Browsing by Author "Altunel, A.O."

Now showing 1 - 8 of 8

Assessment of the global Copernicus, NASADEM, ASTER and AW3D digital elevation models in Central and Southern Africa
(Taylor and Francis Ltd., 2024) Okolie, C.J.; Mills, J.P.; Adeleke, A.K.; Smit, J.L.; Peppa, M.V.; Altunel, A.O.; Arungwa, I.D.
Validation studies of global Digital Elevation Models (DEMs) in the existing literature are limited by the diversity and spread of landscapes, terrain types considered and sparseness of groundtruth. Moreover, there are knowledge gaps on the accuracy variations in rugged and complex landscapes, and previous studies have often not relied on robust internal and external validation measures. Thus, there is still only partial understanding and limited perspective of the reliability and adequacy of global DEMs for several applications. In this study, we utilize a dense spread of LiDAR groundtruth to assess the vertical accuracies of four medium-resolution, readily available, free-access and global coverage 1 arc-second (30 m) DEMs: NASADEM, ASTER GDEM, Copernicus GLO-30, and ALOS World 3D (AW3D). The assessment is carried out at landscapes spread across Cape Town, Southern Africa (urban/industrial, agricultural, mountain, peninsula and grassland/shrubland) and forested national parks in Gabon, Central Africa (low-relief tropical rainforest and high-relief tropical rainforest). The statistical analysis is based on robust accuracy metrics that cater for normal and non-normal elevation error distribution, and error ranking. In Cape Town, Copernicus DEM generally had the least vertical error with an overall Mean Error (ME) of 0.82 m and Root Mean Square Error (RMSE) of 2.34 m while ASTER DEM had the poorest performance. However, ASTER GDEM and NASADEM performed better in the low-relief and high-relief tropical forests of Gabon. Generally, the DEM errors have a moderate to high positive correlation in forests, and a low to moderate positive correlation in mountains and urban areas. Copernicus DEM showed superior vertical accuracy in forests with less than 40% tree cover, while ASTER and NASADEM performed better in denser forests with tree cover greater than 70%. This study is a robust regional assessment of these global DEMs.
Assessment of the global Copernicus, NASADEM, ASTER and AW3D digital elevation models in Central and Southern Africa
(2024.01.01) Okolie, C.J.; Mills, J.P.; Adeleke, A.K.; Smit, J.L.; Peppa, M.V.; Altunel, A.O.; Arungwa, I.D.
Validation studies of global Digital Elevation Models (DEMs) in the existing literature are limited by the diversity and spread of landscapes, terrain types considered and sparseness of groundtruth. Moreover, there are knowledge gaps on the accuracy variations in rugged and complex landscapes, and previous studies have often not relied on robust internal and external validation measures. Thus, there is still only partial understanding and limited perspective of the reliability and adequacy of global DEMs for several applications. In this study, we utilize a dense spread of LiDAR groundtruth to assess the vertical accuracies of four medium-resolution, readily available, free-access and global coverage 1 arc-second (30 m) DEMs: NASADEM, ASTER GDEM, Copernicus GLO-30, and ALOS World 3D (AW3D). The assessment is carried out at landscapes spread across Cape Town, Southern Africa (urban/industrial, agricultural, mountain, peninsula and grassland/shrubland) and forested national parks in Gabon, Central Africa (low-relief tropical rainforest and high-relief tropical rainforest). The statistical analysis is based on robust accuracy metrics that cater for normal and non-normal elevation error distribution, and error ranking. In Cape Town, Copernicus DEM generally had the least vertical error with an overall Mean Error (ME) of 0.82 m and Root Mean Square Error (RMSE) of 2.34 m while ASTER DEM had the poorest performance. However, ASTER GDEM and NASADEM performed better in the low-relief and high-relief tropical forests of Gabon. Generally, the DEM errors have a moderate to high positive correlation in forests, and a low to moderate positive correlation in mountains and urban areas. Copernicus DEM showed superior vertical accuracy in forests with less than 40% tree cover, while ASTER and NASADEM performed better in denser forests with tree cover greater than 70%. This study is a robust regional assessment of these global DEMs.
Comparison of SAR and Optical Data used in Forest Cover Detection; PALSAR-FNF vs. ESRI LAND-COVER over North Central Türkiye
(2024.01.01) Altunel, A.O.; Çelik, D.A.
Swiftly and reliably establishing a spatially and geometrically correct land-cover map of any region is rather important in natural resource planning for conservation and utilization. JAXA's PALSAR2/PALSAR/JERS-1 Mosaic and Forest / Non-forest maps, which as the name suggested, have specifically focused on global forest cover since 2007, benefiting from L-band SAR imagery. ESRI Land-cover, on the other hand, owing to exceptional Sentinel-2 imagery, has produced rather detailed land-cover maps including a distinct forest class. In this particular study, coverages of 2017-2020 readied by both institutions, utilizing the aforementioned imageries, were questioned on yearly basis against a rather detailed geodatabase which is still-in-effective use by two of the current regional directorates of forestry, Kastamonu and Sinop in T & uuml;rkiye, utilizing long adopted accuracy metrics (user, producer and overall accuracies). When all year coverages were concerned, the best overall accuracies were held with 82% in 2017 ESRI land-cover and 83% in 2017 PALSAR-FNF. Both datasets yielded relatively good results in the forest class when user accuracies were investigated. ESRI land-covers managed more than 87% across all four years, while PALSAR-FNFs produced 84.33% in 2020 as the highest scoring year. As for producer accuracies, PALSAR-FNFs produced over 89% across all year coverages, while ESRI produced 84% in 2017 as the highest scoring year. It is worth noting that the ESRI land-covers had better compliance with the compartment boundaries of the reference geodatabase.
Comparison of SAR and Optical derived Data used in Forest Cover Detection; PALSAR-FNF vs. ESRI LAND-COVER over North Central Turkiye (oct, 10.1007/s13762-024-06164-9, 2024)
(2024.01.01) Altunel, A.O.; Çelik, D.A.
Monitoring the operational changes in surface reflectances after logging, based on popular indices over Sentinel-2, Landsat-8, and ASTER imageries
(2025) Genç, Ç.Ö.; Altunel, A.O.
Revealing the status of forests is important for sustainable forest management. The basis of the concept lies in meeting the needs of future generations and today's generations in the management of forests. The use of remote-sensing (RS) technologies and geographic information systems (GIS) techniques in revealing the current forest structure and in long-term planning of forest areas with multipurpose planning techniques is increasing day by day. Significant technological advances are in allowing programmers to modernize how they manage data. Sentinel-2, which is a relatively new addition to Earth observing satellites, is a new-generation satellite that has enabled classification and monitoring of land cover change with high precision at ease. Visible R, G, B, and near-infrared (NIR) bands have offered exceptional 10-m spatial reasolution, making them suitable for vegetation monitoring along with the additional 20-m bands to spare especially in chlorophyll content analyses. On the contrary, Landsat-8 and ASTER which have been longer lasting in Earth observation were rougher results especially in forestry studies. In this study, Landsat-8 and ASTER satellite images were compared against the Sentinel-2 images as a reference in conjunction with GIS techniques to monitor and assess the impact of various logging procedures, including selective logging and regeneration silviculture. The investigation employed a range of plant vegetation indices, including NDVI, GNDVI, and SAVI, to evaluate the efficacy of image resolution in detecting forest cover changes in the Kastamonu region, where the timber production is the hightest in Turkey. For selective and regeneration activities, satellite images were taken pre-harvesting and immediately post-harvesting, and index maps were produced. NDVI, GNDVI, and SAVI indices were the most accurate indicators of green vegetation change in the Sentinel-2A imagery. Similarly, for the Landsat-8 imagery, the SAVI, NDVI, and GNDVI indices were found to be satisfactory indicators. As for ASTER imagery, the success sequance was like SAVI, GNDVI, and NDVI. Based on the findings of this study, it has been noted that the ASTER imagery closeness to Sentinel-2A was more remarkable in detecting changes in green vegetation in forested areas. The data derived from ASTER imageries demonstrated superior efficacy compared to Landsat-8 in generating forest cover maps, owing to their proximity to those produced by Sentinel-2. The findings also indicated that ASTER imagery, with suitable spatial and spectral resolution, could still be utilized as efficienly as Landsats to generate forest cover density maps and monitor long-term forest conservation practices, particularly in professionally managed forests. Thus, this methodology demonstrated the capacity for efficient worldwide forest management.
Monitoring the operational changes in surface reflectances after logging, based on popular indices over Sentinel-2, Landsat-8, and ASTER imageries
(2025) Genç, Ç.Ö.; Altunel, A.O.
Revealing the status of forests is important for sustainable forest management. The basis of the concept lies in meeting the needs of future generations and today's generations in the management of forests. The use of remote-sensing (RS) technologies and geographic information systems (GIS) techniques in revealing the current forest structure and in long-term planning of forest areas with multipurpose planning techniques is increasing day by day. Significant technological advances are in allowing programmers to modernize how they manage data. Sentinel-2, which is a relatively new addition to Earth observing satellites, is a new-generation satellite that has enabled classification and monitoring of land cover change with high precision at ease. Visible R, G, B, and near-infrared (NIR) bands have offered exceptional 10-m spatial reasolution, making them suitable for vegetation monitoring along with the additional 20-m bands to spare especially in chlorophyll content analyses. On the contrary, Landsat-8 and ASTER which have been longer lasting in Earth observation were rougher results especially in forestry studies. In this study, Landsat-8 and ASTER satellite images were compared against the Sentinel-2 images as a reference in conjunction with GIS techniques to monitor and assess the impact of various logging procedures, including selective logging and regeneration silviculture. The investigation employed a range of plant vegetation indices, including NDVI, GNDVI, and SAVI, to evaluate the efficacy of image resolution in detecting forest cover changes in the Kastamonu region, where the timber production is the hightest in Turkey. For selective and regeneration activities, satellite images were taken pre-harvesting and immediately post-harvesting, and index maps were produced. NDVI, GNDVI, and SAVI indices were the most accurate indicators of green vegetation change in the Sentinel-2A imagery. Similarly, for the Landsat-8 imagery, the SAVI, NDVI, and GNDVI indices were found to be satisfactory indicators. As for ASTER imagery, the success sequance was like SAVI, GNDVI, and NDVI. Based on the findings of this study, it has been noted that the ASTER imagery closeness to Sentinel-2A was more remarkable in detecting changes in green vegetation in forested areas. The data derived from ASTER imageries demonstrated superior efficacy compared to Landsat-8 in generating forest cover maps, owing to their proximity to those produced by Sentinel-2. The findings also indicated that ASTER imagery, with suitable spatial and spectral resolution, could still be utilized as efficienly as Landsats to generate forest cover density maps and monitor long-term forest conservation practices, particularly in professionally managed forests. Thus, this methodology demonstrated the capacity for efficient worldwide forest management.
Monitoring the operational changes in surface reflectances after logging, based on popular indices over Sentinel-2, Landsat-8, and ASTER imageries
(2025.01.01) Genç, Ç.Ö.; Altunel, A.O.
Revealing the status of forests is important for sustainable forest management. The basis of the concept lies in meeting the needs of future generations and today's generations in the management of forests. The use of remote-sensing (RS) technologies and geographic information systems (GIS) techniques in revealing the current forest structure and in long-term planning of forest areas with multipurpose planning techniques is increasing day by day. Significant technological advances are in allowing programmers to modernize how they manage data. Sentinel-2, which is a relatively new addition to Earth observing satellites, is a new-generation satellite that has enabled classification and monitoring of land cover change with high precision at ease. Visible R, G, B, and near-infrared (NIR) bands have offered exceptional 10-m spatial reasolution, making them suitable for vegetation monitoring along with the additional 20-m bands to spare especially in chlorophyll content analyses. On the contrary, Landsat-8 and ASTER which have been longer lasting in Earth observation were rougher results especially in forestry studies. In this study, Landsat-8 and ASTER satellite images were compared against the Sentinel-2 images as a reference in conjunction with GIS techniques to monitor and assess the impact of various logging procedures, including selective logging and regeneration silviculture. The investigation employed a range of plant vegetation indices, including NDVI, GNDVI, and SAVI, to evaluate the efficacy of image resolution in detecting forest cover changes in the Kastamonu region, where the timber production is the hightest in Turkey. For selective and regeneration activities, satellite images were taken pre-harvesting and immediately post-harvesting, and index maps were produced. NDVI, GNDVI, and SAVI indices were the most accurate indicators of green vegetation change in the Sentinel-2A imagery. Similarly, for the Landsat-8 imagery, the SAVI, NDVI, and GNDVI indices were found to be satisfactory indicators. As for ASTER imagery, the success sequance was like SAVI, GNDVI, and NDVI. Based on the findings of this study, it has been noted that the ASTER imagery closeness to Sentinel-2A was more remarkable in detecting changes in green vegetation in forested areas. The data derived from ASTER imageries demonstrated superior efficacy compared to Landsat-8 in generating forest cover maps, owing to their proximity to those produced by Sentinel-2. The findings also indicated that ASTER imagery, with suitable spatial and spectral resolution, could still be utilized as efficienly as Landsats to generate forest cover density maps and monitor long-term forest conservation practices, particularly in professionally managed forests. Thus, this methodology demonstrated the capacity for efficient worldwide forest management.
Topografik harita üretim tekniklerine ilişkin yükseklik hassasiyetlerinin arazi örtüsü tipi bağlamında karşılaştırılması
(2024) Altunel, A.O.; Sakıcı, O.E.
Ülkemizde, teknolojik gelişmelere bağlı olarak 1950’lerin sonlarından günümüze kadar farklı ölçeklerde birçok topografik harita üretilmiş ve hizmete sunulmuştur. Bu çalışmada, 1992-1993 yıllarında analog imkanlar çerçevesinde üretilmiş 1:25.000 ölçekli topografik haritalardan elde edilen yükseklik değerleri ile haritacılık sektöründeki teknolojik gelişmelere paralel olarak yakın geçmişte (2009-2010) dijital imkanlarla üretilen topografik haritalardan elde edilen yükseklik değerleri üç farklı arazi örtüsü tipi (ziraat, parçalı orman ve orman) üzerinden CORS-GPS kullanılarak elde edilmiş yersel referans verilerine (ziraat formundaki çalışma sahasında 615 adet, parçalı orman formundaki sahada 3688 adet ve orman sahasında 1739 adet) dayalı olarak karşılaştırılmıştır. Karşılaştırmalarda, raster verilerin doğrudan kullanıldığı iki yöntem (Kesilmiş pafta (KP) ve Tam pafta (TP) yöntemleri) ve yeniden örnekleme ile elde edilen raster verilerin kullanıldığı iki yöntem (10 m mekansal çözünürlükle yeniden örnekleme (R10) ve 30 m mekansal çözünürlükle yeniden örnekleme (R30) yöntemleri) olmak üzere dört farklı raster yüzey modelinden elde edilen yükseklik değerlerinden yararlanılmıştır. Çalışma sonuçları, dijitalleşmenin topografik haritaların yükseklik hassasiyetleri üzerinde olumlu katkılar sağladığını göstermiştir. Analog ve dijital teknikle üretilen haritalar arasındaki yükseklik hassasiyetlerindeki farklılık ziraat arazi örtüsü tipinde oldukça belirgin iken, parçalı orman ve orman alanlarında dijitalleşme ile hassasiyet artışının daha düşük seviyelerde kaldığı belirlenmiştir. Ayrıca, raster veri üretiminde yeniden örnekleme yoluna gidilerek yükseklik değerleri tahmininde daha başarılı sonuçlar elde edilebileceği sonucuna ulaşılmıştır.