Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy.

Land surface temperature (LST) is a direct impact of urbanization and a crucial factor in global climate and land cover changes. In this research, we aim to identify the impact of land use/land cover (LULC) on LST as well as analyze the relationship between LST and three spectral indices using linear, polynomial and multiple regression models. The LST was first retrieved from Landsat imagery using single-channel algorithm. Afterwards, LULC maps were developed using maximum likelihood (ML) classifier and three spectral indices, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). Finally, regression analysis was conducted to model the relationship between LST and the three spectral indices. Landsat 8 OLI/TIRS imagery of year 2019 of Dakahlia Governorate in Egypt was processed for LST retrieval as well as LULC classification. The ML classifier achieved an overall accuracy and kappa coefficient of 95.14% and 0.857, respectively, while of those based on spectral indices were 94.86% and 0.777, respectively. The results demonstrated an average temperature of 35.8°C, 31.2°C and 27.6°C for urban, vegetation and water, respectively. The LST statistics difference between classification methods of the three land covers was less 2°C. Based on the regression analysis, the NDVI and NDWI indicated a negative correlation with LST, while the NDBI indicated a positive correlation with LST. The polynomial regression analysis of LST against NDVI and NDWI demonstrated a better coefficient of determination (R2) than linear regression analysis of 0.341 and 0.305, respectively. For NDBI, linear and polynomial regression analysis demonstrated very close R2 of 0.624 and 0.628, respectively. The multiple regression analysis of LST against NDVI, NDBI and NDWI revealed R2 of 0.699. Consequently, the three spectral indices can be used as effective indicators for separating terrain into different classes, and hence relate their LST.

Green cities worldwide are converting to renewable clean energy from natural sources such as sunlight and wind due to the lack of traditional resources and the significant increase in environmental pollution. This paper presents an approach of two stages for photovoltaic (PV) potential estimation of solar panels mounted on buildings’ rooftops. The first stage is rooftop detection from satellite images using a series of image pre-processing algorithms, followed by applying machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). The second stage is the solar PV potential estimation using the PVWatts calculator, PVGIS, and ArcGIS. Satellite images for the B6 division of Madinaty City in Egypt were evaluated in this paper. The precision, recall, and F1-score of rooftop detection were 91.2%, 98.6%, and 94.7% from SVM, while those from NB were 86.6%, 98.3%, and 92.2%, respectively. About 290 rooftops were extracted, with a total area of 150,698 m² and a relative root mean square error of 10.6%. The usable area of rooftops was utilized to estimate the annual PV potential of 21.1, 24.9, and 22.9 GWh/year from the PVWatts calculator, PVGIS, and ArcGIS, respectively. According to the estimated PV potential, replacing traditional energy sources reduced the amount of CO₂ by an annual average value of 62%.

Coastal areas are environmentally sensitive and are affected by nature events and human activities. Land/water interaction in coastal areas changes over time and, therefore, requires accurate detection and frequent monitoring. Multispectral Light Detection and Ranging (LiDAR) systems, which operate at different wavelengths, have become available. This new technology can provide an effective and accurate solution for the determination of the land/water interface. In this context, we aim to investigate a set of point features based on elevation, intensity, and geometry for this application, followed by a presentation of an unsupervised land/water discrimination method based on seeded region growing algorithm. The multispectral airborne LiDAR sensor, the Optech Titan, was used to acquire LiDAR data at three wavelengths (1550, 1064, and 532 nm) of a study area covering part of Lake Ontario in Scarborough, Canada for testing the discrimination methods. The elevation- and geometry-based features achieved an average overall accuracy of 75.1% and 74.2%, respectively, while the intensity-based features achieved 63.9% accuracy. The region growing method succeeded in discriminating water from land with more than 99% overall accuracy, and the land/water boundary was delineated with an average root mean square error of 0.51 m. The automation of this method is restricted by having double returns from water bodies at the 532 nm wavelength.