Grassland Recognition with the Usage of Thermal Weights

In this paper we apply the usage of thermal weights, a new variable for geostatistical analysis and we present the method for their determination. In the case study we tested a data fusion between Sentinel-2 and Landsat 7/8 data, to incorporate also the thermal factor in the detection of land cover changes. The process distinguishes grasslands from other crops with similar vegetative appearance and offers us the possibility to create a new statistical sample with just grasslands. The data fusion is incorporated in the calculation of Land Surface Temperature (LSTFU) by combining the Sentinel-2 derived Normalized Difference Vegetation Index (NDVI), and from it derived land surface emissivity, with the Landsat 7/8 derived Top of Atmosphere Brightness Temperature (TOABT). The experimental LSTFU is modified into a normalized assessment variable by a time-series analysis. The result is a thermal weight layer which can help us in further object-based image analyses and classification. The thermal weight is calculated from Sentinel-2 and Landsat 7/8 datasets that has small acquisition time gaps between them. The accuracy assessment due to time gaps and sensor differences was evaluated with Cohens’s kappa (κ) and correlation matrix validation. The data fusion is made to test if a Sentinel-2 fusion approach could improve the Thermal Weight created just by Landsat imagery. The purpose was to evaluate the importance of thermal bands for LU/LC cover

Thermally enhanced spectral indices to discriminate burn severity in Mediterranean forest ecosystems

P. 1-8Fires are a problematic and recurrent issue in Mediterranean forest ecosystems. Accurate discrimination of burn severity level is fundamental for the rehabilitation planning of affected areas. Though fieldwork is still necessary for measuring post-fire burn severity, remote sensing based techniques are being widely used to predict it because of their computational simplicity and straightforward application. Among them, spectral indices classification (especially difference Normalized Burn Ratio–dNBR- based ones) may be considered the standard remote sensing based method to distinguish burn severity level. In this work we show how this methodology may be improved by using land surface temperature (LST) to enhance the standard spectral indices. We considered a large wildfire in August 2012 in North Western Spain. The Composite Burn Index (CBI) was measured in 111 field plots and grouped into three burn severity levels. Relationship between Landsat 7 Enhanced Thematic Mapper (ETM+) LST-enhanced spectral indices and CBI was evaluated by using the normalized distance between two burn severity levels and spectral dispersion graphs. Inclusion of LST in the spectral index equation resulted in higher discrimination between burn severity levels than standard spectral indices (0.90, 8.50, and 17.52 NIR-SWIR Temperature version 1 vs 0.60, 2.83, and 6.46 NBR). Our results demonstrate the potential of LST for improving burn severity discrimination and mapping. Future research, however, is needed to evaluate the performance of the proposed LST-enhanced spectral indices in other fire regimes, and forest ecosystems.S

Penggunaan Citra Landsat untuk Pendeteksian Anomali Suhu Permukaan Sebagai Indikasi Keberadaan Manifestasi Panas Bumi. Studi Kasus: Sipoholon, Indonesia

Sipoholon merupakan sebuah kecamatan yang terletak di Kabupaten Tapanuli Utara, Sumatera Utara yang dilewati oleh Sistem Sesar Sumatera. Berdasarkan peta Geologi Lembar Sidikalang, ditemukan adanya manifestasi panas bumi berupa mata air panas (hot spring) di daerah tersebut. Keberadaan manifestasi ini menunjukkan adanya sistem panas bumi yang bekerja di bawahnya. Keberadaan manifestasi panas bumi di suatu daerah akan berpengaruh terhadap suhu permukaan tanah di daerah tersebut. Pendeteksian anomali suhu permukaan tanah dapat dilakukan dengan berbagai cara. Salah satu metode yang mudah untuk dilakukan yaitu menggunakan Citra Penginderaan Jauh. Citra Landsat merupakan salah satu citra penginderaan jauh yang dapat digunakan untuk mendeteksi anomali suhu permukaan. Penelitian ini bertujuan untuk mendeteksi anomali suhu permukaan sebagai indikasi adanya manifestasi panas bumi di Sipoholon, Sumatera Utara menggunakan Citra Landsat. Band 10 dan band 11 yang merupakan thermal infrared (TIR) diolah untuk mendapatkan suhu kecerahan (brightness temperature). Band 4 dan 5 digunakan untuk menghitung kerapatan vegetasi (NDVI) dan emisivitas suhu permukaan. Gabungan dari band 10, band 11 dan NDVI digunakan untuk menghitung nilai suhu permukaan tanah (Land Surface Temperature). Data lapangan berupa suhu mata air panas dan suhu permukaan tanah  diambil untuk melakukan verifikasi terhadap analisis citra yang telah dilakukan. Hasil analisis citra menunjukkan bahwa suhu permukaan di lokasi penelitian berkisar antara 16,7°C sampai dengan 28,4°C. Anomali suhu tinggi berada di daerah yang dilewati oleh sesar sumatera. Hasil verifikasi di lapangan menunjukkan hasil yang selaras, terdapat mata air panas di daerah yang dilewati oleh sesar sumatera dengan suhu 35,7 °C – 64,4°C. Sedangkan suhu permukaan tanah berkisar antara 31,3°C – 48,7°C. Hal ini menunjukkan bahwa citra landsat berupa suhu permukaan tanah (LST) dapat digunakan untuk mendeteksi anomali suhu permukaan sebagai indikasi adanya sistem panas bumi di suatu daerah untuk memudahkan pengerucutan lokasi pada tahap eksplorasi

Análise do comportamento de temperatura de superfície na malha urbana de Araxá/MG, utilizando-se banda termal do satélite Landsat 8Analysis of surface temperature behavior in the urban area of Araxá/MG, using satellite thermal band Landsat 8

O sensoriamento remoto se caracteriza como uma ferramenta metodológica fundamental quando utilizada em análises climáticas de temperatura, dado que a sua funcionalidade e praticidade possibilita diversas perspectivas de análise e resultados. O presente estudo tem como objetivo principal analisar o comportamento de temperatura de superfície na malha urbana do município de Araxá/MG, utilizando-se banda termal do satélite landsat 8. O município de Araxá localiza-se na Macrorregião do Alto Paranaíba. Seu clima é caracterizado por verões úmidos e invernos secos, podendo ser classificado como Aw e Cwa. Os resultados foram obtidos a partir do processamento de imagens de satélites das cenas geradas no sensor 10 (infravermelho termal) do satélite landsat 8 no software ArcGis 10.1. Neste software foram realizados todos os processamentos de correção radiométrica e conversão de valores de radiância para temperatura. Os resultados exibem que, no mês de junho de 2014, as maiores temperaturas concentraram-se na área central e no setor industrial da cidade, variando entre 17,6°C a 24,5°C, e as menores nos bairros residenciais mais afastados, co variação de 15,6°C a 17,6°C. Já no mês de setembro de 2014, as maiores temperaturas variaram entre 33,5°C a 39,5°C, e as menores, entre 27,5°C a 34,5°C. Enfim, a realização deste estudo promoveu uma percepção da oscilação das temperaturas em distintos pontos da cidade, assim como de uma estação para a outra.Abstract Remote sensing is characterized as a basic methodological tool when used in climatic temperature analysis, since its functionality and practicality enables various perspectives of analysis and results. This study is meant to examine the surface temperature behavior of the urban area in the city of Araxá / MG, using thermal band satellite Landsat 8. The city of Araxá is located in the Macro-region of Alto Paranaíba. Its climate is characterized by wet summers and dry winters and can be classified as Aw and Cwa. The results were obtained from the processing of satellite images, with scenes generated in the sensor 10 (thermal infrared) satellite Landsat 8 in ArcGIS 10.1 software. This software conducted all processing of radiometric correction and conversion of radiance values for temperature. Results show that in June 2014 the highest temperatures concentrated in the central area and in the industrial sector of the city, ranging from 17.6 ° C to 24.5 ° C and lower in residential areas further away, where temperatures ranged from 15.6 ° C to 17.6 ° C. Yet in September 2014, higher temperatures ranged from 33.5 ° C to 39.5 ° C and 27.5 ° C lower from the 34.5 ° C. Finally, this study promoted a sense of fluctuation of temperatures in different parts of the city, as well as from one season to the next. Keywords: temperature analysis, sensing, Araxá

Analisis Perubahan Distribusi Urban Heat Island (UHI) di Kota Surabaya Menggunakan Citra Satelit Landsat Multitemporal

Surabaya merupakan kota terbesar kedua di Indonesia setelah Jakarta, dimana jumlah penduduk kota Surabaya setiap tahunnya selalu mengalami kenaikan. Peningkatan populasi penduduk di wilayah perkotaan dapat mengubah pola ruang kawasan perkotaan. Pengunaan lahan akan bergeser dari keperluan pertanian menjadi keperluan tempat tinggal, kawasan bisini/industri dan aktivitas lainnya. Perubahan tutupan lahan ini akan berdampak pada kondisi iklim dan cuaca di kawasan perkotaan sehingga menyebabkan terjadinya fenomena Urban Heat Island (UHI). Pemantauan fenomena ini diperlukan di perkotaan yang mengalami perkembangan pesat, termasuk di Kota Surabaya. Pada penelitian ini akan dianalisis perubahan distribusi Urban Heat Island (UHI) di Kota Surabaya pada tahun 2002, 2014, dan 2019 menggunakan Citra Landsat 7 (ETM+) dan Citra Landsat 8 (OLI/TIRS). Langkah awal yang dilakukan berupa perhitungan Land Surface Temperature (LST) dengan metode Single-Channel Algorithm, kemudian dilakukan analisis distribusi UHI dengan metode Ambang Batas dan Hot Spot Analysis (Getis-Ord Gi*). Hasilnya didapatkan nilai suhu permukaan rata-rata Kota Surabaya untuk tahun 2002, 2014, dan 2019 secara berurutan sebesar 29,094°C; 26,889°C; dan 27,130°C. Uji korelasi Pearson Product Moment dilakukan antara LST dengan suhu lapangan, diperoleh hasi koefisien korelasi (Rxy) sebesar 0,449. Terakhir, dari peta distribusi UHI metode Ambang Batas, luas area yang terdampak UHI selalu mengalami penurunan dari tahun 2002 ke 2014, dan 2014 ke 2019 dengan luas penurunan masing-masing sebesar 0,760 km2 dan 7,995 km2. Hal yang sama juga terjadi untuk metode Hot Spot Analysis, dengan luas penurunan pada tahun 2002 ke 2014, dan 2014 ke 2019 sebesar 2,027 km2 dan 31,168 km2

Urban Land Use Land Cover Changes and Their Effect on Urban Thermal Pattern: Case Study of Nigeria’s Federal Capital City

Since the official movement of the seat of government from Lagos to Abuja in 1991, the Federal Capital City being the most habitable area in the entire Federal Capital Territory has continued to experience rising urban population growth; this has resulted in a significant modification of its natural landscape. Such modifications often affect the microclimate of cities. This study investigates the effect of changing urban land use/cover on urban thermal pattern through the application of remote sensing, geographic information systems (GIS) and statistical methods. Land use land cover (LULC) and land surface temperature (LST) were extracted from Landsat 4 TM (1987) and Landsat 8 OLI/TIRS (2014). The result of LULC show that while built-up area and wasteland classes increased by 19.93% (from 23.57% in 1987 to 43.50% in 2014) and 15.87% (from 14.88% in 1987 to 30.75% in 2014) respectively, vegetation cover decreased by 35.63% (from 60.63% in 1987 to 25% in 2014). This significant vegetation cover loss to both built-up area and wasteland is an indication of man’s dominance of the landscape of the city.  The study revealed that the lowest values for minimum, maximum and mean temperature occurred in 1987 (17.03, 31.16 and 24.3°C respectively) while the highest values for minimum, maximum and mean temperature occurred in 2014 (25.01, 37.38 and 32.54°C respectively). In 1987, relatively lower LST values (23-26°C) were dominant occupying about 84.38% of the total land area while, in 2014; higher LST values (31-34°C) were dominant occupying approximately 84.14% of the total land area. These results show that a significant portion of the natural landscape elements of the FCC has been removed due to rapid urbanization and this has resulted in the formation of hotspots across the city. The results of this research bring to fore the need for urban planners in the FCC to put in place temperature-mitigation strategies so as to ensure the sustainability of the city. Keywords: LULC; LST; classification; FCC; SUHI

MODIFIKASI MODEL FOREST CANOPY DENSITY (FCD) PADA CITRA LANDSAT 8 MULTI-TEMPORAL UNTUK MONITORING PERUBAHAN TUTUPAN VEGETASI DI KECAMATAN SUKASADA-BALI

Modifikasi model Forest Canopy Density (FCD) bertujuan untuk mengetahui seberapa besar peningkatan akurasi pada model FCD. Perubahan modifikasi model FCD dilakukan secara temporal pada tahun 2014 sampai tahun 2019 di Kecamatan Sukasada. Perubahan dilakukan pada indeks vegetasi dengan Soil Adjusted Vegetation Index (SAVI) dan indeks thermal dengan metode Split-Windows Algorithm (SWA). Modifikasi tersebut perlu dilakukan karena modifikasi model FCD sebelumnya berupa pengurangan penggunaan indikator. Berdasarkan hasil modifikasi model FCD yang dilakukan membuktikan model FCD SAVI memiliki akurasi sebesar 83.67% dan model FCD original sebesar 84%. Sedangkan penggunaan SWA pada model FCD memiliki kondisi konsisten sehingga dapat dinyatakan bahwa SWA mampu menyesuaikan terhadap modifikasi model FCD. Secara temporal (2014 – 2019) menunjukkan perubahan tutupan vegetasi tinggi menjadi tutupan vegetasi sedang sebesar 1115.28 Hektare. Disimpulkan bahwa model FCD SAVI memiliki perbedaan sebesar 0,33% dibandingkan model FCD aslinya. Hal ini dipengaruhi oleh kondisi variasi topografi wilayah yang menyebabkan efek bayangan