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

PERBANDINGAN METODE KLASIFIKASI PENUTUP LAHAN BERBASIS PIKSEL DAN BERBASIS OBYEK MENGGUNAKAN DATA PiSAR-L2 (COMPARISON BETWEEN PIXEL-BASED AND OBJECT-BASED METHODS FOR LAND COVER CLASSIFICATION USING PiSAR-L2 DATA)

PiSAR-L2 program is an experimental program for PALSAR-2 sensor installed on ALOS-2. Research collaboration had been conducted between the Japan Aerospace Exploration Agency (JAXA) and Ministry for Research and Technology of Indonesia in 2012 to assess the ability of PiSAR-L2 data for some applications. This paper explores the utilization of PiSAR-L2 data for land cover classification in forest area using pixel-based and object-based methods, then carried out comparison between the two methods. PiSAR-L2 data full polarization with 2.1 level for Riau province was used. Field data conducted by JAXA team and landcover map from WWF were used as references to collect input and evaluation sample. Pre-processing was done by doing backscatter conversion and filtering, then classification was conducted and it`s accuracy was tested. Two methods were used, 1) Maximum Likelihood Enhance Neighbor classifier for pixel-based and 2) Support Vector Machine for object based classification. The effect of spatial resolution on classification result was also analyzed. The results show that pixel-based produced mixed pixels "salt and pepper", the classification accuracies were 62% for 2.5 m and 83% for 10 m spatial resolution. While the object-based has some advantages: high homogeneity (absence of mixed pixels), clear and sharp boundary among classes, and high accuracy (97% for 10 m spatial resolution), although it was still found errors in some classes. ABSTRAKProgram Polarimetric Interferometric Airborne Synthetic Aperture Radar of L-band version 2 (PiSAR-L2) adalah program eksperimen sensor Phased-Array Synthetic Aperture RADAR-2 (PALSAR-2) yang dipasang pada satelit Advanced Land Observing Satellite-2 (ALOS-2). Kerjasama riset telah dilakukan antara JAXA dan Kementerian Riset dan Teknologi pada 2012 untuk mengkaji kemampuan data PiSAR L-2 yang direkam menggunakan pesawat untuk beberapa aplikasi. Kegiatan ini menggunakan data PiSAR L-2 untuk klasifikasi penutup lahan di wilayah hutan dengan metode klasifikasi berbasis piksel dan berbasis obyek, kemudian membandingkan kedua metode tersebut. Data yang digunakan adalah data PiSAR L-2 polarisasi penuh dengan level 2.1 untuk wilayah Provinsi Riau. Data lapangan diperoleh dari survei lapangan tim JAXA dan peta penutup lahan dari World Wildlife Fund dijadikan sebagai referensi untuk sampel masukan dan pengujian. Pengolahan awal melakukan konversi backscatter dan filtering, kemudian melakukan klasifikasi dan uji akurasi. Dua metode klasifikasi yang digunakan, 1) Metode Maximum Likelihood Enhance Neighbor classifier untuk klasifikasi berbasis piksel dan 2) Metode Support Vector Machine untuk klasifikasi berbasis obyek. Pada kegiatan ini dilakukan analisis pengaruh resolusi spasial terhadap hasil klasifikasi. Hasil memperlihatkan bahwa metode berbasis piksel mempunyai piksel bercampur “salt and pepper”, akurasi klasifikasi adalah 62% untuk spasial resolusi 2.5 m dan 83% untuk spasial resolusi 10 m. Sedangkan klasifikasi berbasis obyek mempunyai kelebihan dengan homogenitas obyek yang tinggi (tidak adanya piksel bercampur), batas antara kelas yang jelas dan tegas, serta akurasi yang tinggi (97% untuk resolusi spasial 10 m), walau masih ada kesalahan pada beberapa kelas penutup lahan

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

Integrating RADAR and optical imagery improve the modelling of carbon stocks in a mopane-dominated African savannah dry forest

This study examined the integration of two satellite data sets, that is Landsat 7 ETM+ and ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture RADAR) in estimating carbon stocks in mopane woodlands of north-western Zimbabwe. Mopane woodlands cover large spatial extents and provide ecosystem benefits to the rural economies and grazing resources for both livestock and wildlife. In this study, artificial neural networks (ANN) were used to estimate carbon stocks based on spectral metrics derived from Landsat 7 ETM+ and ALOS PALSAR. To determine the utility of the two satellite-derived metrics, a two-pronged modelling framework was adopted. Firstly, we used spectral bands and vegetation indices from the two satellite data sets independently, and subsequently, we integrated the metrics from the two satellite data sets into the final model. Results showed that the ALOS PALSAR (R2 = 0.75 and nRMSE = 0.16) and Landsat ETM+ (R2 = 0.78 and nRMSE = 0.14) derived spectral bands and vegetation indices comparatively yielded accurate estimations of carbon stocks. Integrating spectral bands and vegetation indices from both sensors significantly improved the estimation of carbon stocks (R2 = 0.84 and nRMSE = 0.12). These findings underscore the importance of integrating satellite data in vegetation biophysical assessment and monitoring in poorly documented ecosystems such as the mopane woodlands

A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection

To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat- and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series

Characterizing forest disturbance dynamics in the humid tropics using optical and LIDAR remotely sensed data sets

Human-induced tropical deforestation and forest degradation are widely recognized as major environmental threats, negatively affecting tropical forest ecosystem services, such as biodiversity and climate regulation. To mitigate the effects of forest disturbance, particularly carbon emissions, national forest monitoring systems are being established throughout the tropics. Multiple good practice guidelines aimed at developing accurate, compatible and cost-effective monitoring systems have been issued by IPCC, UNFCCC, GFOI and other organizations. However, there is a lack of consensus in characterization of the baseline state of the forests and carbon stocks. This dissertation is focused on the improvement of the current methods of remotely-sensed forest area and carbon loss estimation. A sample-based estimation method employing Landsat-based forest type and change maps and GLAS Lidar-modeled carbon data was first prototyped for the Democratic Republic of the Congo (DRC), and then applied for the entire pan-tropical region. The DRC study found that Landsat-scale (30m) map-based forest loss assessments unadjusted for errors may lead to significant underestimation of forest aboveground carbon (AGC) loss in the environments with small-scale land cover change dynamics. This conclusion was supported by the pan-tropical study, which revealed that Landsat-based mapping omitted almost half (44%) of forest loss in Africa compared to the sample-based estimate (sample-based estimate exceeded map-based by 78%). Landsat performed well in Latin America and Southeast Asia (sample-based estimate exceeded map-based by 15% and 6% respectively), where forest dynamics are dominated by large-scale industrial forest clearings. The pan-tropical validation sample also allowed disaggregating forest cover and AGC loss by occurrence in natural- (primary and mature secondary forests, and natural woodlands) or human-managed (tree plantations, agroforestry systems, areas of subsistence agriculture with rapid tree cover rotation) forests. Pan-tropically, 58% of AGC loss came from natural forests, with proportion of natural AGC loss being the highest in Brazil (72%) and the lowest in the humid tropical Africa outside of the DRC (22%). The pan-tropical study employed a novel forest stratification for carbon estimation based on forest structural characteristics (canopy cover and height) and intactness, which aided in reducing standard errors of the sample-based estimate (SE of 4% for the pan-tropical gross forest loss area estimate). Such a stratification also allowed for the quantification of forest degradation by delineating intact and non-intact forest areas with different carbon content. This indirect approach to quantify forest degradation was advanced in the last research chapter by automating the process of intact (hinterland) forest mapping. Hinterland forests are defined as forest patches absent of and removed from disturbance in near-term history. Their utility in using spatial context to map structurally different (degraded and non-degraded) forests points a way forward for improved stratification of forest carbon stocks. Conclusions from the dissertation summarize strengths and challenges of sample-based area estimation in monitoring forest carbon stocks and the possible use of such estimates in the revision of spatially explicit maps by adjusting them to match the unbiased sample-based estimates. Hinterland forest maps, in addition to providing a valuable stratum for sample-based carbon monitoring, may serve as a baseline for the near real-time monitoring of remaining ecologically intact tropical forests

Utilization of bistatic TanDEM-X data to derive land cover information

Forests have significance as carbon sink in climate change. Therefore, it is of high importance to track land use changes as well as to estimate the state as carbon sink. This is useful for sustainable forest management, land use planning, carbon modelling, and support to implement international initiatives like REDD+ (Reducing Emissions from Deforestation and Degradation). A combination of field measurements and remote sensing seems most suitable to monitor forests. Radar sensors are considered as high potential due to the weather and daytime independence. TanDEM-X is a interferometric SAR (synthetic aperture radar) mission in space and can be used for land use monitoring as well as estimation of biophysical parameters. TanDEM-X is a X-band system resulting in low penetration depth into the forest canopy. Interferometric information can be useful, whereas the low penetration can be considered as an advantage. The interferometric height is assumable as canopy height, which is correlated with forest biomass. Furthermore, the interferometric coherence is mainly governed by volume decorrelation, whereas temporal decorrelation is minimized. This information can be valuable for quantitative estimations and land use monitoring. The interferometric coherence improved results in comparison to land use classifications without coherence of about 10% (75% vs. 85%). Especially the differentiation between forest classes profited from coherence. The coherence correlated with aboveground biomass in a R² of about 0.5 and resulted in a root mean square error (RSME) of 14%. The interferometric height achieved an even higher correlation with the biomass (R²=0.68) resulting in cross-validated RMSE of 7.5%. These results indicated that TanDEM-X can be considered as valuable and consistent data source for forest monitoring. Especially interferometric information seemed suitable for biomass estimation

Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms

Forests are one of the major carbon sinks that significantly contribute towards achieving targets of the Kyoto Protocol, and its successors, in reducing greenhouse (GHG) emissions. In order to contribute to regular National Inventory Reporting, and as part of the on-going development of the Irish national GHG reporting system (CARBWARE), improvements in characterisation of changes in forest carbon stocks have been recommended to provide a comprehensive information flow into CARBWARE. The Irish National Forest Inventory (NFI) is updated once every six years, thus there is a need for an enhanced forest monitoring system to obtain annual forest updates to support government agencies and forest management companies in their strategic decision making and to comply with international GHG reporting standards. Sustainable forest management is imperative to promote net carbon absorption from forests. Based on the NFI data, Irish forests have removed or sequestered an average of 3.8 Mt of atmospheric CO2 per year between 2007 and 2016. However, unmanaged and degraded forests become a net emitter of carbon. Disturbances from human induced activities such as clear felling, thinning and deforestation results in carbon emissions back into the atmosphere. Funded by the Department of Agriculture, Food and the Marine (DAFM, Ireland), this PhD study focuses on exploring the potential of data from L-band Synthetic Aperture Radar (SAR) satellite based sensors for monitoring changes in the small stand forests of Ireland. Historic data from ALOS PALSAR in the late 2000s and more recent data from ALOS-2 PALSAR-2 sensors have been used to map forest areas and characterise the different disturbances observed within three different regions of Ireland. Forest mapping and disturbance characterisation was achieved by combining the machine learning supervised Random Forests (RF) and unsupervised Iterative Self-Organizing Data Analysis (ISODATA) classification techniques. The lack of availability of ground truth data supported use of this unsupervised approach which forms natural clusters based on their multi-temporal signatures, with divergence statistics used to select the optimal number of clusters to represent different forest classes. This approach to forest monitoring using SAR imagery has not been reported in the peer-review literature and is particularly beneficial where there is a dearth of ground-based information. When applied to the forests, mapped with an accuracy of up to 97% by RF, the ISODATA technique successfully identified the unique multi-temporal pattern associated with clear-fells which exhibited a decrease of 4 to 5 decibels (dB) between the images acquired before and after the event. The clustering algorithm effectively highlighted the occurrence of other disturbance events within forests with a decrease of 2±0.5dB between two consecutive years, as well as areas of tree growth and afforestation. A highlight of the work is the successful transferability of the algorithm, developed using ALOS PALSAR, to ALOS-2 PALSAR-2 data thereby demonstrating the potential continuity of annual forest monitoring. The higher spatial and radiometric resolutions of ALOS-2 PALSAR-2 data have shown improvements in forest mapping compared to ALOS PALSAR data. From mapping a minimum forest size of 1.8 ha with ALOS PALSAR, a minimum area of 1.1 ha was achieved with the ALOS-2 PALSAR-2 images. Moreover, even with some different backscatter characteristics of images acquired in different seasons, similar signature patterns between the sensors were retrieved that helped to define the cluster groups, thus demonstrating the robustness of the algorithm and its successful transferability. Having proven the potential to monitor forest disturbances, the results from both the sensors were used to detect deforestation over the time period 2007-2016. Permanent land-use changes pertaining to conversion of forests to agricultural lands and windfarms were identified which are important with respect to forest monitoring and carbon reporting in Ireland. Overall, this work has presented a viable approach to support forest monitoring operations in Ireland. By providing disturbance information from SAR, it can supplement projects working with optical images which are generally limited by cloud cover, particularly in parts of northern, western and upland Ireland. This approach adds value to ground based forest monitoring by mapping distinct forests over large areas on an annual basis. This study has demonstrated the ability to apply the algorithm to three different study areas, with a vision to operationalise the algorithm on a national scale. The main limitations experienced in this study were the lack of L-band SAR data availability and reference datasets. With typically only one image acquired per year, and discrepancies and omissions existing within reference datasets, understanding the behaviour of certain cluster groups representing disturbances was challenging. However, this approach has addressed some issues within the reference datasets, for example locating areas for which a felling licence was granted but where trees were never cut, by providing detailed systematic mapping of forests. Future satellites such as Tandem-L, SAOCOM-2A and 2B, P-band BIOMASS mission and ALOS-4 PALSAR-3 may overcome the issue of limited SAR image acquisitions provided more images per year are available, especially during the summer months