Internet of Things for Sustainable Forestry

Forests and grasslands play an important role in water and air purification, prevention of the soil erosion, and in provision of habitat to wildlife. Internet of Things has a tremendous potential to play a vital role in the forest ecosystem management and stability. The conservation of species and habitats, timber production, prevention of forest soil degradation, forest fire prediction, mitigation, and control can be attained through forest management using Internet of Things. The use and adoption of IoT in forest ecosystem management is challenging due to many factors. Vast geographical areas and limited resources in terms of budget and equipment are some of the limiting factors. In digital forestry, IoT deployment offers effective operations, control, and forecasts for soil erosion, fires, and undesirable depositions. In this chapter, IoT sensing and communication applications are presented for digital forestry systems. Different IoT systems for digital forest monitoring applications are also discussed

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ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88 % for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6 °C for 2001 and 6.8 °C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r2 = 0.7 and r2 = 0.9 for 2001 and 2014, respectively)

PLASTIC AND GLASS GREENHOUSES DETECTION AND DELINEATION FROM WORLDVIEW-2 SATELLITE IMAGERY

Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML), Random Forest (RF), and Support Vector Machines (SVM) classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery

BUILDING DETECTION FROM PAN-SHARPENED IKONOS IMAGERY THROUGH SUPPORT VECTOR MACHINES CLASSIFICATION

An approach is presented for detecting the buildings from high resolution pan-sharpened IKONOS imagery through binary Support Vector Machines (SVM) classification. In addition to original spectral bands, the bands nDSM (normalized Digital Surface Model), NDVI (Normalized Difference Vegetation Index), PC1, PC2, PC3, and PC4 (First, Second, Third, and Fourth Principal Components), are also included in the classification. The proposed classification procedure was carried out in three study areas selected in the Batikent district of Ankara, Turkey. The study areas show different residential and industrial characteristics. The first study area covers mainly the residential parts that include buildings with different shapes, sizes, dwelling types, and colored roofs. The second study area also represents the residential characteristics but contains buildings with more regular shapes. The third study area contains the industrial buildings with the gray tone roofs and the sizes of the buildings are larger. Also tested in the present study is the effect of the training sample size in the accuracy of the SVM classification. The results reveal that the overall accuracies were computed to be between 90% and 99%, while the kappa coefficients were found to be between 0.80 and 0.98. The inclusion of additional bands in the SVM classification had a considerable effect in the accuracy of building detection. Increasing the training size increased the accuracy, however, the increase was not more than 3%

ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88&amp;thinsp;% for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6&amp;thinsp;&amp;deg;C for 2001 and 6.8&amp;thinsp;&amp;deg;C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;&amp;thinsp;=&amp;thinsp;0.7 and r&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;&amp;thinsp;=&amp;thinsp;0.9 for 2001 and 2014, respectively).</jats:p

ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

Abstract. The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88 % for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6 °C for 2001 and 6.8 °C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r2 = 0.7 and r2 = 0.9 for 2001 and 2014, respectively). </jats:p

SPATIOTEMPORAL LAND USE CHANGE ANALYSIS AND FUTURE URBAN GROWTH SIMULATION USING REMOTE SENSING: A CASE STUDY OF ANTALYA

Abstract. The objectives of this study are: to create land-use maps by 5-year interval from 1995 to 2015, to analyse the land use change and urban development, and to estimate future land-use pattern and urban growth for the years: 2030, 2045 and 2060. Antalya, which is the 5th biggest city of Turkey, was selected as study area. In this study, there are basically three stages: (i) preprocessing and preparing additional bands, (ii) spatiotemporal land use detection using image classification and (iii) land use simulation using urban growth models. Firstly, atmospheric correction was applied to the Landsat 5 TM and Landsat 8 OLI images and land-cover indices, ASTER Global Digital Elevation Model (GDEM), and Nighttime data were prepared to use them as additional bands during the classification process. Secondly, Landsat images were classified using Random Forest (RF) machine-learning algorithm. Thirdly, urban simulations were performed for the years 2005, 2010, and 2015 and land-use pattern and urban growth was estimated for the years 2030, 2045 and 2060. The RF classification accuracies range from 84.44% to 92.82%. The urban areas increased from 49.56 km2 to 96.25 km2 from 1995 to 2015. The simulation accuracies were computed above 80%. According to the 2030, 2045 and 2060 simulation results, the urban areas were computed as 133.61 km2, 148.27 km2 and 156.85 km2, respectively. As a result, it was seen that the urban area of Antalya has almost doubled between the years 1995–2015 and the urban expansion is expected to continue increasing up to 1960. </jats:p

Automatic building extraction from high resolution satellite images for map updating: A model based approach

An approach was developed for automatically updating the buildings of an existing vector database from high resolution satellite images using spectral image classification, Digital Elevation Models (DEM) and the model-based extraction techniques. First, the areas that contain buildings are detected using spectral image classification and the normalized Digital Surface Model (nDSM). The classified output provides the shapes and the approximate locations of the buildings. However, those buildings that have similar reflectance values with the other classes were not able to be detected. Therefore, nDSM was generated by subtracting the Digital Terrain Model (DTM) from the Digital Surface Model (DSM). Next, the buildings were differentiated from the trees by using the Normalized Difference Vegetation Index (NDVI). Areas other than the buildings are excluded from further processing. The buildings that exist in the vector database but missing in the image were detected through analyzing the results of the classification and nDSM. Finally, the buildings constructed after the date of the compilation of the existing vector database were extracted through the proposed model-based approach and the vector database was updated with the new building boundaries. The method was implemented in a selected urban area in Ankara, Turkey using the IKONOS pan-sharpened and panchromatic images. The results show that the proposed approach is quite satisfactory for detecting and delineating the buildings from high resolution space images

Building Detection From Pan-Sharpened Ikonos Imagery Through Support Vector Machines Classification

An approach is presented for detecting the buildings from high resolution pan-sharpened IKONOS imagery through binary Support Vector Machines (SVM) classification. In addition to original spectral bands, the bands nDSM (normalized Digital Surface Model), NDVI (Normalized Difference Vegetation Index), PC1, PC2, PC3, and PC4 (First, Second, Third, and Fourth Principal Components), are also included in the classification. The proposed classification procedure was carried out in three study areas selected in the Batikent district of Ankara, Turkey. The study areas show different residential and industrial characteristics. The first study area covers mainly the residential parts that include buildings with different shapes, sizes, dwelling types, and colored roofs. The second study area also represents the residential characteristics but contains buildings with more regular shapes. The third study area contains the industrial buildings with the gray tone roofs and the sizes of the buildings are larger. Also tested in the present study is the effect of the training sample size in the accuracy of the SVM classification. The results reveal that the overall accuracies were computed to be between 90% and 99%, while the kappa coefficients were found to be between 0.80 and 0.98. The inclusion of additional bands in the SVM classification had a considerable effect in the accuracy of building detection. Increasing the training size increased the accuracy, however, the increase was not more than 3%.Wo