Accuracy of remotely sensed data: Sampling and analysis procedures

A review and update of the discrete multivariate analysis techniques used for accuracy assessment is given. A listing of the computer program written to implement these techniques is given. New work on evaluating accuracy assessment using Monte Carlo simulation with different sampling schemes is given. The results of matrices from the mapping effort of the San Juan National Forest is given. A method for estimating the sample size requirements for implementing the accuracy assessment procedures is given. A proposed method for determining the reliability of change detection between two maps of the same area produced at different times is given

Nationwide forestry applications program. Analysis of forest classification accuracy

The development of LANDSAT classification accuracy assessment techniques, and of a computerized system for assessing wildlife habitat from land cover maps are considered. A literature review on accuracy assessment techniques and an explanation for the techniques development under both projects are included along with listings of the computer programs. The presentations and discussions at the National Working Conference on LANDSAT Classification Accuracy are summarized. Two symposium papers which were published on the results of this project are appended

Modelling Associations between Public Understanding, Engagement and Forest Conditions in the Inland Northwest, USA.

Abstract Opinions about public lands and the actions of private non-industrial forest owners in the western United States play important roles in forested landscape management as both public and private forests face increasing risks from large wildfires, pests and disease. This work presents the responses from two surveys, a random-sample telephone survey of more than 1500 residents and a mail survey targeting owners of parcels with 10 or more acres of forest. These surveys were conducted in three counties (Wallowa, Union, and Baker) in northeast Oregon, USA. We analyze these survey data using structural equation models in order to assess how individual characteristics and understanding of forest management issues affect perceptions about forest conditions and risks associated with declining forest health on public lands. We test whether forest understanding is informed by background, beliefs, and experiences, and whether as an intervening variable it is associated with views about forest conditions on publicly managed forests. Individual background characteristics such as age, gender and county of residence have significant direct or indirect effects on our measurement of understanding. Controlling for background factors, we found that forest owners with higher self-assessed understanding, and more education about forest management, tend to hold more pessimistic views about forest conditions. Based on our results we argue that self-assessed understanding, interest in learning, and willingness to engage in extension activities together have leverage to affect perceptions about the risks posed by declining forest conditions on public lands, influence land owner actions, and affect support for public policies. These results also have broader implications for management of forested landscapes on public and private lands amidst changing demographics in rural communities across the Inland Northwest where migration may significantly alter the composition of forest owner goals, understanding, and support for various management actions

Modeling associations between public understanding, engagement and forest conditions in theInland Northwest, USA

Opinions about public lands and the actions of private non-industrial forest owners in the western United States play important roles in forested landscape management as both public and private forests face increasing risks from large wildfires, pests and disease. This work presents the responses from two surveys, a random-sample telephone survey of more than 1500 residents and a mail survey targeting owners of parcels with 10 or more acres of forest. These surveys were conducted in three counties (Wallowa, Union, and Baker) in northeast Oregon, USA. We analyze these survey data using structural equation models in order to assess how individual characteristics and understanding of forest management issues affect perceptions about forest conditions and risks associated with declining forest health on public lands. We test whether forest understanding is informed by background, beliefs, and experiences, and whether as an intervening variable it is associated with views about forest conditions on publicly managed forests. Individual background characteristics such as age, gender and county of residence have significant direct or indirect effects on our measurement of understanding. Controlling for background factors, we found that forest owners with higher self-assessed understanding, and more education about forest management, tend to hold more pessimistic views about forest conditions. Based on our results we argue that self-assessed understanding, interest in learning, and willingness to engage in extension activities together have leverage to affect perceptions about the risks posed by declining forest conditions on public lands, influence land owner actions, and affect support for public policies. These results also have broader implications for management of forested landscapes on public and private lands amidst changing demographics in rural communities across the Inland Northwest where migration may significantly alter the composition of forest owner goals, understanding, and support for various management actions

Update and review of accuracy assessment techniques for remotely sensed data

Research performed in the accuracy assessment of remotely sensed data is updated and reviewed. The use of discrete multivariate analysis techniques for the assessment of error matrices, the use of computer simulation for assessing various sampling strategies, and an investigation of spatial autocorrelation techniques are examined

Forest management and wildfire risk in inland northwest

This brief reports the results of a mail survey of forest landowners in northeastern Oregon conducted in the fall of 2012 by the Communities and Forests in Oregon (CAFOR) Project at the University of Colorado and the University of New Hampshire in cooperation with Oregon State University College of Forestry Extension. The mail survey--a follow-up to a telephone survey conducted for the counties of Baker, Union, and Wallowa in the fall of 2011 -was administered to understand who constituted forest landowners in these three coun¬ties and their perceptions about forest management on both public and private land, as well as risks to forests in the area and the actions they have taken to reduce those risks. The respondents indicated that they perceive wildfire as the greatest threat to their lands, and they consider cooperation with neighbors as very or extremely important for land management. Forest landowners believe public lands are managed poorly and see a greater risk of wildfire occurring on neighboring public land than on their own land. Their opinions on land management are not strongly related to background factors or ideology (for example, gender, age, political party, wealth) but may be heavily influenced by personal experience with wildfire

CropRef: Reference Datasets and techniques to improve global cropland mapping

Global timely, accurate, and cost-effective cropland mapping is a prerequisite for agriculture monitoring and application. Recently, the world’s first global 30-m cropland product was produced through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) supported global food security-support analysis data (GFSAD) project (https://croplands.org/app/map). However, on average, in over 50 discrete segments of the world errors of omissions and commissions of GFSAD cropland extent product was around 20%. One of the major reasons for these errors is due to lack of sufficient, spatially well distributed in-situ data for the development of these products. To address this issue, we built a web application (CropRef) to help collect crowdsourced geoTagged cropland samples (https://croplands.org/app/data/search). The system allows users to interactively query and browse the geo-referenced statistical data in the form of maps and to subsequently download them for the regions of interest from any place in the world. Our system (CropRef) also integrates online and mobile applications, very high spatial resolution satellite imagery (sub-meter to 5-m) available from Google Earth, as well as various forms of data collected through crowdsourcing as a mechanism for validating and improving globally relevant spatial information on agriculture. Through its growing network of volunteers and a number of successful data collection campaigns, over 100,000 samples of croplands versus non-croplands have been collected around the globe. This paper provides an overview of the main features of CropRef, and then using a series of examples, illustrates how the crowdsourced data collected through CropRef have been used to improve information on knowledge extraction and consequential global cropland mapping. Validating land-cover maps at the global scale is a significant challenge. We also built a global reference dataset for validating 30 m-resolution global land-cover maps in the GFSAD30 project. The dataset has been carefully improved through several rounds of interpretation and verification by different image interpreters, and checked by an expert quality controller. Certainty in interpretation was measured by majority of interpreters agreeing on a class that is also accepted by expert quality controller. The tool and dataset are located at croplands.org

Mapping Croplands of Southeast Asia, Japan, and North and South Korea using Landsat 30-m time-series, random forest algorithm

Southeast Asia (e.g. Myanmar, Thailand, Vietnam, Indonesia), Japan, and North and South Korea, 17 countries in total, have a population of 846 million people, which is about 9% of the world’s population; it is predicted to increase to 1 billion by 2050. This population expansion will coincide with a reduction in arable land due to an increase in urban and industrial development, increasing precipitation variability, and sea level rise. Additionally, these 17 countries are leading exporters of rice, sugar, shrimp, cassava, oil palm, pulses & beans, cocoa & coffee, tropical fruit, and spices. To help address the future food demand, in support of the Global Food Security-Support Analysis Data (GFSAD) project, this study mapped a wall to wall 30-m cropland product for the nominal year 2015 at 30-m resolution using 10 band cloud free composites derived from, Landsat-7&8 data from 2013-2016. The study adopted random forest (RF) machine learning algorithm and generated croplands versus non-croplands knowledge using several thousand training samples derived from sub-meter to 5-m very high spatial resolution imagery. The RF algorithm was run separately in seven distinct zones based on political divisions, agro-climatology, and elevation to ensure knowledge base that can distinctly separate croplands from non-croplands. All computing was performed on Google Earth Engine (GEE) cloud platform. Accuracies (overall, producer’s and user’s) accuracies of the croplands exceeded 80% as determined by an independent accuracy assessment team. Overall croplands areas of 17 countries was 128 Mha compared with UN FAO reported cropland areas of 137 Mha