https://gispub4.epa.gov/APS/documents/MODIS_Change_Detection.10_06.pdf

Land-cover change detection using multi-temporal MODIS NDVI data

Identifying and characterizing vascular plants in time and space is required in various disciplines, e.g. in forestry, conservation and agriculture. Remote sensing emerged as a key technology revealing both spatial and temporal vegetation patterns. Harnessing the ever growing streams of remote sensing data for the increasing demands on vegetation assessments and monitoring requires efficient, accurate and flexible methods for data analysis. In this respect, the use of deep learning methods is trend-setting, enabling high predictive accuracy, while learning the relevant data features independently in an end-to-end fashion. Very recently, a series of studies have demonstrated that the deep learning method of Convolutional Neural Networks (CNN) is very effective to represent spatial patterns enabling to extract a wide array of vegetation properties from remote sensing imagery. This review introduces the principles of CNN and distils why they are particularly suitable for vegetation remote sensing. The main part synthesizes current trends and developments, including considerations about spectral resolution, spatial grain, different sensors types, modes of reference data generation, sources of existing reference data, as well as CNN approaches and architectures. The literature review showed that CNN can be applied to various problems, including the detection of individual plants or the pixel-wise segmentation of vegetation classes, while numerous studies have evinced that CNN outperform shallow machine learning methods. Several studies suggest that the ability of CNN to exploit spatial patterns particularly facilitates the value of very high spatial resolution data. The modularity in the common deep learning frameworks allows a high flexibility for the adaptation of architectures, whereby especially multi-modal or multi-temporal applications can benefit. An increasing availability of techniques for visualizing features learned by CNNs will not only contribute to interpret but to learn from such models and improve our understanding of remotely sensed signals of vegetation. Although CNN has not been around for long, it seems obvious that they will usher in a new era of vegetation remote sensing.

Review on Convolutional Neural Networks (CNN) in vegetation remote sensing

Accuracy assessment of land-cover classifications derived from remote sensing data has been recognized as a valuable tool in judging the fitness of these data for a particular application. Recent research initiatives in the area of spatial data accuracy and integration of remote sensing data in geographic information systems have revived the discussion on accuracy assessment. This article aims at contributing to this discussion by means of a review based on a division into positional and thematic accuracy. An important observation is that there are a limited number of methods for assessing data accuracy. However, the applied definitions differ very much from author to author, especially in the assessment of thermatic accuracy. Accuracy assessment mostly yields one single measure such as root-mean-square error or proportion of pixels correctly classified. These single measures do not give sufficient information and they can be based on statistically or methodologically non-valid methods. Therefore, not a single measure but also the total process of assessing these measures should explicitly be reported.

Accuracy assessment of satellite derived land - cover data : a review

In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial data collection in which not only few specialist data providers collect and deliver remotely sensed data, but a whole diverse community is potentially able to gather geospatial data that fit their needs. However, the diversification of sensing systems and user applications challenges the common application of good practice procedures that ensure the quality of the data. This challenge can only be met by establishing and communicating common procedures that have had demonstrated success in scientific experiments and operational demonstrations. In this review, we evaluate the state-of-the-art methods in UAV spectral remote sensing and discuss sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation. We follow the 'journey' of the reflected energy from the particle in the environment to its representation as a pixel in a 2D or 2.5D map, or 3D spectral point cloud. Additionally, we reflect on the current revolution in remote sensing, and identify trends, potential opportunities, and limitations.

/pdf/quantitative-remote-sensing-at-ultra-high-resolution-with-1fowvgc7qt.pdf

Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

Today, validation or accuracy assessment is an integral component of most mapping projects incorporating remotely sensed data. Other spatial information may not be so stringently evaluated, but at least requires meta-data that documents how the information was generated. This emphasis on data quality was not always the case. In the 1970s only a few brave scientists and researchers dared ask the question, 'How good is this map derived from Landsat MSS imagery?' In the 1980s, the use of the error matrix became a common tool for representing the accuracy of individual map categories. By the 1990s, most maps derived from remotely sensed imagery were required to meet some minimum accuracy standard. A similar progression can be outlined for other spatial information. However, this progression is about 5 years behind the validation of remotely sensed data. This paper presents a series of steps moving towards better assessment and validation of spatial information and asks the reader to evaluate where they are in this series and to move forward.

/pdf/accuracy-assessment-and-validation-of-remotely-sensed-and-3zmweswf3j.pdf

Accuracy assessment and validation of remotely sensed and other spatial information

Error associated with the remote sensing and GIS data acquisition, processing, analysis, conversion, and final product presentation can have a significant impact on the confidence of decisions made using the data. The goal of this paper is to provide a broad overview of spatial data error sources, and to identify priority research topics which will reduce impediments and enhance the quality of integrated remote sensing and GIS data. Potential sources of error will be identified at each data integration process step. Impacts of error propagation on decision making and implementation processes will be assessed, and priority error quantification research topics will be recommended.

L. K. Fenstermaker

Papers

Remote sensing and Geographic Information System data integration: error sources and research issues