Monitoring of an Indonesian Tropical Wetland by Machine Learning-Based Data Fusion of Passive and Active Microwave Sensors

In this study, a novel data fusion approach was used to monitor the water-body extent in a tropical wetland (Lake Sentarum, Indonesia). Monitoring is required in the region to support the conservation of water resources and biodiversity. The developed approach, random forest database unmixing (RFDBUX), makes use of pixel-based random forest regression to overcome the limitations of the existing lookup-table-based approach (DBUX). The RFDBUX approach with passive microwave data (AMSR2) and active microwave data (PALSAR-2) was used from 2012 to 2017 in order to obtain PALSAR-2-like images with a 100 m spatial resolution and three-day temporal resolution. In addition, a thresholding approach for the obtained PALSAR-2-like backscatter coefficient images provided water body extent maps. The validation revealed that the spatial patterns of the images predicted by RFDBUX are consistent with the original PALSAR-2 backscatter coefficient images (r = 0.94, RMSE = 1.04 in average), and that the temporal pattern of the predicted water body extent can track the wetland dynamics. The PALSAR-2-like images should be a useful basis for further investigation of the hydrological/climatological features of the site, and the proposed approach appears to have the potential for application in other tropical regions worldwide

PALSAR-2/ALOS-2 AND OLI/LANDSAT-8 DATA INTEGRATION FOR LAND USE AND LAND COVER MAPPING IN NORTHERN BRAZILIAN AMAZON

In northern Brazilian Amazon, the crops, savannahs and rainforests form a complex landscape where land use and land cover (LULC) mapping is difficult. Here, data from the Operational Land Imager (OLI)/Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)/ALOS-2 were combined for mapping 17 LULC classes using Random Forest (RF) during the dry season. The potential thematic accuracy of each dataset was assessed and compared with results of the hybrid classification from both datasets. The results showed that the combination of PALSAR-2 HH/HV amplitudes with the reflectance of the six OLI bands produced an overall accuracy of 83% and a Kappa of 0.81, which represented an improvement of 6% in relation to the RF classification derived solely from OLI data. The RF models using OLI multispectral metrics performed better than RF models using PALSAR-2 L-band dual polarization attributes. However, the major contribution of PALSAR-2 in the savannahs was to discriminate low biomass classes such as savannah grassland and wooded savannah

ANALYSIS OF THE TARGET DECOMPOSITION TECHNIQUE ATTRIBUTES AND POLARIMETRIC RATIOS TO DISCRIMINATE LAND USE AND LAND COVER CLASSES OF THE TAPAJÓS REGION

This study aims to analyze the capability of the target decomposition techniques and the polarimetric ratios applied to the ALOS/PALSAR-2 satellite polarimetric images to discriminate the land use and land cover classes in the Tapajós National Forest region, Pará State. Three full polarimetric ALOS/PALSAR-2, level 1 single look complex scenes were selected to generate the coherence and the covariance matrices to derive the Cloude-Pottier and the Freeman-Durden target decomposition attributes. From the radiometrically calibrated PALSAR-2 images, we generated the backscatter coefficients, the cross polarized ratio (RC; HV/HH), the parallel polarized ratio (RP; VV/HH) and the Radar Forest Degradation Index (RFDI). The images resulting from these polarimetric attributes were processed by the Maximum Likelihood (MAXVER) classifier coupled with the Iterated Conditional Modes (ICM) contextual algorithm. We found that the classifications derived from the target decomposition attributes, mainly from the CloudePottier technique, with a Kappa index of 0.75, presented a significant higher performance than those derived from the RC ratio, RP ratio, and RFDI

Extraction of forest plantation extents using majority voting classification fusion algorithm

© 2018 Proceedings - 39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018 Satellite Phased Array L-band Synthetic Aperture Radar-2 has great advantages in extracting natural and industrial forest plantation in tropical areas, but it suffers from presence of speckle that create problem to identify the forest body. Optimal fusion of Landsat-8 operational land imager bands with ALOS PALSAR-2 can provide the ideal complementary information for an accurate forest extraction while suppressing unwanted information. The goal of this study is to analyze the potential ability of Landsat-8 OLI and ALOS PALSAR-2 as complementary data resources in order to extract land cover especially forest types. Comprehensive preprocessing analysis (e.g. geometric correction, filtering enhancement and polarization combination) were conducted on ALOS PALSAR-2 dataset in order to make the imagery ready for processing. Principal component index method as one of the most effective Pan-Sharpening fusion approaches was used to synthesize Landsat and ALOS PALSAR-2 images. Three different classifiers methods (support vector machine, k-nearest neighborhood, and random forest) were employed and then fused by majority voting algorithm to generate more robust and precise classification result. Accuracy of the final fused result was assessed on the basis of ground truth points by using confusion matrices and kappa coefficient. This study proves that the accurate and reliable majority voting fusion method can be used to extract large-scale land cover with emphasis on natural and industrial forest plantation from synthetic aperture radar and optical datasets

Damage mapping after the 2017 Puebla Earthquake in Mexico using high-resolution Alos2 Palsar2 data

On September 19, 2017, the Mw7.1 Puebla Earthquake caused significant destruction in several cities in central Mexico. In this paper, two pre- and one post-event ALOS2-PALSAR2 data were used to detect the damaged area around Izucar de Matamoros town in Mexico. First, we identify the built-up areas using pre-event data. Second, we evaluate the earthquake-induced damage areas using an RGB color-coded image constructed from the pre- and co-event coherence images. Our analysis showed that the green and red bands display a great potential to discriminate the damaged areas.Accepted manuscrip

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies drive innovations in forest resource assessments and monitoring at varying scales. Data acquired with airborne and spaceborne platforms provide us with higher spatial resolution, more frequent coverage and increased spectral information. Recent developments in ground-based sensors have advanced three dimensional (3D) measurements, low-cost permanent systems and community-based monitoring of forests. The REDD+ mechanism has moved the remote sensing community in advancing and developing forest geospatial products which can be used by countries for the international reporting and national forest monitoring. However, there still is an urgent need to better understand the options and limitations of remote and close-range sensing techniques in the field of degradation and forest change assessment. This Special Issue contains 12 studies that provided insight into new advances in the field of remote sensing for forest management and REDD+. This includes developments into algorithm development using satellite data; synthetic aperture radar (SAR); airborne and terrestrial LiDAR; as well as forest reference emissions level (FREL) frameworks

Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce annual, global 25 m forest maps between 2007 and 2010, and the latest global forest maps of 2015 and 2016 were produced by using the ALOS-2 PALSAR-2 data. However, annual 25 m spatial resolution forest maps during 2011–2014 are missing because of the gap in operation between ALOS and ALOS-2, preventing the construction of a continuous, fine resolution time-series dataset on the world's forests. In contrast, the MODerate Resolution Imaging Spectroradiometer (MODIS) NDVI images were available globally since 2000. This research developed a novel method to produce annual 25 m forest maps during 2007–2016 by fusing the fine spatial resolution, but asynchronous PALSAR/PALSAR-2 with coarse spatial resolution, but synchronous MODIS NDVI data, thus, filling the four-year gap in the ALOS and ALOS-2 time-series, as well as enhancing the existing mapping activity. The method was developed concentrating on two key objectives: 1) producing more accurate 25 m forest maps by integrating PALSAR/PALSAR-2 and MODIS NDVI data during 2007–2010 and 2015–2016; 2) reconstructing annual 25 m forest maps from time-series MODIS NDVI images during 2011–2014. Specifically, a decision tree classification was developed for forest mapping based on both the PALSAR/PALSAR-2 and MODIS NDVI data, and a new spatial-temporal super-resolution mapping was proposed to reconstruct the 25 m forest maps from time-series MODIS NDVI images. Three study sites including Paraguay, the USA and Russia were chosen, as they represent the world's three main forest types: tropical forest, temperate broadleaf and mixed forest, and boreal conifer forest, respectively. Compared with traditional methods, the proposed approach produced the most accurate continuous time-series of fine spatial resolution forest maps both visually and quantitatively. For the forest maps during 2007–2010 and 2015–2016, the results had greater overall accuracy values (>98%) than those of the original JAXA forest product. For the reconstructed 25 m forest maps during 2011–2014, the increases in classifications accuracy relative to three benchmark methods were statistically significant, and the overall accuracy values of the three study sites were almost universally >92%. The proposed approach, therefore, has great potential to support the production of annual 25 m forest maps by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

Procjena visinske točnosti digitalnog modela terena (DMT) ALOS PALSAR-2 u području Rote Dead Sea – Indonezija

Vertical accuracy evaluation is essential to determine in order to know the quality of a DTM. This quality will affect the scale type and utilization of the DTM. The study data use DTM ALOS PALSAR-2. This study evaluates the vertical accuracy of the DTM ALOS PALSAR-2 with different height reference fields in the Rote Dead Sea Area, Indonesia. Each DTM is made with the EGM 1996, EGM 2008, and WGM 2012. The three DTMs extracted based on the height reference field will have different orthometric heights; therefore, an evaluation of the vertical accuracy is needed to determine the quality of the three DTMs. They compare with field measurements from GNSS-levelling. This test is carried out at lowland and highland, using 10 test points. For the lowland area, the RMSE (z) in height at DTM is 1.363 m for WGM 2012, 2.017 m for EGM 2008, and 1.934 m for EGM 1996. For the highlands area, the RMSE (z) in height at DTM is 1.185 m for WGM 2012, 1.201 m for EGM 2008, and 1.432 m for EGM 1996. The DTM-WGM 2012 and DTM-EGM 1996 are recommended to use in this area because they have higher vertical accuracy. The vertical accuracy test in the Rote lowland corresponds to class 2 and class 3 on a scale of 1:10,000. The vertical accuracy test results in the Rote highland correspond to class 1 and class 2 on a scale of 1:10,000. The ALOS PALSAR-2 DTM vertical accuracy test results can be used for mapping scales of 1:10,000 – 1:25,000 in Rote.Procjenu visinske točnosti neophodno je obaviti kako bi se utvrdila kvaliteta DMT-a. Ta će kvaliteta utjecati na vrstu mjerila i korištenje DMT-a. U studiji se koriste podaci iz DMT-a ALOS PALSAR-2. Ova studija procjenjuje visinsku točnost DMT-a ALOS PALSAR-2 s različitim referentnim područjima visine u području Rote Dead Sea, Indonezija. Svaki DMT izrađen je uz pomoć EGM 1996, EGM 2008 i WGM 2012. Tri DMT-a, koja se temelje na referentnim područjima visine, imat će različite ortometrijske visine; zato je potrebna procjena visinske točnosti kako bi se odredila kvaliteta ta tri DMT-a. Oni se uspoređuju s terenskim mjerenjima dobivenima GNSS-niveliranjem. Ovo ispitivanje provodi se u nizinskim i brdskim područjima koristeći 10 testnih točaka. Za nizinsko područje, RMSE (z) u visini na DMT-u je 1,363 m za WGM 2012, 2,017 m za EGM 2008 i 1,934 m za EGM 1996. Za brdsko područje, RMSE (z) u visini na DMT-u je 1,185 m za WGM 2012, 1,201 m za EGM 2008 i 1,432 m za EGM 1996. DMT-WGM 2012 i DMT-EGM 1996 preporučuju se za korištenje u tom području jer imaju veću visinsku točnost. Ispitivanje visinske točnosti u nizinskom području otoka Rote odgovara klasi 2 i klasi 3 u mjerilu 1:10 000. Ispitivanje visinske točnosti u brdskom području otoka Rote odgovara klasi 1 i klasi 2 u mjerilu 1:10 000. Rezultati ispitivanja visinske točnosti DMT-a ALOS PALSAR-2 mogu se koristiti za mjerila kartiranja 1:10 000 – 1:25 000 na otoku Rote

Combination of PALSAR-2 Observation and VDES Operation for Monitoring Illegal, Unreported and Unregulated Fishing Activities

Indonesian Exclusive Economic Zone (EEZ) is the sixth largest in the world. Fisheries in Indonesian EEZ are active and producing high amount of products, but illegal, unreported and unregulated (IUU) fishing activities are also recognized. For monitoring and covering large area quantitatively, satellites are effective. The Indonesian government inspected 2,827 IUU fishing vessels and arrested 167 of them in 2021. It was also estimated that one illegal trawl fishing vessel would be able to lead 1.2 million USD of economic losses in Indonesia annually. The author put forward to use two different types of satellites for monitoring and managing wide areas for marine activities: radar observations and communication networks. The author and his colleagues have cooperated with Indonesian government to enhance their abilities of satellite data utilization for monitoring IUU fishing activities in Indonesian EEZ since 2021. PALSAR-2, the Japanese synthetic aperture radar (SAR) sensor on the Japanese satellite named ALOS-2 has ScanSAR Normal Mode (PALSAR-2/ScanSAR) which covers area of 350.5 km * 355 km, and it is effective for monitoring wide offshore areas of Indonesia. Although the average size of fishing vessels is less than 20 m and smaller than 50 m resolution of PALSAR-2/ScanSAR. The author and his colleagues demonstrated abilities of PALSAR-2/ScanSAR for monitoring IUU fishing activities in June 2022 by taking a vessel to West Bali on the PALSAR-2 observation date. Effectiveness of satellite utilization for monitoring IUU fishing activities was proved by the demonstration, but the monitoring frequencies and management system for fishing activities is not good enough. VHF Data Exchange System (VDES) is now expected as the next generation of Automatic Identification System (AIS) for managing vessel activities in the world. Small satellites which load VDES antenna for covering 2,000 km of radius has been discussed and examined in Japan, too. AIS is a system which transmits information to unspecified number of devices. On the other hand, VDES is expected to provide information network system in the oceans, and users would use server systems. The real time communications among vessels for accident avoidance, and business communications between the land and vessels would be realized by VDES. Moreover, constellation of about 60 small satellites which load VDES antenna would be able to cover and provide real time communication network in the whole areas of the Earth. The fishing activities would be monitored and managed by two ways: satellite data analysis for detecting vessel locations and communications among vessels. Not only monitoring IUU fishing activities but also monitoring maritime accident, realizing traceability, and managing marine resources would be realized by the combination, and it is also expected to contribute the fair marine uses