A comparison of machine learning models for the mapping of groundwater spring potential

Groundwater resources are vitally important in arid and semi-arid areas meaning that spatial planning tools are required for their exploration and mapping. Accordingly, this research compared the predictive powers of five machine learning models for groundwater potential spatial mapping in Wadi az-Zarqa watershed in Jordan. The five models were random forest (RF), boosted regression tree (BRT), support vector machine (SVM), mixture discriminant analysis (MDA), and multivariate adaptive regression spline (MARS). These algorithms explored spatial distributions of 12 hydrological-geological-physiographical (HGP) conditioning factors (slope, altitude, profile curvature, plan curvature, slope aspect, slope length (SL), lithology, soil texture, average annual rainfall, topographic wetness index (TWI), distance to drainage network, and distance to faults) that determine where groundwater springs are located. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the prediction accuracies of the five individual models. Here the results were ranked in descending order as MDA (83.2%), RF (80.6%), SVM (80.2%), BRT (78.0%), and MARS (75.5%).The results show good potential for further use of machine learning techniques for mapping groundwater spring potential in other places where the use and management of groundwater resources is essential for sustaining rural or urban life

Monitoring and Assessing the Coastal Ecosystem at Hurghada, Red Sea Coast, Egypt

In the framework of the investment plan, the government of Egypt introduced an accelerated development of Hurghada in collaboration with the private sector, as   early as 1980's.  The government intended to construct tourist resort communities, which required establishment of infrastructures. The  demand  of  such  facilities, in absence of enforced environmental roles led owners  to implement  processes  of  landfilling  and  dredging  for the purpose of smoothing, paving and widening the beach in order to construct swimming pools, marinas and other recreational facilities. Such activities came on the expense of the marine ecosystem and especially assault on the coral reef communities. For monitoring and assessing such oppressive activities;  MSS,  TM,  ETM+,  and SPOT XS 4 satellite images acquired  during  1972,  1984,  1992,  2004  and 2011. Shoreline change detection from 1972 to 2011 reveals landfilling of some 7.56Km2 and dredging of 2.67km2, with loss of 5.34km2 of the reef tracts. At the same period, the region has witnessed expansions in urban and road network by 16.47km2 and 8.738km2 respectively. The Egyptian government issued the essential laws for regulating and saving the coastal ecosystem, yet mostly violated. Activation of such laws, applying judicial officers, toughening penalties and establishment of coastal building front line (CBFL), and a reef protection line (RPL) are important tasks especially south of Hurghada to the Egyptian-Sudanese borders to preserve the remnants of such unique coastal ecosystem. Keywords: Satellite images, Hurghada, Red Sea, coastal ecosystem, shoreline changes, urban, road network, environment law

Uso de aprendizagem de máquina e redes neurais convolucionais profundas para a classificação de áreas queimadas em imagens de alta resolução espacial

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2019.Os incêndios florestais queimam enorme quantidade de áreas em todo o mundo, provocando danos ecológicos, econômicos, sociais e à saúde. O Bioma Cerrado com as peculiaridades de ser uma savana possui relação com incêndios, sendo afetada por esse tipo de ocorrência. O monitoramento desses eventos de fogo favorece a compreensão e entendimento das ocorrências, sendo o sensoriamento remoto ferramenta adequada para obtenção de dados relativos ao fogo em diferentes escalas. O uso de machine learning e deep learning em sensoriamento remoto possui diversas finalidades, sendo a classificação de imagens uma importante componente. Nesse estudo, três algoritmos de machine learning (Support Vector Machine, K-Nearest Neighbors e Random Forest) e a Convolutional Neural Network (CNN) foram testados para a classificação de imagens da frota SkySat da Planet com alta resolução espacial visando à identificação de áreas queimadas. A classificação foi feita por meio de cenas individuais, com coleta de amostras para treinamento e posterior classificação. Os resultados das classificações foram avaliados por meio da exatidão global, coeficiente Kappa e AUROC e confrontados entre si. A CNN obteve os melhores resultados sendo seguida pelo KNN, SVM e RF. Em relação à acurácia, não foi evidenciada grande diferença entre os métodos, sendo necessários novos estudos buscando avaliar diferentes classificações.Forest fires burns huge number of areas around the world, causing ecological, economic, social and health damage. The Cerrado Biome with its peculiarities of being a savannah is related to fires, being affected by this type of occurrence. The monitoring of fire events favors the understanding of occurrences, and remote sensing is an adequate tool to obtain fire data at different scales. The use of machine learning and convolutional neural networks in remote sensing have several purposes, and image classification is an important component. In this study, three machine learning algorithms (Support Vector Machine, K-Nearest Neighbors and Random Forest) and a convolutional neural network - CNN were tested for the classification of images from the Planet´s SkySat fleet with a high spatial resolution for the identification of burned areas. The classification was made in individual scenes, with sample collection for training and subsequent classification. The results of the classifications were evaluated by global accuracy, Kappa index and AUROC and compared to each other. CNN obtained the best results being followed by KNN, SVM and RF. Regarding accuracy, there was no evidence of great difference between the methods, and further studies are needed to evaluate different classifications

Quantifying Surface Urban Heat Island Formation in the World Heritage Tropical Mountain City of Sri Lanka

Presently, the urban heat island (UHI) phenomenon, and its adverse impacts, are becoming major research foci in various interrelated fields due to rapid changes in urban ecological environments. Various cities have been investigated in previous studies, and most of the findings have facilitated the introduction of proper mitigation measures to overcome the negative impact of UHI. At present, most of the mountain cities of the world have undergone rapid urban development, and this has resulted in the increasing surface UHI (SUHI) phenomenon. Hence, this study focuses on quantifying SUHI in Kandy City, the world heritage tropical mountain city of Sri Lanka, using Landsat data (1996 and 2017) based on the mean land surface temperature (LST), the difference between the fraction of impervious surfaces (IS), and the fraction of green space (GS). Additionally, we examined the relationship of LST to the green space/impervious surface fraction ratio (GS/IS fraction ratio) and the magnitude of the GS/IS fraction ratio. The SUHI intensity (SUHII) was calculated based on the temperature difference between main land use/cover categories and the temperature difference between urban-rural zones. We demarcated the rural zone based on the fraction of IS recorded, <10%, along with the urban-rural gradient zone. The result shows a SUHII increase from 3.9 °C in 1996 to 6.2 °C in 2017 along the urban-rural gradient between the urban and rural zones (10 < IS). These results relate to the rapid urban expansion of the study areas from 1996 to 2017. Most of the natural surfaces have changed to impervious surfaces, causing an increase of SUHI in Kandy City. The mean LST has a positive relationship with the fraction of IS and a negative relationship with the fraction of GS. Additionally, the GS/IS fraction ratio shows a rapid decline. Thus, the findings of this study can be considered as a proxy indicator for introducing proper landscape and urban planning for the World Heritage tropical mountain city of Kandy in Sri Lanka

Mapping Secondary Vegetation of a Region of Deforestation Hotspot in the Brazilian Amazon: Performance Analysis of C- and L-Band SAR Data Acquired in the Rainy Season.

Abstract: The re-suppression of secondary vegetation (SV) in the Brazilian Amazon for agriculture or land speculation occurs mostly in the rainy season. The use of optical images to monitor such re-suppression during the rainy season is limited because of the persistent cloud cover. This study aimed to evaluate the potential of C- and L-band SAR data acquired in the rainy season to dis- criminate SV in an area of new hotspot of deforestation in the municipality of Colniza, northwest- ern of Mato Grosso State, Brazil. This is the first time that the potential of dual-frequency SAR data was analyzed to discriminate SV, with an emphasis on data acquired during the rainy season. The L-band ALOS/PALSAR-2 and the C-band Sentinel-1 data acquired in March 2018 were processed to obtain backscattering coefficients and nine textural attributes were derived from the gray level co-occurrence matrix method (GLCM). Then, we classified the images based on the non-parametric Random Forest (RF) and Support Vector Machine (SVM) algorithms. The use of SAR textural attributes improved the discrimination capability of different LULC classes found in the study area. The results showed the best performance of ALOS/PALSAR-2 data classified by the RF algo- rithm to discriminate the following representative land use and land cover classes of the study area: primary forest, secondary forest, shrubby pasture, clean pasture, and bare soil, with an over- all accuracy and Kappa coefficient of 84% and 0.78, respectively. The RF outperformed the SVM classifier to discriminate these five LULC classes in 14% of overall accuracy for both ALOS-2 and Sentinel-1 data sets. This study also showed that the textural attributes derived from the GLCM method are highly sensitive to the moving window size to be applied to the GLCM method. The results of this study can assist the future development of an operation system based on du- al-frequency SAR data to monitor re-suppression of SV in the Brazilian Amazon or in other tropical rainforests

An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time

In the case of ongoing wildfire events, timely information on current fire parameters is crucial for informed decision making. Satellite imagery can provide valuable information in this regard, since thermal sensors can detect the exact location and intensity of an active fire at the moment the satellite passes over. This information can be derived and distributed in near-real time, allowing for a picture of current fire activity. However, the derivation of the size and shape of an already affected area is more complex and therefore most often not available within a short time frame. For urgent decision making though, it would be desirable to have this information available in near-real time, and on a large scale. The approach presented here works fully automatic and provides perimeters of burnt areas within two hours after the satellite scene acquisition. It uses the red and near-infrared bands of mid-resolution imagery to facilitate continental-scale monitoring of recently occurred burnt areas. To allow for a high detection capacity independent of the affected vegetation type, segmentation thresholds are derived dynamically from contextual information. This is done by using a Morphological Active Contour approach for perimeter determination. The results are validated against semi-automatically derived burnt areas for five wildfire incidents in Europe. Furthermore, these results are compared with three widely used burnt area datasets on a country-wide scale. It is shown that a high detection quality can be reached in near real-time. The large-scale inter-comparison shows that the results coincide with 63% to 76% of the burnt area in the reference datasets. While these established datasets are only available with a time lag of several months or are created by using manual interaction, the presented approach produces results in near-real time fully automatically. This work is therefore supposed to represent a valuable improvement in wildfire related rapid damage assessment