Geospatial technologies for physical planning: Bridging the gap between earth science and planning

The application of geospatial information technologies has increased recently due to increase in data sources from the earth sciences. The systematic data collection, storage and processing together with data transformation require geospatial information technologies. Rapidly developing computer technology has become an effective tool in design and physical planning in international platforms. Especially, the availability of geospatial information technologies (remote sensing, GIS, spatial models and GPS) for diverse disciplines and the capability of these technologies in data conversion from two dimensions to the three dimensions provide great efficiency. Thus, this study explores how digital technologies are reshaping physical planning and design. While the potential of digital technologies is well documented within physical planning and visualization, its application within practice is far less understood. This paper highlights the role of the geospatial information technologies in encouraging a new planning and design logic that moves from the privileging of the visual to a focus on processes of formation, bridging the interface of the earth science and physical planning

İklim Değişikliği Senaryoları Altında Konumsal Modelleme Kullanarak Türkiye'nin Çevresel Risk Analizi: Net Birincil Üretim Örneği

Çalışmanın amacı, Türkiye"de bölgesel iklim değişikliğinin Net Birincil Üretim (NPP)"e etkilerinin biyokimyasal modelleme yaklaşımı ile tahmin edilmesidir. Güncel ve gelecek iklim koşullarında karasal NPP"in yıllık bölgesel döngülerinin tahmininde CASA modeli kullanılmıştır. Modelin oluşturulmasında ağaç kapalılık yüzdesi, arazi örtüsü, toprak tekstürü, Normalleştirilmiş Fark Vejetasyon İndeksi (NDVI) ve iklim değişkenlerinden oluşan geniş bir veri seti kullanılmıştır. Çoklu zamansal metrikler 250 m çözünürlü MODIS verileri kullanılarak üretilmiştir. Gelecek tahmini için IPCC"nin 5. Değerlendirme Raporunda tanımlanan RCP (Representative Concentration Pathways) senaryoları baz alınmıştır. Bu kapsamda, 1,1ºC ile 2,0ºC arasında sıcaklık ve 421 ppm"e kadar CO2 artışı limit alınmıştır. Model sonuçları, Türkiye için ortalama NPP değerinin 1232 gCm2y-1 olduğunu göstermiştir. Karasal NPP güncel durum için 9,61 to 316,1 gCm2y-1 değişmektedir. Modellenen yıllık toplam NPP ise 2060-2080 yılları için 1320,8 gCm2y-1"dir. Toplam karbon bütçesi yıllık 104,78 milyon ton tahmin edilmiştir. Model sonuçları karasal NPP"nin sıcaklık ve yağış değişimlerine hassas olduğunu göstermiştir. CASA modeli, güncel ve gelecek NPP değerlerinin hesaplanmasında bölgesel temelde başarılı sonuçlar vermiştir. Bu çalışma, Türkiye"de iklim değişikliği altında ekolojik ve ekonomik sonuçların ortaya konması yardımcı veriler üretilmesi bakımından önem taşımaktadırThe aim of this study is to estimate the response of NPP to regional climate changes in Turkey using a biogeochemical modelling approach. The CASA model was utilized to predict annual regional fluxes in terrestrial net primary production for present (2000-2010) and future (2060-2080) climate conditions. A comprehensive data set including percentage of tree cover, land cover map, soil texture, NDVI (Normalized Difference Vegetation Index) and climate variables were used to constitute the model. The multi-temporal metrics were produced using 16 days MODIS composites with 250 m spatial resolution. The future climate projections were based on a RCP (Representative Concentration Pathways) scenario that was defined in 5thAssessment Report of IPCC. In this context, the future NPP modelling was performed with prescribed CO2 concentrations up to 421 ppm and temperature increasing 1.1ºC to 2.0ºC.The model results indicated that the NPP in Turkey averages 1232 gCm2y-1. Terrestrial NPP ranges from 9.61 to 316.1 gCm2y-1 for the baseline period (2000-2010). Modeled total NPP averages 1320.8 gCm2y-1 per year in the period 2060-2080. Total carbon budget of NPP was estimated as 104.78 MT (million tons) per year. The model results showed that the terrestrial NPP was sensitive to changes in temperature and precipitation. Addressing the model results, the CASA provided a great potential to predict present and future productivity on regional basis. Thus, this study will provide a scientific foundation to understand and assess ecological and economic implications and consequences of climate change on the productivity in Turkey

Percent tree cover mapping from Envisat MERIS and MODIS data

© 2008 International Society for Photogrammetry and Remote Sensing. All rights reserved.The aim of this study was to compare percent tree cover products of Envisat MERIS and MODIS data of Seyhan River Basin at the Eastern Mediterranean Region of Turkey. In this study, Regression Tree (RT) algorithm was used to estimate percent tree cover maps. This technique is well suited for percentage tree cover mapping because, as a non-parametric classifier, it requires no prior assumptions about the distribution of the training data. This model also allows for the calibration of the model along the entire continuum of tree cover, avoiding the problems of using only end members for calibration.The medium resolution Envisat MERIS with a 300 m and MODIS with a 500 m pixel representation data were used as predictor variables. Three scenes of high resolution IKONOS images were employed as a training data, and testing the accuracy of model. The regression tree method for this study consisted of six steps: i) generate reference percentage tree cover data, ii) derive metrics from Envisat MERIS and MODIS data, iii) select predictor variables, iv) fit RT model, v) undertake accuracy assessment and produce final model and map, vi) compare results. The training data set was derived supervised land cover classification of IKONOS imagery to generate reference percent tree cover data. Specifically, this classification was aggregated to estimate percent tree cover at the MERIS and MODIS spatial resolution.The predictor variables incorporated the MERIS and MODIS wavebands in addition to biophysical variables estimated from the MERIS and MODIS data. Percent tree cover maps were derived from MERIS and MODIS data for Seyhan upper Basin as final outputs. These final outputs consisted of spatially distributed estimates of percent tree cover at 300 m and 500 m spatial resolution and error estimates obtained through validation. This study showed that Envisat MERIS data can be used to predict percentage tree cover with greater spatial detail than using MODIS data. This finer-scale depiction should be of great utility for environmental monitoring purposes at the regional scale

Effectiveness of boosting algorithms in forest fire classification

In this paper, it is aimed to investigate the capabilities of boosting classification approach for forest fire detection using SPOT-4 imagery. The study area, Bodrum in the province of Muǧla, is located at the south-western Mediterranean coast of Turkey where recent largest forest fires occurred in July 2007. Boosting method is one of the recent advanced classifiers proposed in the machine learning community, such as neural networks classifiers based on multilayer perceptron (MLP), radial basis function and learning vector quantization. The Adaboost (AB) and Logitboost (LB) algorithms which are the most common boosting methods were used for binary and multiclass classifications. The effectiveness of boosting algorithms was shown through comparison with Bayesian maximum likelihood (ML) classifier, neural network classifier based on multilayer perceptron (MLP) and regression tree (RT) classifiers. The pre and post SPOT images were corrected atmospherically and geometrically. Binary classification comprised burnt and non-burnt classes. In addition to the pixel based classification, textural measures including, gray level co-occurrence matrix such as entropy, homogeneity, second angular moment, etc. were also incorporated. Instead of the traditional boosting weak (base) classifiers such as decision tree builder or perceptron learning rule, neural network classifier based on multilayer perceptron were adapted as a weak classifier. The accuracy of the MLP was greater than that of ML, AB, LB and RT both using spectral data alone and textural data. The use of texture measures alone was found to increase classification accuracy of binary and multi-class classifications. The accuracy of the land cover classifications based on either binary or multi-class was maximised using a MLP approach. This was slightly greater than the accuracy achieved using AB and LB classifications. However, it was shown that AB and LB classifications hold great potential as an alternative to conventional techniques

Geospatial technologies for physical planning: Bridging the gap between earth science and planning

The application of geospatial information technologies has increased recently due to increase in data sources from the earth sciences. The systematic data collection, storage and processing together with data transformation require geospatial information technologies. Rapidly developing computer technology has become an effective tool in design and physical planning in international platforms. Especially, the availability of geospatial information technologies (remote sensing, GIS, spatial models and GPS) for diverse disciplines and the capability of these technologies in data conversion from two dimensions to the three dimensions provide great efficiency. Thus, this study explores how digital technologies are reshaping physical planning and design. While the potential of digital technologies is well documented within physical planning and visualization, its application within practice is far less understood. This paper highlights the role of the geospatial information technologies in encouraging a new planning and design logic that moves from the privileging of the visual to a focus on processes of formation, bridging the interface of the earth science and physical planning