Multi-temporal landslide activity investigation by spaceborne SAR interferometry: The case study of the Polish Carpathians

The main goal of this research is to verify the activity state of landslides provided by an existing landslide inventory map using Persistent Scatterers (PS) Interferometry (PSInSAR). The study was conducted in the Małopolskie municipality, a rural setting with sparse urbanization in the Polish Flysch Carpathians. PSInSAR has been applied using Synthetic Aperture Radar (SAR) data from ALOS PALSAR and Sentinel 1A/B with different acquisition geometries (ascending and descending orbit) to increase PS coverage and mitigate the geometric effects due to layover and shadowing. The Line-Of-Sight PSInSAR measurements were projected to the steepest slope, which allowed to homogenize the results from diverse acquisition modes and to compare the displacement velocities with different slope orientations. Additionally, landslide intensity (motion rate) and expected damage maps were generated and verified during field investigations. A high correlation between PSInSAR results and in-situ damage observations was confirmed. The activity state and landslide-related expected damage maps have been confirmed for 43 out of a total of 50 landslides investigated in the field. The short temporal baseline provided by both Sentinel satellites (1A/B data) increases the PS density significantly. The study substantiates the usefulness of SAR based landslide activity monitoring for land use and land development, even in rural areas

FISHPOND AQUACULTURE INVENTORY IN MAROS REGENCY OF SOUTH SULAWESI PROVINCE

Currently, fishpond aquaculture becomes an interesting business for investors because of its profit,  and  a  source  of  livelihood  for  coastal  communities.  Inventory  and  monitoring  of  fishpond aquaculture provide important baseline data to determine the policy of expansion and revitalization of the fishpond. The aim of this research was to conduct an inventory and monitoring of fishpond area inMaros regency of South Sulawesi province using Satellite Pour l’Observation de la Terre (SPOT -4) and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Apeture Radar (PALSAR). SPOT image classification process was performed using maximum likelihood supervised classification  method and  the  density  slice  method  for ALOS  PALSAR.  Fishpond  area  from  SPOT data was  9693.58  hectares  (ha),  this  results  have  been  through  the  process  of  validation  and verification by the ground truth data. The fishponds area from PALSAR was 7080.5 Ha, less than the result  from  SPOT  data.  This  was  due  to  the  classification  result  of  PALSAR  data  showing someobjects around fishponds (dike, mangrove, and scrub) separately and were not combined in fishponds area  calculation.  Meanwhile, the  result  of  SPOT -4  image  classification  combined object  around fishponds area

First results of the ALOS PALSAR verification processor

Among the several applications that will take advantage of the newly available data from the ALOS PALSAR instrument, considerable interest is in the peculiar features that derive from the penetration and polarimetric capabilities of the system. These capabilities, new for a single spaceborne sensor, need specific software tools for the processing of the different acquisition modes. This paper presents a verification processor, developed under ESA contract, for the generation of polarimetric, interferometric and polarimetric-interferometric geocoded products derived from ALOS PALSAR data. The processor, developed with a modular approach, contains the following main elements: - Phase-preserving fine resolution processor; - Phase-preserving ScanSAR processor; - Interference removal tools; - Polarimetric calibration tools; - Polarimetric analysis tools; - Fine resolution interferometric processor; - ScanSAR interferometric processor; - Polarimetric-interferometric processor; - Geocoding; - Atmospheric modelling tools. The processor architecture is presented; highlights are given on specific modules and algorithms. Early results are shown, in particular of the processing of polarimetric and polarimetric-interferometric data over different test sites

Identification of woodland vernal pools with seasonal change PALSAR data for habitat conservation

Woodland vernal pools are important, small, cryptic, ephemeral wetland ecosystems that are vulnerable to a changing climate and anthropogenic influences. To conserve woodland vernal pools for the state of Michigan USA, vernal pool detection and mapping methods were sought that would be efficient, cost-effective, repeatable and accurate. Satellite-based L-band radar data from the high (10 m) resolution Japanese ALOS PALSAR sensor were evaluated for suitability in vernal pool detection beneath forest canopies. In a two phase study, potential vernal pool (PVP) detection was first assessed with unsupervised PALSAR (LHH) two season change detection (spring when flooded—summer when dry) and validated with 268, 1 ha field-sampled test cells. This resulted in low false negatives (14%–22%), overall map accuracy of 48% to 62% and high commission error (66%). These results make this blind two-season PALSAR approach for cryptic PVP detection of use for locating areas of high vernal pool likelihood. In a second phase of the research, PALSAR was integrated with 10 m USGS DEM derivatives in a machine learning classifier, which greatly improved overall PVP map accuracies (91% to 93%). This supervised approach with PALSAR was found to produce better mapping results than using LiDAR intensity or C-band SAR data in a fusion with the USGS DEM-derivatives

Potential of Spaceborne X & L-Band SAR-Data for Soil Moisture Mapping Using GIS and its Application to Hydrological Modelling: the Example of Gottleuba Catchment, Saxony / Germany

Hydrological modelling is a powerful tool for hydrologists and engineers involved in the planning and development of integrated approach for the management of water resources. With the recent advent of computational power and the growing availability of spatial data, RS and GIS technologies can augment to a great extent the conventional methods used in rainfall runoff studies; it is possible to accurately describe watershed characteristics in particularly when determining runoff response to rainfall input. The main objective of this study is to apply the potential of spaceborne SAR data for soil moisture retrieval in order to improve the spatial input parameters required for hydrological modelling. For the spatial database creation, high resolution 2 m aerial laser scanning Digital Terrain Model (DTM), soil map, and landuse map were used. Rainfall records were transformed into a runoff through hydrological parameterisation of the watershed and the river network using HEC-HMS software for rainfall runoff simulation. The Soil Conservation Services Curve Number (SCS-CN) and Soil Moisture Accounting (SMA) loss methods were selected to calculate the infiltration losses. In microwave remote sensing, the study of how the microwave interacts with the earth terrain has always been interesting in interpreting the satellite SAR images. In this research soil moisture was derived from two different types of Spaceborne SAR data; TerraSAR-X and ALOS PALSAR (L band). The developed integrated hydrological model was applied to the test site of the Gottleuba Catchment area which covers approximately 400 sqkm, located south of Pirna (Saxony, Germany). To validate the model historical precipitation data of the past ten years were performed. The validated model was further optimized using the extracted soil moisture from SAR data. The simulation results showed a reasonable match between the simulated and the observed hydrographs. Quantitatively the study concluded that based on SAR data, the model could be used as an expeditious tool of soil moisture mapping which required for hydrological modelling

Rapid Mangrove Forest Loss and Nipa Palm (Nypa fruticans) Expansion in the Niger Delta, 2007-2017

Mangrove forests in the Niger Delta are very valuable, providing ecosystem services, such as carbon storage, fish nurseries, coastal protection, and aesthetic values. However, they are under threat from urbanization, logging, oil pollution, and the proliferation of the invasive Nipa Palm (Nypa fruticans). However, there are no reliable data on the current extent of mangrove forest in the Niger Delta, its rate of loss, or the rate of colonization by the invasive Nipa Palm. Here, we estimate the area of Nipa Palm and mangrove forests in the Niger Delta in 2007 and 2017, using 567 ground control points, Advanced Land Observatory Satellite Phased Array L-band SAR (ALOS PALSAR), Landsat and the Shuttle Radar Topography Mission Digital Elevation Model 2000 (SRTM DEM). We performed the classification using Maximum Likelihood (ML) and Support Vector Machine (SVM) methods. The classification results showed SVM (overall accuracy 93%) performed better than ML (77%). Producers (PA) and User’s accuracy (UA) for the best SVM classification were above 80% for most classes; however, these were considerably lower for Nipa Palm (PA—32%, UA—30%). We estimated a 2017 mangrove area of 801,774 ± 34,787 ha (±95% Confidence Interval) ha and Nipa Palm extent of 11,447 ± 7343 ha. Our maps show a greater landward extent than other reported products. The results indicate a 12% (7–17%) decrease in mangrove area and 694 (0–1304)% increase in Nipa Palm. Mapping efforts should continue for policy targeting and monitoring. The mangroves of the Niger Delta are clearly in grave danger from both rapid clearance and encroachment by the invasive Nipa Palm. This is of great concern given the dense carbon stocks and the value of these mangroves to local communities for generating fish stocks and protection from extreme events