Development of time series stacks of Landsat images for reconstructing forest disturbance history

Abstract

Forest dynamics is highly relevant to a broad range of earth science studies, many of which have geographic coverage ranging from regional to global scales. While the temporally dense Landsat acquisitions available in many regions provide a unique opportunity for understanding forest disturbance history dating back to 1972, large quantities of Landsat images will need to be analysed for studies at regional to global scales. This will not only require effective change detection algorithms, but also highly automated, high level preprocessing capabilities to produce images with subpixel geolocation accuracies and best achievable radiometric consistency, a status called imagery-ready-to-use (IRU). This paper describes a streamlined approach for producing IRU quality Landsat time series stacks (LTSS). This approach consists of an image selection protocol, high level preprocessing algorithms and IRU quality verification procedures. The high level preprocessing algorithms include updated radiometric calibration and atmospheric correction for calculating surface reflectance and precision registration and orthorectification routines for improving geolocation accuracy. These automated routines have been implemented in the Landsat Ecosystem Disturbance Adaptive System (LEDAPS) designed for processing large quantities of Landsat images. Some characteristics of the LTSS developed using this approach are discussed.     

A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies

Recently, five Global LAnd Surface Satellite (GLASS) products have been released: leaf area index (LAI), shortwave broadband albedo, longwave broadband emissivity, incident short radiation, and photosynthetically active radiation (PAR). The first three products cover the years 1982–2012 (LAI) and 1981–2010 (albedo and emissivity) at 1–5 km and 8-day resolutions, and the last two radiation products span the period 2008–2010 at 5 km and 3-h resolutions. These products have been evaluated and validated, and the preliminary results indicate that they are of higher quality and accuracy than the existing products. In particular, the first three products have much longer time series, and are therefore highly suitable for various environmental studies. This paper outlines the algorithms, product characteristics, preliminary validation results, potential applications and some examples of initial analysis of these products.     

Polarimetric synthetic aperture radar change detection for specific land cover types

This paper presents a supervised polarimetric synthetic aperture radar (PolSAR) change detection method applied to specific land cover types. For each pixel of a PolSAR image, its target scattering vector can be modeled as having a complex multivariate normal distribution. Based on this assumption, the joint distribution of two corresponding vectors in a pair of PolSAR images is derived. Then, a generalized likelihood ratio test statistic for the equality of two likelihood functions of such joint distribution is considered and a maximum likelihood distance measure for specific land cover types is presented. Subsequently, the Kittler and Illingworth minimum error threshold segmentation method is applied to extract the specific changed areas. Experiments on two repeat-pass Radarsat-2 fully polarimetric images of Suzhou, China, demonstrate that the proposed change detection method gives a good performance in determining the specific changed areas in PolSAR images, especially the areas that have changed to water.     

North Carolina’s forest disturbance and timber production assessed using time series Landsat observations

Wood products provide a relatively long-term carbon storage mechanism. Due to lack of consistent datasets on these products, however, it is difficult to determine their carbon contents. The main purpose of this study was to quantify forest disturbance and timber product output (TPO) using time series Landsat observations for North Carolina. The results revealed that North Carolina had an average forest disturbance rate of 178,000 ha per year from 1985 to 2010. The derived disturbance products were found to be highly correlated with TPO survey data, explaining up to 87% of the total variance of county level industrial roundwood production. State level TPO estimates derived using the Landsat-based disturbance products tracked those derived from ground-based survey data closely. The TPO modeling approach developed in this study complements the ground-based TPO surveys conducted by the US Forest Service. It allows derivation of TPO estimates for the years that did not have TPO survey data, and may be applicable in other regions or countries where at least some ground-based survey data on timber production are available for model development and dense time series Landsat observations exist for developing annual forest disturbance products.     

Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information

ABSTRACT

In this geo-statistical analysis of change detection, we illustrate the evolution of the built-up environment in Shanghai at the street-block level. Based on two TerraSAR-X image stacks with 36 and 15 images, covering the city centre of Shanghai for the time period from 2008 to 2015, a set of coherence images was created using a small baseline approach. The road network from Open Street Map, a volunteered geographic information product, serves as the input dataset to create street-blocks. A street-block is surrounded by roads and resembles a ground parcel, a real estate property – a cadastral unit. The coherence information is aggregated to these street-blocks for each observation and the variation is analysed over time. An analysis of spatial autocorrelation reveals clusters of similar behaviours. The result is a detailed map of Shanghai highlighting areas of change. We argue that the aggregation and grouping of synthetic aperture radar coherence image information to real-world entities (street-blocks) is comprehensible and relevant to the urban planning process. Therefore, this research is a contribution to the community of urban planners, designers, and government agencies who want to monitor the development of the urban landscape.     

A hierarchical spatiotemporal adaptive fusion model using one image pair

ABSTRACT

Image fusion techniques that blend multi-sensor characteristics to generate synthetic data with fine resolutions have generated great interest within the remote sensing community. Over the past decade, although many advances have been made in the spatiotemporal fusion models, there still remain several shortcomings in existing methods. In this article, a hierarchical spatiotemporal adaptive fusion model (HSTAFM) is proposed for producing daily synthetic fine-resolution fusions. The suggested model uses only one prior or posterior image pair, especially with the aim being to predict arbitrary temporal changes. The proposed model is implemented in two stages. First, the coarse-resolution image is enhanced through super-resolution based on sparse representation; second, a pre-selection of temporal change is performed. It then adopts a two-level strategy to select similar pixels, and blends multi-sensor features adaptively to generate the final synthetic data. The results of tests using both simulated and actual observed data show that the model can accurately capture both seasonal phenology change and land-cover-type change. Comparisons between HSTAFM and other developed models also demonstrate our proposed model produces consistently lower biases.     

高分辨率遥感影像的深度学习变化检测方法

为提升高分辨率遥感影像的变化检测精度,提出一种利用深度学习的变化检测方法。在预处理的基础上,利用顾及邻域信息的改进变化矢量分析算法和灰度共生矩阵算法获取影像间光谱和纹理变化,并通过设置自适应采样区间提取最可能的变化和未变化区域样本。构建并训练包含标签层的高斯伯努利深度限制玻尔兹曼机模型,以提取变化和未变化区域深层特征,从而有效辨别变化区域。通过WorldView-3与Pléiades-1影像的试验表明本文方法在变化检测精度方面优于对比方法。     

基于遥感影像的土地动态变化监测

土地利用/覆盖变化是目前研究全球及区域环境的一个重要领域,在城镇化加速的今天,城镇的土地利用格局也发生了飞速的变化。本文通过其一研究区内的Landsat TM遥感影像进行处理,获取了2007~2016年10个时相土地利用/覆盖信息,通过不同的预测模型对监测到的数据进行处理及比较,根据相应的最优预测方法预测了2017~2019年南昌市各土地类型的数据,由此研究并探讨了南昌市土地利用/覆盖的时空格局变化。     

丹江口水库面积季节变化遥感监测

基于2014年2月19日、2014年6月7日和2014年10月8日的三景资源三号卫星影像分别作为春季、夏季和秋季的代表,以河南省内的丹江口水库为研究区域,采用面向对象的遥感影像监督分类方法进行影像的分割和分类,提取出遥感影像中的水体,并结合南水北调中线工程对水域面积和周围地物的影响对这3个时期的影像进行比较和分析,得出这3个时期水域面积的变化数据。     

基于ArcEngine的带状图批量制图方法研究

带状图具有折点位置难处理、生成算法复杂等特点,是城市地下管线信息系统建设的难点之一。本文基于ArcEngine组件对象模型,提出了一种通过计算带状图图幅顶点坐标的途径生成带状图的方法,对带状图顶点坐标计算方法及折线端点处理方法进行了重点研究,提出了基于ArcEngine索引图的带状图批量制图输出方法,并且最后通过实验验证了研究方法的可行性。