Muhammad Kamal

The Effect of Field Spectral Reflectance Measurement Distance to the Spectral Reflectance of Rhizophora stylosa

Mapping mangrove species from remote sensing data through its spectral reflectance pattern collected in the field is challenging. There are high variations in light condition, leaf orientation, canopy structure, background objects and measurement distance when measuring mangrove spectral reflectance in the field. Spectral measurement distance to the object is one of the most important aspects controlling the result of spectral reflectance pattern. This research is aimed to assess the effect of spectral reflectance pattern of Rhizophora stylosa collected at various distances. Specific objectives of this research are to collect samples of mangrove spectral reflectance pattern in the field, to assess the effect of the observation scale to the result of the spectral reflectance pattern, and to characterize the mangrove spectral reflectance pattern resulted from different observation scales. Spectral reflectance data collection in the field was conducted using JAZ EL-350 field spectrometer at 2cm, 50cm, 1m, 2m, and 5m distance and was conducted in Karimunjawa Island, Jepara, Central Java, Indonesia. A visual comparison of the spectral reflectance curve was conducted to understand the effect of measurement distance. The results of this study indicate that the difference in the measurement distance of Rhizophora stylosa species was highly influential to the resulting spectral reflectance curve. The spectral reflectance curve recorded at close range to the leaf (i.e. 2 cm) has the lowest curve variation, as well as the furthest distance (i.e. 5 m). This study is a basic study that supports the development of the use of remote sensing imagery for mangrove species mapping.

Multispectral Resampling of Seagrass Species Spectra: WorldView-2, Quickbird, Sentinel-2A, ASTER VNIR, and Landsat 8 OLI

Although spectrally different, seagrass species may not be able to be mapped from multispectral remote sensing images due to the limitation of their spectral resolution. Therefore, it is important to quantitatively assess the possibility of mapping seagrass species using multispectral images by resampling seagrass species spectra to multispectral bands. Seagrass species spectra were measured on harvested seagrass leaves. Spectral resolution of multispectral images used in this research was adopted from WorldView-2, Quickbird, Sentinel-2A, ASTER VNIR, and Landsat 8 OLI. These images are widely available and can be a good representative and baseline for previous or future remote sensing images. Seagrass species considered in this research are Enhalus acoroides (Ea), Thalassodendron ciliatum (Tc), Thalassia hemprichii (Th), Cymodocea rotundata (Cr), Cymodocea serrulata (Cs), Halodule uninervis (Hu), Halodule pinifolia (Hp), Syringodum isoetifolium (Si), Halophila ovalis (Ho), and Halophila minor (Hm). Multispectral resampling analysis indicate that the resampled spectra exhibit similar shape and pattern with the original spectra but less precise, and they lose the unique absorption feature of seagrass species. Relying on spectral bands alone, multispectral image is not effective in mapping these seagrass species individually, which is shown by the poor and inconsistent result of Spectral Angle Mapper (SAM) classification technique in classifying seagrass species using seagrass species spectra as pure endmember. Only Sentinel-2A produced acceptable classification result using SAM.

Machine-learning regression for coral reef percentage cover mapping

Coral reef live percent cover (LPC) mapping has always been a challenging application for remote-sensing. The adoption of machine-learning algorithm in remote-sensing has opened-up the possibility of mapping coral reef at higher accuracy. This paper presents the application of machine-learning regression in the empirical modeling of coral reef LPC mapping. Stepwise regression, Support Vector Machine (SVM) regression, and Random Forest (RF) regression were used model the percentage of live coral cover in optically shallow water of Parang Island, Central Java, Indonesia using field photo-transect data to train the PlanetScope image. PlanetScope multispectral bands were transformed into water column corrected bands, Principle Component bands, and Cooccurrence texture analysis bands to be used as predictors in the regression process. The results indicate that the accuracy of machine learning algorithm to map coral reef LPC is relatively low due to the radiometric quality issue in the PlanetScope image (RMSE = 15.43%). We could not yet fairly justify the performance of machine learning algorithm until we applied the algorithms in other images.

PENGARUH RESOLUSI SPASIAL CITRA TERHADAP HASIL PEMETAAN KANDUNGAN HARA NITROGEN PERKEBUNAN KARET

Nitrogen merupakan salah satu unsur hara yang dibutuhkan dalam jumlah banyak oleh tanaman. Tanaman yang mengalami kekurangan unsur hara nitrogen akan menyebabkan terhambatnya pertumbuhan dan penurunan produktivitas tanaman. Penerapan sistem pertanian presisi pada kegiatan pemupukan di perkebunan karet dilakukan dengan cara dosis pemupukan dibuat berdasarkan kandungan hara tanah dan kandungan hara pada tanaman. Pada areal yang luas membutuhkan biaya analisa hara tanaman yang cukup mahal. Oleh karena itu sangat dibutuhkan suatu teknologi yang dapat mengestimasi kondisi hara tanaman dengan cepat dan biaya yang murah. Teknologi penginderaan jauh merupakan alternatif yang dapat digunakan untuk areal yang luas dan dengan waktu yang cepat serta biaya yang relatif murah. Penelitian ini bertujuan untuk mengetahui pengaruh resolusi spasial citra terhadap peta hasil estimasi kandungan nitrogen perkebunan karet. Citra multi resolusi yang digunakan antara lain GeoEye-1 (2 m) Sentinel-2A (10 dan 20 m) dan Landsat 8 OLI (30 m). Metode yang digunakan adalah membangun hubungan semi-empiris antara band tunggal dan indeks vegetasi citra dengan kandungan hara nitrogen perkebunan karet. Hasil penelitian menunjukkan bahwa peta hasil estimasi kandungan hara nitrogen perkebunan karet menggunakan citra Sentinel-2A (SE 0,369) memiliki akurasi yang lebih tinggi dibandingkan dengan menggunakan citra GeoEye-1 (SE 0,519) dan Landsat 8 OLI (SE 0,462).

Evaluation of atmospheric correction models and Landsat surface reflectance product in Daerah Istimewa Yogyakarta, Indonesia

The atmospheric disturbance in remote sensing imagery greatly influences the objects spectral response in the imagery. This, in turn, will impact the object characterization. The atmospheric effects on remote sensing imagery can be reduced through atmospheric correction. There are various types of atmospheric correction methods and each of them has its own working principles. Daerah Istimewa Yogyakarta (DIY) Province, Indonesia, was chosen to be study area for this research. The research objectives are to evaluate the atmospheric correction method, which consist of Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Quick Atmospheric Correction (QUAC), Dark Object Subtraction (DOS), Second Simulation of the Satellite Signal in the Solar Spectrum (6S), Atmospheric Correction (ATCOR2), and Landsat 8 Surface Reflectance Code (LaSRC) by NASA. The compared objects consist of water, vegetation, and soil objects. The evaluation was based on Standard Error of Estimate (SEE), accuracy, and curve pattern. The result shows that the best atmospheric correction varies on each object. The spectral response curve pattern shows similarity but each object has its own accurate atmospheric method based on SEE result. The FLAASH, 6s, and ATCOR2 method show the lowest SEE result for mature vegetation leaves, beach sand, sand suns, and, lagoon, while QUAC method shows the lowest SEE result for young vegetation leaves, paddy plants, grass, and reservoir.

Combining field and image spectral reflectance for mangrove species identification and mapping using WorldView-2 image

Mangrove species inventory and mapping is very important as an effort to preserve the ecosystem and biodiversity of mangrove forests. One way of efficient mangrove species inventory and mapping is to use remote sensing imagery, especially through the analysis of its spectral reflectance pattern. This study aims to map the fourteen mangrove species on Karimunjawa Island, Central Java, Indonesia by: (1) measuring the mangrove species spectral reflectance pattern in the field, (2) characteristic analysis of the mangrove species reflectance pattern, and (3) mapping the dominant mangrove species distribution. The spectral reflectance measurement of mangrove species objects in the field was done by using JAZ EL-350 VIS-NIR (ranges from 300 to 1100 nm). The JAZ field spectrometer was pointed at a distance of 2 cm from the target objects with 10 reading repetitions for each species. Field measurements results were then taken to the laboratory for analysis of spectral reflectance and absorbance patterns, which served as key object recognition in this study. To combine the field and image spectral reflectance patterns, the field reflectance patterns were resampled to the spectral resolution of WorldView-2 image (8 bands, 2 m pixel size). The spectral angle mapper (SAM) method was the used to locate and map the distribution of each targeted mangrove species. As expected, the results showed that the largest difference of spectral curves between species was at the NIR wavelength spectrum (700-900nm). Hence, it is potential to be used as the basis for identification of species mangrove from remote sensing imagery. However, the result of this mapping approach only showed a low accuracy of 62%. The low value of map accuracy was attributed to the inaccuracy in defining threshold in SAM for each class. This study provides a basic understanding of the use of spectral reflectance for mangrove species mapping from remote sensing imagery.

Spectral response of healthy and damaged leaves of tropical seagrass Enhalus acoroides, Thalassia hemprichii, and Cymodocea rotundata

Characterization of seagrass spectral reflectance response is important to understand seagrass condition and for the possibility of mapping activities using remote sensing data, which is important for the management, monitoring, and evaluation of seagrass ecosystem. This paper presents the spectral reflectance response of several tropical seagrass species. These species are Enhalus acoroides (Ea), Thalassia hemprichii (Th) and Cymodocea rotundata (Cr). Spectral reflectance response of healthy seagrass, epiphyte-covered seagrass, and damaged seagrass leaves for each species were measured using Jaz EL-350 field spectrometer ranged from 350 - 1100 nm. Repeated measurements were performed above water on harvested seagrass leaves. The results indicate that there is a change in spectral reflectance response of damaged or epiphyte-covered seagrass leaves compared to the healthy leaves. The results show similar pattern for the three species, where the peak reflectance in visible wavelengths shifted toward longer wavelengths on damaged seagrass leaves. The results of this research open up a possibility of mapping seagrass health condition using remote sensing image.

EKSTRAKSI INFORMASI PENUTUP LAHAN MENGGUNAKAN SPEKTROMETER LAPANGAN SEBAGAI MASUKAN ENDMEMBER PADA DATA HIPERSPEKTRAL RESOLUSI SEDANG

Land use function is basic information in spatial planning process. Land use function describes the area division based on its capability. Usually, land use function can be divided into three categories which are protected area, buffer area, and cultivated area. Recently, land use function in spatial plan document is generated by applying scoring method. However, land use function can be also obtained from land capability assessment published by USDA (United States Department of Agriculture). Land use function in this research is determined by using scoring method (regarding to legal document of Ministry of Agriculture number 837/Kpts/UM/11/1980 and number 683/Kpts/UM/8/1981) and proposed method (developed by modifying USDA land capability assessment). Land capability itself is assessed by using landform approach. Landform is obtained through interpretation of satellite image, topographic map, and field survey. Based on scoring method, the obtained range score is 90-195. The study area can be classified into protected zone (51%), buffer zone (31%), and cultivated zone (18%).On the other hand, proposed method gives some results that study area consists of five land capability classes, i.e. IV, V, VI, VII, and VIII. The percentage for each class is 26%, 2%, 2%, 12%, and 58% respectively. Related to land use function, this result represents that 58% of total area is allocated as protected zone, 16% of total area is classified as buffer zone, and the rest area is provided as cultivated zone. Key Words: Scoring method, USDA land capability classification, land use function ABSTRAK Fungsi kawasan merupakan informasi dasar yang diperlukan proses dalam penyusunan rencana tata ruang. Fungsi kawasan menggambarkan pembagian area berdasarkan kemampuan yang dimilikinya. Pada umumnya, fungsi kawasan dibedakan menjadi tiga kategori, yaitu kawasan lindung, kawasan penyangga, dan kawasan budidaya/penanaman. Saat ini, fungsi kawasan dalam dokumen rencana tata ruang ditentukan dengan menggunakan metode skor. Meskipun demikian, fungsi kawasan dapat juga ditentukan dengan memanfaatkan perkiraan kemampuan lahan yang diterbitkan oleh USDA (United States Department of Agriculture). Fungsi kawasan pada penelitian ini ditentukan berdasarkan metode skor yang bersumber dari SK Menteri Pertanian No. 837/Kpts/UM/11/1980 dan No. 683/Kpts/UM/8/1981, sedangkan metode yang diusulkan dikembangkan dengan memodifikasi penilaian kemampuan lahan yang diterbitkan oleh USDA. Kemampuan lahan tersebut dinilai dengan menggunakan pendekatan bentanglahan. Bentang lahan diperoleh melalui interpretasi foto satelit, peta topografi dan survei lapangan. Berdasarkan metode skor, range skor yang didapatkan adalah 90 – 195. Wilayah studi dapat diklasifikasikan menjadi kawasan lindung (51%), kawasan penyangga (31%) dan kawasan budidaya (18%). Di sisi lain, metode yang diusulkan menghasilkan lima kelas kemampuan lahan yaitu kelas IV, V, VI, VII, dan VIII. Prosentase setiap kelas secara berurutan adalah 26%, 2%, 2%, 12%, and 58%. Berkaitan dengan fungsi penggunaan lahan, hasil ini menunjukkan bahwa 58% dari seluruh wilayah studi dialokasikan sebagai kawasan lindung, 16% dari total wilayah studi diklasifikasikan sebagai kawasan penyangga, sedangkan sisanya sebagai kawasan budidaya. Kata Kunci : Metode skor, klasifikasi kemampuan lahan USDA, fungsi kawasan