Assessing Global Forest Land-Use Change by Object-Based Image Analysis

Erik Lindquist,Rémi D’Annunzio

doi:10.3390/rs8080678

Abstract

Consistent estimates of forest land-use and change over time are important for understanding and managing human activities on the Earth’s surface, parameterizing models used for global and regional climate change analyses and a critical component of reporting requirements faced by countries as part of the international effort to Reduce Emissions from Deforestation and Degradation (REDD). In this study, object-based image analysis methods were applied to a global sample of Landsat imagery from years 1990, 2000 and 2005 to produce a land cover classification suitable for expert human review, revision and translation into forest and non-forest land use classes. We describe and analyse here the derivation and application of an automated, multi-date image segmentation, neural network classification method and independent, automated change detection procedure to all sample sites. The automated results were compared against expert human interpretation and found to have an overall agreement of ~76% for a 5-class land cover classification and ~88% agreement for change/no-change assessment. The establishment of a 5 ha minimum mapping unit affected the ability of the segmentation methods to detect small or irregularly-shaped land cover change and, combined with aggregation rules that favour forest, added bias to the automated results. However, the OBIA methods provided an efficient means of processing over 11,000 sample sites, 33,000 Landsat 20 × 20 km sample tiles and more than 6.5 million individual polygons over three epochs and adequately facilitated human expert review, revision and conversion to a global forest land-use product.

Highlights

In an effort to produce a set of spatially consistent and comparable statistics on global tree cover, forest area and change, the United Nations Food and Agriculture Organization (FAO) in collaboration with the Joint Research Centre of the European Commission (JRC) used object-based image analysis (OBIA) techniques and remotely sensed satellite imagery to implement a sample-based survey of theEarth’s land surface called the Global Forest Resource Assessment (FRA) 2010 Remote Sensing Survey.Forest land-use change was estimated at global, regional and ecological domain scales for the time period 1990–2005 [1].OBIA is increasingly used to classify remotely sensed data [2] in a process that includes image segmentation techniques as an integral part of the classification
This paper describes and analyses the OBIA methodology used by the FAO to classify land cover and land use for over 11,000 globally distributed, satellite image-based sample sites
We provide an analysis of the image segmentation and classification and we revisit the problem of scale in satellite image classification [16], namely the selection of a minimum mapping unit, its potential addition of bias to the results and the effectiveness of image segments coerced to a pre-defined minimum mapping unit at detecting land cover changes of different shapes and sizes when using medium spatial resolution imagery

Summary

Introduction

In an effort to produce a set of spatially consistent and comparable statistics on global tree cover, forest area and change, the United Nations Food and Agriculture Organization (FAO) in collaboration with the Joint Research Centre of the European Commission (JRC) used object-based image analysis (OBIA) techniques and remotely sensed satellite imagery to implement a sample-based survey of theEarth’s land surface called the Global Forest Resource Assessment (FRA) 2010 Remote Sensing Survey.Forest land-use change was estimated at global, regional and ecological domain scales for the time period 1990–2005 [1].OBIA is increasingly used to classify remotely sensed data [2] in a process that includes image segmentation techniques as an integral part of the classification. In an effort to produce a set of spatially consistent and comparable statistics on global tree cover, forest area and change, the United Nations Food and Agriculture Organization (FAO) in collaboration with the Joint Research Centre of the European Commission (JRC) used object-based image analysis (OBIA) techniques and remotely sensed satellite imagery to implement a sample-based survey of the. Earth’s land surface called the Global Forest Resource Assessment (FRA) 2010 Remote Sensing Survey. OBIA is increasingly used to classify remotely sensed data [2] in a process that includes image segmentation techniques as an integral part of the classification. Image segmentation is the process of combining the individual picture elements (pixels) of raster data into meaningful objects for identification purposes [3]. Merging pixels of similar spectral and proximal spatial properties together and assigning a common label accomplish this.

Methods

Results

Conclusion