PALSAR wide-area mapping and annual monitoring methodology for Borneo

This paper describes the operational radar mapping processing chain developed and steps taken to produce a provisional wide-area PALSAR forest and land cover map of Borneo for the year 2007, compliant with emerging international standards (CEOS guidelines, FAO LCCS). The methodology is based on the classification of FBS and FBD image pairs. To cover Borneo the equivalent of 554 standard images is required. The final overall accuracy assessment result shows this demonstration map product is in 85.5% full agreement with the independent reference dataset and in 7.8% ‘partial agreement’. Monitoring land cover change on an annual basis requires consistent year-to-year mapping. This implies that the localised and temporal effects of environmental factors on the backscatter level (such as inundation or El Niño drought) and variation due to differing observation dates/cycles (related to change of season) have to be accounted for strip by strip. New concepts for (a) automated intercalibration of radar data, (b) time-consistency and (c) automated adaptation of radar signatures to changing environmental conditions have been evaluated for its usefulness to improve the classification and the consistency of annual monitoring

Key issues in rigorous accuracy assessment of land cover products

© 2019 Accuracy assessment and land cover mapping have been inexorably linked throughout the first 50 years of publication of Remote Sensing of Environment. The earliest developers of land-cover maps recognized the importance of evaluating the quality of their maps, and the methods and reporting format of these early accuracy assessments included features that would be familiar to practitioners today. Specifically, practitioners have consistently recognized the importance of obtaining high quality reference data to which the map is compared, the need for sampling to collect these reference data, and the role of an error matrix and accuracy measures derived from the error matrix to summarize the accuracy information. Over the past half century these techniques have undergone refinements to place accuracy assessment on a more scientifically credible footing. We describe the current status of accuracy assessment that has emerged from nearly 50 years of practice and identify opportunities for future advances. The article is organized by the three major components of accuracy assessment, the sampling design, response design, and analysis, focusing on good practice methodology that contributes to a rigorous, informative, and honest assessment. The long history of research and applications underlying the current practice of accuracy assessment has advanced the field to a mature state. However, documentation of accuracy assessment methods needs to be improved to enhance reproducibility and transparency, and improved methods are required to address new challenges created by advanced technology that has expanded the capacity to map land cover extensively in space and intensively in time

Shrunken Locally Linear Embedding for Passive Microwave Retrieval of Precipitation

This paper introduces a new Bayesian approach to the inverse problem of passive microwave rainfall retrieval. The proposed methodology relies on a regularization technique and makes use of two joint dictionaries of coincidental rainfall profiles and their corresponding upwelling spectral radiative fluxes. A sequential detection-estimation strategy is adopted, which basically assumes that similar rainfall intensity values and their spectral radiances live close to some sufficiently smooth manifolds with analogous local geometry. The detection step employs a nearest neighborhood classification rule, while the estimation scheme is equipped with a constrained shrinkage estimator to ensure stability of retrieval and some physical consistency. The algorithm is examined using coincidental observations of the active precipitation radar (PR) and passive microwave imager (TMI) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. We present promising results of instantaneous rainfall retrieval for some tropical storms and mesoscale convective systems over ocean, land, and coastal zones. We provide evidence that the algorithm is capable of properly capturing different storm morphologies including high intensity rain-cells and trailing light rainfall, especially over land and coastal areas. The algorithm is also validated at an annual scale for calendar year 2013 versus the standard (version 7) radar (2A25) and radiometer (2A12) rainfall products of the TRMM satellite

Evaluation of neural network pattern classifiers for a remote sensing application

This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing setshttps://ssrn.com/abstract=1523788%20or%20http://dx.doi.org/10.2139/ssrn.1523788Published versio

Tree species classification using Sentinel-2 imagery and Bayesian inference

The increased temporal frequency of optical satellite data acquisitions provides a data stream that has the potential to improve land cover mapping, including mapping of tree species. However, for large area operational mapping, partial cloud cover and different image extents can pose challenges. Therefore, methods are needed to assimilate new images in a straightforward way without requiring a total spatial coverage for each new image. This study shows that Bayesian inference applied sequentially has the potential to solve this problem. To test Bayesian inference for tree species classification in the boreo-nemoral zone of southern Sweden, field data from the study area of Remningstorp (58?27?18.35?N, 13?39?8.03?E) were used. By updating class likelihood with an increasing number of combined Sentinel-2 images, a higher and more stable cross-validated overall accuracy was achieved. Based on a Mahalanobis distance, 23 images were automatically chosen from the period of 2016 to 2018 (from 142 images total). An overall accuracy of 87% (a Cohen?s kappa of 78.5%) was obtained for four tree species classes: Betula spp., Picea abies, Pinus sylvestris, and Quercus robur. This application of Bayesian inference in a boreo-nemoral forest suggests that it is a practical way to provide a high and stable classification accuracy. The method could be applied where data are not always complete for all areas. Furthermore, the method requires less reference data than if all images were used for classification simultaneously

A statistical approach for predicting grassland degradation in disturbance-driven landscapes

Maintaining a land base that supports safe and realistic training operations is a significant challenge for military land managers which can be informed by frequent monitoring of land condition in relation to management practices. This study explores the relationship between fire and trends in tallgrass prairie vegetation at military and non -military sites in the Kansas Flint Hills. The response variable was the longterm linear trend (2001-2010) of surface greenness measured by MODIS NDVI using BFAST time series trend analysis. Explanatory variables included fire regime (frequency and seasonality) and spatial strata based on existing management unit boundaries. Several non-spatial generalized linear models (GLM) were computed to explain trends by fire regime and/or stratification. Spatialized versions of the GLMs were also constructed. For non-spatial models at the military site, fire regime explained little (4%) of the observed surface greenness trend compared to strata alone (7% - 26%). The non-spatial and spatial models for the non -military site performed better for each explanatory variable and combination tested with fire regime. Existing stratifications contained much of the spatial structure in model residuals. Fire had only a marginal effect on surface greenness trends at the military site despite the use of burning as a grassland management tool. Interestingly, fire explained more of the trend at the nonmilitary site and models including strata improved explanatory power. Analysis of spatial model predictors based on management unit stratification suggested ways to reduce the number of strata while achieving similar performance and may benefit managers of other public areas lacking sound data regarding land usage

Forest land management by satellite: LANDSAT-derived information as input to a forest inventory system

The author has identified the following significant results. Analysis of LANDSAT temporal data, specifically the digitally merged winter and summer scenes, provided the best overall classification results. Comparison of temporal classification results with available ground truth reveal a 94% agreement in the delineation of hardwood categories, a 96% agreement for the combined pine category, and a greater than 50% agreement for each individual pine subcategory. For nearly 1000 acres, compared clearcut acreage estimated with LANDSAT digital data differed from company inventory records by only 3%. Through analysis of summer data, pine stands were successfully classified into subcategories based upon the extent of crown closure. Maximum spectral separability of hardwood and pine stands was obtained from the analysis of winter data