained using the Landsat-8 Thermal Infrared Sensor (TIRS). A multilevel negative binomial model was fitted to the data, which showed that the winter season has the greatest influence on decreases in mosquito abundance. Green areas and pavements were negatively associated, and a higher cover of asbestos roofs and exposed soil was positively associated with the presence of adult females. These features are related to socio-economic factors but also provide favorable breeding conditions for mosquitos. The application of remote sensing technologies has significant potential for optimizing vector control strategies, future mosquito suppression, and outbreak prediction.

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