Pemetaan Lamun Mengunakan Machine Learning Dengan Citra Planetscope Di Nusa Lembongan

Seagrass is one community in benthic habitat that has tremendous benefits for the ecosystem, however the existence of seagrass has been frequently marginalized in recent decades. Seagrass beds functions as a blue carbon ecosystem which are able to absorb carbon higher than terrestrial vegetation. Therefore, it is important to detect and map the seagrass beds distribution to calculate the potential carbon uptake from seagrass. The seagrass mapping can be employed efficiently by using remote sensing imagery and the use of machine learning technology. This research aims to examine the utilization of PlanetScope imagery (3.7 m spatial resolution) for seagrass mapping and to subsequently examine, the effect of atmospheric corrections, sun-glint, and the water column corrections on the accuracy of seagrass mapping. In addition, this study also identified the cover changes in seagrass area from 2016 to 2021 in Nusa Lembongan. The study utilized the tree-based machine learning methods such as decision tree and random forest. The results showed that the best model accuracy was generated by using raw PlanetScope data the best model accuracy of 98% and classification accuracy of 94% from decision tree method. Based on the decision tree mapping using PlanetScope data for 2016 and 2021, there was a decline in the seagrass cover from 100.53 hectares to 97.31 hectares. Lamun merupakan salah satu dari ekosistem habitat bentik yang memiliki manfaat yang sangat besar namun sebagai ekosistem, kehadiran lamun sering dikesampingkan beberapa dekade terakhir. Fungsi padang lamun sebagai ekosistem karbon biru mampu menyerap karbon lebih tinggi dibandingkan vegetasi daratan. Karena itu, penting untuk mendeteksi dan memetakan informasi padang lamun untuk memperhitungkan serapan karbon oleh lamun. Pemanfaatan lamun dapat dilakukan secara cepat dan efisien dengan mengunakan  teknologi penginderaan jauh dan pemenfaatan teknologi machine learning. Penelitian bertujuan untuk mengkaji pemanfaatan citra PlanetScope untuk memetakan lamun dan selanjutnya menganalisis pengaruh kalibrasi atmosferik, sun-glint, dan kolom air terhadap akurasi pemetaan padang lamun. Selain itu, perubahan tutupan lamun tahun 2016 – 2021 di Nusa Lembongan juga dipetakan. Penelitian ini menggunakan metode machine learning berbasis pohon seperti decision tree dan random forest. Hasil penelitian menunjukkan akurasi model terbaik dihasilkan dengan menggunakan data mentah dengan akurasi model 98% dan akurasi klasifikasi 94% dari metode decision tree. Berdasarkan data PlanetScope tahun 2016 dan 2021 dengan mengunakan metode decision tree terjadi penurunan luasan lamun dari 100,53 Ha menjadi 97,31 Ha

Accuracy and Spatial Pattern Assessment of Forest Cover Change Datasets in Central Kalimantan

The accurate information of forest cover change is important to measure the amount of carbon release and sink. The newly-available remote sensing based products and method such as Daichi Forest/Non-Forest (FNF), Global Forest Change (GFC) datasets and Semi-automatic Claslite systems offers the benefit to derive these information in a quick and simple manner. We measured the accuracy by constructing area-proportion error matrix from 388 random sample points and assessed the consistency analysis by looking at the spatial pattern of deforestation and regrowth from built-up area, roads, and rivers from 2010 – 2015 in Katingan district, Central Kalimantan. Accuracy assessment showed that those 3 datasets indicate low to medium accuracy level in which the highest accuracy was achieved by Claslite who produced 71 % ± 5 % of overall accuracy. The consistency analysis provides a similar spatial pattern of deforestation and regrowth measured from the road, river, and built-up area though their distance sensitivity are different one to another.

Identifikasi Tumpahan Minyak di Laut Akibat Tank Cleaning Menggunakan Metode Tidak Terselia

Tumpahan minyak di laut dapat terdeteksi oleh citra satelit dengan sensor Synthetic Aperture Eadar (SAR) dan memungkinkan untuk diidentifikasi menggunakan berbagai macam metode baik terselia maupun tidak terselia. Salah satu metode terselia yang biasa digunakan adalah digitasi visual, namun metode ini sangat subjektif pada kapasitas interpreter. Untuk meminimalisasi subjektifitas interpreter maka metode tidak terselia perlu dikaji lebih lanjut. Tujuan dari penelitian ini adalah mengkaji algoritma tidak terselia untuk identifikasi tumpahan minyak yang diakibatkan oleh tank cleaning. Citra satelit yang digunakan dalam penelitian ini adalah citra Sentinel-1 di wilayah perairan utara Pulau Bintan. Proses identifikasi dilakukan menggunakan metode tidak terselia, dan penelitian ini membandingkan dua algoritma dalam proses identifikasi, yaitu K-Means dan CLARA. Dapat disimpulkan bahwa dalam melakukan identifikasi perlu diketahui terlebih dahulu kondisi perairan terutama kecepatan angin dan arus laut sebelum memasuki tahap komputasi. Hasil identifikasi menggunakan kedua algoritma ini dibandingkan dengan data referensi dari LAPAN sebagai instansi yang melakukan diseminasi terkait tumpahan minyak di laut. Jika dibandingkan dengan data referensi tersebut, algoritma K-Means memiliki persentase hasil yang lebih baik dalam mendeteksi luasan tumpahan minyak, namun algoritma CLARA mampu memberikan hasil identifikasi dengan look-alike tumpahan minyak yang lebih sedikit sehingga kesalahan identifikasi menjadi minimal

Classification of Mangrove Vegetation Structure using Airborne LiDAR in Ratai Bay, Lampung Province, Indonesia

Mapping and inventory of the distribution and composition of mangrove vegetation structures are crucial in managing mangrove ecosystems. The availability of airborne LiDAR remote sensing technology provides capability of mapping vegetation structures in three dimensions. It provides an alternative data source for mapping and inventory of the distribution of mangrove ecosystems. This study aims to test the ability of airborne LiDAR data to classify mangrove vegetation structures conducted in Ratai Bay, Pesawaran District, Lampung Province. The classification system applied integrates structure attributes of lifeforms, canopy height, and canopy cover percentage. Airborne LiDAR data are derived into canopy height models (CHM) and canopy cover percentage models, then grouped by examining statistics and the zonation distribution of mangroves in the study area. The results of this study show that airborne LiDAR data are able to map vegetation structures accurately. The canopy height model derived using a pit-free algorithm can represent the maximum tree height with an error range of 3.17 meters and 82.3-88.6% accuracy. On the other hand, the canopy cover percentage model using LiDAR Fraction Cover (LFC) tends to be overestimate, with an error range of 16.6% and an accuracy of 79.6-94.7%. Meanwhile, the classification results of vegetation structures show an overall accuracy of 77%

Rapid conversions and avoided deforestation: examining four decades of industrial plantation expansion in Borneo

New plantations can either cause deforestation by replacing natural forests or avoid this by using previously cleared areas. The extent of these two situations is contested in tropical biodiversity hotspots where objective data are limited. Here, we explore delays between deforestation and the establishment of industrial tree plantations on Borneo using satellite imagery. Between 1973 and 2015 an estimated 18.7 Mha of Borneo’s old-growth forest were cleared (14.4 Mha and 4.2 Mha in Indonesian and Malaysian Borneo). Industrial plantations expanded by 9.1 Mha (7.8 Mha oil-palm; 1.3 Mha pulpwood). Approximately 7.0 Mha of the total plantation area in 2015 (9.2 Mha) were old-growth forest in 1973, of which 4.5–4.8 Mha (24–26% of Borneo-wide deforestation) were planted within five years of forest clearance (3.7–3.9 Mha oil-palm; 0.8–0.9 Mha pulpwood). This rapid within-five-year conversion has been greater in Malaysia than in Indonesia (57–60% versus 15–16%). In Indonesia, a higher proportion of oil-palm plantations was developed on already cleared degraded lands (a legacy of recurrent forest fires). However, rapid conversion of Indonesian forests to industrial plantations has increased steeply since 2005. We conclude that plantation industries have been the principle driver of deforestation in Malaysian Borneo over the last four decades. In contrast, their role in deforestation in Indonesian Borneo was less marked, but has been growing recently. We note caveats in interpreting these results and highlight the need for greater accountability in plantation development

Evaluating variable selection and machine learning algorithms for estimating forest heights by combining lidar and hyperspectral data

Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection; it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data

Perbandingan Nilai Spektral Berbagai Metode Resampling Pada Proses Pembuatan Primary Product SPOT 6/7 = Comparison of Various Spectral Value Resampling Methods for Generating SPOT 6/7 Primary Product

Primary Product adalah merupakan level pemrosesan yang paling mendekati dengan gambaran alami yang diperoleh dari sensor. Pada proses produksi data primary, terdapat berbagai macam metode resampling yang dapat digunakan. Dari berbagai macam metode resampling tersebut belum diketahui seberapa besar perubahan nilai spektralnya. Penelitian ini memiliki tujuan untuk mengetahui besar perubahan nilai spektral dari berbagai metode untuk resampling Primary Product SPOT 6/7. Dengan mengetahui besar perubahan nilai spektral setelah proses resampling, maka akan dapat diketahui metode resampling yang konsisten dalam mempertahankan nilai spektral yang penting pada saat akan melakukan proses transformasi citra ataupun klasifikasi citra digital. Pada Penelitian ini, korelasi antara citra resampling menggunakan metode bicubic optimized dan bicubic bilinear dengan near neighbour dapat dibandingkan untuk memperoleh kesimpulan tentang konsistensi metode tersebut dalam mempertahankan nilai spektral citra asli. Hasil penelitian ini menunjukkan bahwa korelasi sangat tinggi antar berbagai metode resampling sehingga bisa dipilih salah satu dari berbagai metode resampling untuk memproduksi Primary Products pada SPOT 6/7. Namun lebih disarankan untuk menggunakan metode resamplingnear neighbour karena lebih mempertahankan nilai spektral citra asli. Primary product is the closest processing level to the raw image acquired by sensor. In the process of generating Primary Product, there are various methods of resampling used to generate this product. Among these various methods of resampling, it is not yet known how much of these resampling methods change the spectral value of the final resampled product. This study aims to determine the changes in the spectral value of the final resampled products for the Primary Product of SPOT 6/7. By knowing how spectral value changes in the final product, the consistency of the resampling methods can be concluded which is important to be considered when performing digitalclassification or spectral ransformation. In this research, the correlation of the resampled imagery using Bicubic Optimized and Bicubic Bilinear methods was compared with Near Neighbour method to conclude which one is the most spectral-consistent method. Based on the analysis, the result indicated that there is a very high correlation between the various resampling methods with near neighbour method so that based on the final correlation analysis, whatever resampling methods can be used for generating primary products of SPOT 6/7. However it is advisable to use near Neighbour resampling method considering that this method retains most of the original spectral values.hlm.327-33

Mapping the invasive palm species Arenga obtusifolia using multiple endmember spectral mixture analysis (MESMA) and PRISMA hyperspectral data in Ujung Kulon National Park, Indonesia

Arenga obtusifolia or “langkap” is an invasive palm that interferes with the growth of native vegetation in Ujung Kulon National Park. This study aims to map A. obtusifolia distribution in Ujung Kulon using PRISMA hyperspectral imagery and MESMA (Multiple Endmember Spectral Mixture Analysis). 38 spectra samples for the A. obtusifolia, sand, wetland vegetation and other vegetation were collected, and optimized using the IES (Iterative Endmember Selection), Multiple Signal Classification (MUSIC) and Automated MUsic and Spectral Separability based Endmember Selection (AMUSES) algorithms. Here, different schemes of MESMA were tested by using spectral weighting (SPEC) and stable zone unmixing (STABLE). Our results demonstrated that MESMA and PRISMA data was able to identify A. obtusifolia with the accuracy ranging from 55.36 (AMUSES-STABLE) to 83.93 (MUSIC-STABLE). Our results indicated the potential of MESMA and PRISMA hyperspectral data using optimized spectra from MUSIC and stable zone unmixing for mapping invasive A. obtusifolia. © 2022 Informa UK Limited, trading as Taylor & Francis Group

Assessment of Gap-Filling Interpolation Methods for Identifying Mangrove Trends at Segara Anakan in 2015 by using Landsat 8 OLI and Proba-V

The existence and services of mangrove ecosystems in Segara Anakan are threatened by changes in land use on land and global warming, which requires proper and intensive monitoring. The monitoring of mangrove and its trend over large areas can be done using multi-temporal remote sensing technology. However, remote sensing data is often contaminated by cloud cover, and its corresponding shadow resulted in missing data. This study aims to assess the performance of the existed gap-filling techniques, such as, linear, spline, stineman,  data interpolation Empirical Orthogonal Function (dineof) and spatial downscaling strategy employing the Proba-V imagery in 100 m, when being used for estimating the missing data and depicting the trend in NDVI from Landsat 8 OLI by using Mann-Kendall test. Our result suggested that EOF-based interpolation gave better prediction results and more accurate in predicting longer missing data. Linear interpolation, on the other hand, was accurate to predict shorter missing data. Overall, all interpolation results can reconstruct 64 (spline) to 72 % (dineof) of missing data in NDVI with the RMSE of 0.10 (dineof) – 0.13 (spline). A consistent decreasing trend was also found from the four interpolation methods, which showed the consistency of the interpolated values when used for deriving trends with similar patterns of overall decreasing trend and magnitude of changes of -0.0095 to -0.0099 (NDVI unit) over the mangrove areas in 2015. The result demonstrated the potential ability of gap-filling methods for simulating the value of missing data and for deriving trends