Using CORONA imagery to study land use and land cover change : a review of applications

CORONA spy satellites offer high spatial resolution imagery acquired in the 1960s and early 1970s and declassified in 1995, and they have been used in various scientific fields, such as archaeology, geomorphology, geology, and land change research. The images are panchromatic but contain many details of objects on the land surface due to their high spatial resolution. This systematic review aims to study the use of CORONA imagery in land use and land cover change (LULC) research. Based on a set of queries conducted on the SCOPUS database, we identified and examined 54 research papers using such data in their study of LULC. Our analysis considered case-study area distributions, LULC classes and LULC changes, as well as the methods and types of geospatial data used alongside CORONA data. While the use of CORONA images has increased over time, their potential has not been fully explored due to difficulties in processing CORONA images. In most cases, study areas are small and below 5000 km2 because of the reported drawbacks related to data acquisition frequency, data quality and analysis. While CORONA imagery allows analyzing built-up areas, infrastructure and individual buildings due to its high spatial resolution and initial mission design, in LULC studies, researchers use the data mostly to study forests. In most case studies, CORONA imagery was used to extend the study period into the 1960s, with only some examples of using CORONA alongside older historical data. Our analysis proves that in order to detect LULC changes, CORONA can be compared with various contemporary geospatial data, particularly high and very high-resolution satellite imagery, as well as aerial imagery

Evaluation of Skylab (EREP) data for forest and rangeland surveys

The author has identified the following significant results. Four widely separated sites (near Augusta, Georgia; Lead, South Dakota; Manitou, Colorado; and Redding, California) were selected as typical sites for forest inventory, forest stress, rangeland inventory, and atmospheric and solar measurements, respectively. Results indicated that Skylab S190B color photography is good for classification of Level 1 forest and nonforest land (90 to 95 percent correct) and could be used as a data base for sampling by small and medium scale photography using regression techniques. The accuracy of Level 2 forest and nonforest classes, however, varied from fair to poor. Results of plant community classification tests indicate that both visual and microdensitometric techniques can separate deciduous, conifirous, and grassland classes to the region level in the Ecoclass hierarchical classification system. There was no consistency in classifying tree categories at the series level by visual photointerpretation. The relationship between ground measurements and large scale photo measurements of foliar cover had a correlation coefficient of greater than 0.75. Some of the relationships, however, were site dependent

Airphoto use in resource management - A survey of non-federal purchasers of agricultural stabilization and conservation service airphotos

Survey and analysis of nonfederal users of spacecraft aerial photographs sold by US Department of Agricultur

INTEGRATING MULTI-SOURCE DATA TO QUANTIFY CHANGES IN BIOMASS AND SOIL ORGANIC CARBON DUE TO LAND-USE CHANGE IN THE BOREAL PLAINS ECOZONE, CANADA

Land use and cover change (LUCCs) is the second largest source of global carbon emission and there has been a growing interest in LUCCs to mitigate climate change effects. Global land-use change associated with cropland expansion, which is a major carbon source, was dominant in the last century. Abandoned cropland typically is a carbon sink and was observed in many regions in the recent decades. However, there has been little research on carbon balance resulting from LUCCs in agricultural landscapes, especially under abandoned cropland in Canada. Information on carbon balance resulting from LUCCs is necessary for national greenhouse gas (GHG) inventories as well as emission mitigation options. The primary objective of the study is to quantify carbon stocks and dynamics as consequences of LUCCs in the Boreal Plains Ecozone, Canada. Field measurement on carbon stocks in abandoned cropland was assessed at field sites in Saskatchewan. Vegetation C ranged from 7.6 to 90.1 Mg C ha-1 and increased linearly with stand age. Ecosystem C increased from 74.2 to 137.6 Mg C ha-1 after 41 years of abandonment (or net C sink of 1.9 Mg C ha-1 yr-1). In the agriculture region of the Boreal Plains Ecozone, land-use change accounted for 6.5% of the total area during the 1990-2000 period. Forest to cropland conversion was dominant on well-drained Chernozemic and Luvisolic soil orders. Abandoned cropland occurred mainly on poorly drained and acidic parent materials. LUCCs in agriculture region was estimated to be a net C sink of 0.76 ±0.3 Mg C ha-1 yr-1 during this period. In the agriculture-forest transition region of the Boreal Plains Ecozone, substantial land-use changes occurred in pasture (+76%) and summer fallow (-87.8%) over a 27-year period (1984 - 2011). The shrub and forest area was reduced -31.6% and -16.4%, respectively. Forest disturbances occurred mainly during 2005 – 2011. Substantial changes of summer fallow to annual cropland took place on the higher soil capability land and annual cropland to pasture conversion was more likely on lower capability soil classes. We estimated that LUCCs in the region was a net C source of approximately 552.7 Gg C across the research period or 0.07 Mg C ha-1 yr-1

An integrated study of earth resources in the State of California based on ERTS-1 and supporting aircraft data, volume 1

There are no author-identified significant results in this report

Improving land use change tracking in the UK Greenhouse Gas Inventory: final outputs report

This report describes work on the project “Improving Land Use Change Tracking in the UK Greenhouse Gas Inventory” for the Department for Business, Energy & Industrial Strategy (reference TRN 2384/05/2020). The aim of the project was to make improved estimates of land-use change in the UK, using multiple sources of data. We applied a method for estimating land-use change using a Bayesian data assimilation approach. This allows us to constrain estimates of gross land-use change with national-scale census data, whilst retaining the detailed information available from several other sources. We produced a time series of maps describing our best estimate of land-use change given the available data, as well as the full posterior distribution of this space-time data cube. This quantifies the joint probability distribution of the parameters, and properly propagates the uncertainty from input data to final output. The output data has been summarised in the form of land-use vectors. The results show that we can provide improved estimates of past land-use change using this method. The main advantage of the approach is that it provides a coherent, generalised framework for combining multiple disparate sources of data, and adding further sources of data in future would be straightforward. Future work could focus on more detailed analysis of existing data sets, introducing independent constraints where possible, and obtaining further relevant data sets. The code is available via GitHub

Agricultural land abandonment in Bulgaria: a long-term remote sensing perspective, 1950–1980

Agricultural land abandonment is a globally significant threat to the sustenance of economic, ecological, and social balance. Although the driving forces behind it can be multifold and versatile, rural depopulation and urbanization are significant contributors to agricultural land abandonment. In our chosen case study, focusing on two locations, Ruen and Stamboliyski, within the Plovdiv region of Bulgaria, we use aerial photographs and satellite imagery dating from the 1950s until 1980, in connection with official population census data, to assess the magnitude of agricultural abandonment for the first time from a remote sensing perspective. We use multi-modal data obtained from historical aerial and satellite images to accurately identify Land Use Land Cover changes. We suggest using the rubber sheeting method for the geometric correction of multi-modal data obtained from aerial photos and Key Hole missions. Our approach helps with precise sub-pixel alignment of related datasets. We implemented an iterative object-based classification approach to accurately map LULC distribution and quantify spatio-temporal changes from historical panchromatic images, which could be applied to similar images of different geographical regions

Utilization of EREP data in geological evaluation, regional planning, forest management, and water management in North Carolina

The author has identified the following significant results. The S190A, S190B, and S192 photographs and imagery were studied, using standard air-photo interpretation techniques supplemented by color additive viewing and density slicing. The EREP data were found to have potential usefulness for natural resource inventory work, water quality monitoring, and land use mapping for specific problems at scales up to 1:30,000. Distinctions between forest types in North Carolina are limited to conifers, mixed conifer-hardwoods, and hardwoods. Geologic interpretation was limited to detection of lineaments; lithologic differentiation and soil group mapping have proved infeasible in North Carolina except for differentiation of wetland soils in the coastal plain. Imagery from the S192 multispectral scanner has proved to be capable of useful discriminations for vegetation and crop analysis

Monitoring earth resources from aircraft and spacecraft

Procedures and techniques for determining earth resources from high altitude aircraft and Apollo 9 photograph