Variability in the effects of disturbances and extreme climate events can lead to changes in tree cover over time, including partial or complete loss, with diverse ecological consequences. It is therefore critical to identify in space and time the change processes that lead to tree cover change. Studies of change are often hampered by the lack of data capable of consistently detecting different types of change. Using the Landsat satellite record to create a long time-series of land cover and land cover change, the U.S. Geological Survey Land Change Monitoring Assessment and Projection (LCMAP) project has made an annual time series of land cover across the conterminous United States for the period 1985 to 2018. Multiple LCMAP products analyzed together with map validation reference plots provide a robust basis for understanding tree cover change. In LCMAP (Collection 1.2), annual change detection is based on harmonic model breaks calculated at each Landsat pixel from the Continuous Change Detection and Classification (CCDC) algorithm. The results showed that the majority of CCDC harmonic model breaks (signifying change) indicated partial tree cover loss (associated with management practices such as tree cover thinning) as compared to complete tree cover loss (associated with practices like clearcut harvest or fire disturbance). Substantially fewer occurrences of complete tree cover loss were associated with change in land cover state. The area of annual tree cover change increased after the late 1990s and stayed high for the rest of the study period. The reference data showed that tree harvest dominated across the conterminous United States. The majority of tree cover change occurred in evergreen forests. Large estimates of disturbance-related tree cover change indicated that tree cover loss may have previously been underreported due to omission of partial tree cover loss in prior studies. This has considerable implications for forest carbon accounting along with tracking ecosystem goods and services.

The U.S. State of California has experienced frequent drought events, hotter temperatures and other disruptions to the climate system whose effects on ecosystems have been widely reported in recent decades. Studies primarily confined to specific vegetation communities or species, individual drought incidents, or analysis over a relatively short intervals, has limited our understanding of the broad-scale effects on tree cover and the spatiotemporal variability of effects across broader regions. We focused analysis on multi-annual land cover and land surface change to assess patterns and trends in tree cover loss in tree-dominated Californian ecoregions from 1986 to 2019. The top three years of total tree cover loss for the state were 2018 (1901 km(2)), 2015 (1556 km(2)), and 2008 (1549 km(2)). Overall, annual tree cover loss had upward trends. Tree cover loss rapidly surged later in the study period and was apparently driven by climate stress and wildfires. Underlying geographic variability was apparent in both non-fire and fire-related tree cover loss that sharply increased during hotter multi-year droughts. The increasingly hotter and drier climate conditions were associated with significant increases in fire-induced mortality. Our findings indicate that a possible effect of future hotter and drier climate would lead to further tree cover loss, thereby endangering California's ecosystem goods and services. Geographic variability in tree cover trends indicates that ecoregion-specific mitigation and adaptation strategies would be useful to conserve the region's forest resources. Such strategies may benefit from consideration of historical disturbances, ecoregion's sensitivity to disturbance types, as well as potential ecoregion-specific climate-vegetation-fire feedbacks.

An assessment of annual land cover on publicly and privately managed lands across the conterminous United States (CONUS) from 1985–2018 was performed, including land cover conversions within their management category, to inform future policy and land-use decision-making in natural resource management. Synthesizing land cover data with land management delineations aids our ability to address effects of land management decisions by public or private entities. The U.S. Geological Survey (USGS) Protected Areas Database of the United States (PAD-US) version 2.1 data delineate land management categories and enable examination of land cover composition and change using the USGS Land Change Monitoring, Assessment, and Projection (LCMAP) reference data. Average composition of our delineated CONUS results using LCMAP land cover classes is 40% Grass/Shrub (GS), 29% Tree Cover (TC), 18% Cropland (CP), 5% Developed (DV), 5% Wetland (WL), 1.8% Water (WR), and 0.9% Barren (BN). Private (public) land is composed of 35% (52%) GS, 27% (36%) TC, 25% (1%) CP, 7% (1%) DV, 5% (5%) WL, 2% (2%) WR, and less than 1% (3%) BN. Land cover change averaged less than 1% per year. The largest net percentage gains across CONUS were in DV land and GS, and the greatest net losses were in CP and TC. Approximately 73% of CONUS is private land and, thus, land cover change across CONUS is largely a reflection of private land change dynamics. Private compositional changes show net gains from 1985–2018 in DV (2.3%), WR (0.2%), and GS (0.1%) classes, while net losses occurred in CP (−1.9%), TC (−0.6%), WL (−0.1%), and BN (−0.01%). Public land cover changes show net gains in GS (1%), DV (0.2%), WR (0.01%), WL (0.05%), and BN (0.1%) classes, and net losses in CP (−0.3%) and TC (−1%). Our study reveals connections between land cover conversion and various policy and socioeconomic decisions through time.

Sample-based estimates augmented by complete coverage land-cover maps were used to estimate area and describe patterns of annual land-cover change across the conterminous United States (CONUS) between 1985 and 2016. Most of the CONUS land cover remained stable in terms of net class change over this time, but a substantial gross change dynamic was captured by the annual and cumulative time intervals. The dominant types of changes can be grouped into natural resource cycles, increases in urbanization, and surface-water dynamics. The annual estimates over the 30-year time series showed a reduction in the rate of urban expansion after 2006, new growth in cropland after 2007, but a net overall decline in cropland since 1985, and two eras of net tree cover loss, the first one early in the time series and the second starting in 2012. Our study provides a holistic assessment of the CONUS land-cover conversion (class) change and can serve as a new benchmark for future research.

Urban development and associated land cover and land use change alter the thermal, hydrological, and physical properties of the land surface. Assessments of surface urban heat island (UHI) usually focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain period. However, the mechanisms and complex interactions in landscape dynamics and land surface thermal features are still being assessed. In this study, we developed and implemented a novel approach to characterize landscape thermal conditions by focusing on UHI intensity and its spatiotemporal variation using the recently available time series of Landsat land surface temperature and land cover change products. We analyzed land surface temperature changes in urban and surrounding non-urban lands to quantify the UHI intensity and landscape thermal conditions in the Atlanta and Minneapolis metropolitan areas of the United States. Our results revealed that UHI intensities had averages of 3.4 degrees C and 3.3 degrees C in the Atlanta and Minneapolis metropolitan areas, respectively. The dominant land cover type in rural areas and urban imperviousness cover determines the UHI intensity. Increasing trends of 0.04 degrees C/year and 0.01 degrees C/year in UHI intensity between 1985 and 2018 were found in Atlanta and Minneapolis, respectively. The UHI intensity variations in 1985 and 2018 suggest that the magnitudes and temporal variations of UHI intensity averaged from all urban land cover classes are close to the UHI intensity estimated from the low intensity urban area only while the UHI intensities are more than 2 degrees C larger in medium to high and high intensity urban areas. The UHI intensities estimated from the maximum temperature that have statistically significant increasing trends suggest that the maximum temperature is a good element for measuring UHI effect. Urban land cover dynamics play an important role in controlling temporal variation of UHI and the UHI hotspots. Our findings support the scientific value of implementing the prototype approach as an objective framework to quantify and monitor UHI intensity at a large geographic extent.

Forest covers about one-third of the land area of the conterminous United States (CONUS) and plays an important role in offsetting carbon emissions and supporting local economies. Growing interest in forests as relatively cost-effective nature-based climate solutions, particularly restoration and reforestation activities has increased the demand for information on forest regrowth and recovery following natural and anthropogenic disturbances (e.g. fire, harvest, or thinning). However, a wall-to-wall mapping of the CONUS tree regrowth duration at an annual time interval and 30-m resolution is still challenging. In this study, we utilized the annual land cover products to develop a dataset to quantify forest regrowth duration for CONUS over 1985–2017. The land cover data used to derive the tree regrowth duration map is from the primary land cover product in the U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) collection. The LCMAP product used all available Landsat images to detect disturbances over forest and classify Grass/Shrub to Tree Cover transitions on an annual basis. The average regrowth duration was then calculated for each pixel. The regrowth duration map was validated using human-interpreted annual reference data that were collected independently. The validation results show 1 year of underestimation and 6-year standard deviation of error between the reference data and the regrowth duration map. In southeastern CONUS, where major tree regrowth activities have been observed, our map showed higher accuracy with less than one-year bias and 3.6 years standard deviation of error. Forest in the southeast took around 5 years to recover, which was faster than other regions of CONUS. Many pixels had multiple disturbances during the 33-year study period in the region. The spatial pattern of the tree regrowth indicated intense harvesting activities in this region. The Pacific Northwest coast region was the second main area of tree regrowth, but this region often took multiple decades to recover. Given the increasing interest in forests as nature-based climate solutions, the tree regrowth duration map can be used to assess reforestation activities as well as forest recovery following natural disturbance and harvesting.

The increasing availability of high-quality remote sensing data and advanced technologies has spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985–2017 at a 30 m resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover products and five products related to land surface change. The LCMAP land cover products were validated using a collection of ∼25 000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5 %. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and shared Mesos cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30 m annual land change datasets that include land cover, land cover change, and spectral change spanning from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future. The LCMAP products introduced in this paper are freely available at https://doi.org/10.5066/P9W1TO6E (LCMAP, 2021).

The increasing availability of high-quality remote sensing data and advanced technologies has spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985–2017 at a 30 m resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover products and five products related to land surface change. The LCMAP land cover products were validated using a collection of ∼25 000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5 %. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and shared Mesos cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30 m annual land change datasets that include land cover, land cover change, and spectral change spanning from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future. The LCMAP products introduced in this paper are freely available at https://doi.org/10.5066/P9W1TO6E (LCMAP, 2021).

Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal data, including concepts, datasets, methodologies, and applications. We focus on reviewing progress on multi-sensor image selection, preprocessing, computing, gap filling, image fusion, deep learning, and developing new metrics. This literature review shows that new satellite sensors and valuable methods have been developed for calculating land surface temperature (LST) and UHI intensity, and for assessing UHIRIP. Additionally, some of the limitations of using remotely sensed data to analyze the LST, UHI, and UHI intensity are discussed. Finally, we review a variety of applications in UHI and UHIRIP analyses. The assimilation of time-series remotely sensed data with the application of data fusion, gap filling models, and deep learning using the Google Cloud platform and Google Earth Engine platform also has the potential to improve the estimation accuracy of change patterns of UHI and UHIRIP over long time periods.

Assessments of surface urban heat island (UHI) have focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain time period by including land cover information. In this study, we implemented a prototype approach to characterize the spatiotemporal variations of UHI using time series of Landsat land surface temperature products and annual land change information. We analyzed UHI distribution and change in Sioux Falls, South Dakota, in the north-central United States and found that the mean UHI intensity in the region was as large as 2.2°C during the period 1986–2017 with an increasing trend of 0.02°C per year within the area with a 5-km non-urban extent. The UHI intensity associated with high intensity urban land cover usually is stronger than with low intensity urban land cover. We evaluated the impact of different non-urban reference extents on UHI variation using different non-urban buffers. The result also suggests that the overall temporal trends of UHI intensity are almost the same when using a 5-km or 10-km non-urban buffer surrounding the urban core. The prototype approach provides a framework to consistently quantify UHI and monitor its change to a large geographic extent.

California has, in recent years, become a hotspot of interannual climatic variability, recording devastating climate-related disturbances with severe effects on tree resources. Understanding the patterns of tree cover change associated with these events is vital for developing strategies to sustain critical habitats of endemic and threatened vegetation communities. We assessed patterns of tree cover change, especially the effects of the 2012–2016 drought within the distribution range of blue oak (Quercus douglasii), an endemic tree species to California with a narrow geographic extent. We utilized multiple, annual land-cover and land-surface change products from the U.S. Geological Survey (USGS) Land Change Monitoring, Assessment and Projection (LCMAP) project along with climate and wildfire datasets to monitor changes in tree cover state and condition and examine their relationships with interannual climate variability between 1985 and 2016. Here, we refer to a change in tree cover class without a land-cover change to another class as “conditional change.” The unusual drought of 2012–2016, accompanied by anomalously high temperatures and vapor pressure deficit, was associated with exceptional spikes in the amount of both fire and non-fire induced tree cover loss and tree cover conditional change, especially in 2015 and 2016. Approximately 1,266 km2 of tree cover loss and 617 km2 of tree cover conditional change were recorded during that drought. Tree cover loss through medium to high severity fires was especially large in exceptionally dry and hot years. Our study demonstrates the usefulness of the LCMAP products for monitoring the effects of climatic extremes and disturbance events on both thematic and conditional land-cover change over a multi-decadal period. Our results signify that blue oak woodlands may be vulnerable to extreme climate events and changing wildfire regimes. Here, we present early evidence that frequent droughts associated with climate warming may continue to affect tree cover in this region, while drought interaction with wildfires and the resulting feedbacks may have substantial influence as well. Consequently, efforts to conserve the blue oak woodlands, and potentially other vegetation communities in the Western United States, may benefit from consideration of climate risks as well as the potential for climate-fire and vegetation feedbacks.

First posted April 15, 2020 For additional information, contact: Customer Service, Earth Resources Observation and Science Center (EROS)U.S. Geological Survey47914 252d Street Sioux Falls, SD 57198https://www.usgs.gov/land-resources/eros/lcmap There is a pressing need to monitor and understand the rapid land change happening around the world. The U.S. Geological Survey is developing a new capability, called Land Change Monitoring, Assessment, and Projection (LCMAP), to innovate the understanding of land change. This capability is the Earth Resources Observation and Science Center's foundation for an integrated U.S. Geological Survey-wide land change science framework. LCMAP supports the development of consistent data and land cover products spanning large geographic extents, over extended periods, and at a higher frequency than in the past. LCMAP provides solutions to the science and management communities’ growing need for an improved understanding of the fundamental drivers of land change, the consequences of change in human and natural systems, and feedbacks associated with land change processes.

The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov), includes Landsat-based, 30 m resolution products over the conterminous (CONUS) United States (U.S.) for land cover, urban imperviousness, and tree, shrub, herbaceous and bare ground fractional percentages. The release of NLCD 2016 provides important new information on land change patterns across CONUS from 2001 to 2016. For land cover, seven epochs were concurrently generated for years 2001, 2004, 2006, 2008, 2011, 2013, and 2016. Products reveal that land cover change is significant across most land cover classes and time periods. The land cover product was validated using existing reference data from the legacy NLCD 2011 accuracy assessment, applied to the 2011 epoch of the NLCD 2016 product line. The legacy and new NLCD 2011 overall accuracies were 82% and 83%, respectively, (standard error (SE) was 0.5%), demonstrating a small but significant increase in overall accuracy. Between 2001 and 2016, the CONUS landscape experienced significant change, with almost 8% of the landscape having experienced a land cover change at least once during this period. Nearly 50% of that change involves forest, driven by change agents of harvest, fire, disease and pests that resulted in an overall forest decline, including increasing fragmentation and loss of interior forest. Agricultural change represented 15.9% of the change, with total agricultural spatial extent showing only a slight increase of 4778 km(2), however there was a substantial decline (7.94%) in pasture/hay during this time, transitioning mostly to cultivated crop. Water and wetland change comprised 15.2% of change and represent highly dynamic land cover classes from epoch to epoch, heavily influenced by precipitation. Grass and shrub change comprise 14.5% of the total change, with most change resulting from fire. Developed change was the most persistent and permanent land change increase adding almost 29,000 km(2) over 15 years (5.6% of total CONUS change), with southern states exhibiting expansion much faster than most of the northern states. Temporal rates of developed change increased in 2001-2006 at twice the rate of 2011-2016, reflecting a slowdown in CONUS economic activity. Future NLCD plans include increasing monitoring frequency, reducing latency time between satellite imaging and product delivery, improving accuracy and expanding the variety of products available in an integrated database.

The causes of land change from 2001 through 2011 for the Interior Highlands region of the south-central United States were assessed using satellite imagery, historical land-use and land-cover data, and digital orthophotos. The study was designed to develop improved regional land-use and land-cover change information, including identification of the proximate causes of change. The four leading causes of land change involved various stages of forest change: harvest (376,497 hectares), reforestation (105,150 hectares), stand loss to fire (98,875 hectares), and thinning (54,029 hectares). The study provides baseline spatial data for understanding human and ecological dynamics in the region. The spatial data, including metadata, are available in the data release associated with this report at https://doi.org/ 10.5066/ P9W4SF05. Introduction The causes of land change from 2001 through 2011 in the Interior Highlands region of the south-central United States were assessed using satellite imagery, historical land-use and land-cover data, and digital orthophotos. The assessment developed estimates of the extent, type, location, direction, and proximate cause of changes in the following four ecological regions (ecoregions) of the Interior Highlands: Ozark Highlands, Boston Mountains, Arkansas Valley, and Ouachita Mountains (fig. 1). The study provides baseline spatial data for understanding human and ecological dynamics in the region. 1U.S. Geological Survey, Fort Collins, CO 80526 2U.S. Geological Survey, Denver, CO 80225 3U.S. Geological Survey, Bozeman, MT 59715 4U.S. Geological Survey, Sioux Falls, SD 57198 5Stinger Ghaffarian Technologies, Inc., under contract to the U.S. Geological Survey. The low mountains and plateaus of the Interior Highlands extend across parts of Missouri, Kansas, Oklahoma, and Arkansas, covering approximately 17.5 million hectares. Vegetation cover is primarily temperate broadleaf forest, including oak (Quercus spp.) and hickory (Carya Nutt. spp.), mixed with pine (for example, shortleaf pine, Pinus echinate Mill.) and areas of eastern red-cedar (Juniperus virginiana L.) (U.S. Department of Agriculture, Forest Service, 1999; Chastain and others, 2006). Grassland glades are a major element of the biological diversity of the region, but they have been invaded by trees and shrubs, including the eastern red-cedar, owing largely to historical fire suppression and human disturbance (Chastain and others, 2006; Miller and others, 2017). The dominant historical changes to forest cover were timber harvest, reforestation, and land clearance for pasture and cropland (Karstensen, 2009, 2010; Auch and Karstensen, 2015). Examination of the Interior Highlands causes of land change data for 2001 through 2011 for the Ozark Highlands and Arkansas Valley ecoregions show substantial agricultural land use, whereas the Ouachita Mountains ecoregion has a large amount of industrial silviculture and timber harvest activity. Methods The study was designed to develop enhanced regional land-use and land-cover change information with an emphasis on identifying the proximate causes of change. A proximate cause is defined as a specific local action that is the explanation for a change in land use or land cover. The approach involved aggregating numerous historical spatial data sources to assess the amount and cause of change (Drummond and others, 2019). During the analysis, areas of change were validated and attributed with a land-use classification using the available spatial data supplemented with manual interpretation of aerial photos when necessary. The type and extent of change were considered validated if corroborated with two or more datasets. All other potential changes without corroboration were verified using manual interpretation techniques, primarily by comparing the area of prospective change to high-resolution orthoimagery to 2 Causes of Land Change in the U.S. Interior Highlands, 2001–2011 Rolla Kansas City Columbia Saint Louis Saint Joseph

While there are currently a number of irrigated land datasets available for the western United States (U.S.), there is uncertainty regarding in how they relate to each other. To help understand the characteristics of available irrigated datasets, we compared (1) the Cropland Data Layer (CDL), (2) Moderate Resolution Imaging Spectroradiometer Irrigated Agriculture Dataset (IAD), (3) Digitized Irrigated Land (DIL), and (4) Consumptive Use for Irrigation (CUI) data in Arizona and Colorado, U.S. These datasets were derived from multiple sources at various spatial resolutions and temporal scales. We found spatial and temporal trends among all of them. The datasets showed decreases in irrigated land area in Arizona during the 2000–2010 time period. The change ranges and ratios were similar in all Arizona datasets. Irrigated land in Colorado decreased in DIL and CUI but increased in IAD and CDL. The agreement within the same type of dataset during different time periods was from 60% to 80% ( R 2 from 0.35 to 0.72) in Arizona and from 50% to 80% ( R 2 from 0.23 to 0.68) in Colorado. DIL had the highest agreement (80%) in both states. The agreement among different datasets acquired at approximately the same time frame ranged from 51% to 63% ( R 2 from 0.14 to 0.31) in Arizona and from 47% to 69% ( R 2 from 0.32 to 0.40) in Colorado. The results from this study support a greater understanding of the multiresolution and multitemporal nature of these datasets for various applications.

Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely information for land managers. To meet these needs, the U.S. Geological Survey has implemented a capability to monitor land surface change called the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. This paper describes the methodological foundations and lessons learned during development and testing of the LCMAP approach. Testing and evaluation of a suite of 10 annual land cover and land surface change data sets over six diverse study areas across the United States revealed good agreement with other published maps (overall agreement ranged from 73% to 87%) as well as several challenges that needed to be addressed to meet the goals of robust, repeatable, and geographically consistent monitoring results from the Continuous Change Detection and Classification (CCDC) algorithm. First, the high spatial and temporal variability of observational frequency led to differences in the number of changes identified, so CCDC was modified such that change detection is dependent on observational frequency. Second, the CCDC classification methodology was modified to improve its ability to characterize gradual land surface changes. Third, modifications were made to the classification element of CCDC to improve the representativeness of training data, which necessitated replacing the random forest algorithm with a boosted decision tree. Following these modifications, assessment of prototype Version 1 LCMAP results showed improvements in overall agreement (ranging from 85% to 90%).

Grassland to cropland conversion in the northern prairie of the United States has been a topic of recent land use change studies. Within this.' region more corn and soybeans are grown now (2017) than in the past but most studies to date have not examined multi-decadal trends and the synergistic web of socio-ecological driving forces involved, opting instead for short-term analyses and easily targeted agents of change This paper examines the coalescing of biophysical and socioeconomic driving forces that have brought change to the agricultural landscape of this region between 1980 and 2013. While land conversion has occurred, most of the region's cropland in 2013 had been previously cropped by the early 1980s. Furthermore, the agricultural conditions in which crops were grown during those three decades have changed considerably because of non-biophysical alterations to production practices and changing agricultural markets. Findings revealed that human drivers played more of a role in crop change than biophysical changes, that blending quantitative and qualitative methods to tell a more complete story of crop change in this region was difficult because of the synergistic characteristics of the drivers involved, and that more research is needed to understand how farmers make crop choice decisions.