scholarly journals Exploring the Regional Dynamics of U.S. Irrigated Agriculture from 2002 to 2017

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 394
Author(s):  
Dinesh Shrestha ◽  
Jesslyn F. Brown ◽  
Trenton D. Benedict ◽  
Daniel M. Howard
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Crop Yields ◽  
The United States ◽  
Economic Incentives ◽  
Irrigated Agriculture ◽  
Periodic Patterns ◽  
The U.S

The United States has a geographically mature and stable land use and land cover system including land used as irrigated cropland; however, changes in irrigation land use frequently occur related to various drivers. We applied a consistent methodology at a 250 m spatial resolution across the lower 48 states to map and estimate irrigation dynamics for four map eras (2002, 2007, 2012, and 2017) and over four 5-year mapping intervals. The resulting geospatial maps (called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset or MIrAD-US) involved inputs from county-level irrigated statistics from the U.S. Department of Agriculture, National Agricultural Statistics Service, agricultural land cover from the U.S. Geological Survey National Land Cover Database, and an annual peak vegetation index derived from expedited MODIS satellite imagery. This study investigated regional and periodic patterns in the amount of change in irrigated agriculture and linked gains and losses to proximal causes and consequences. While there was a 7% overall increase in irrigated area from 2002 to 2017, we found surprising variability by region and by 5-year map interval. Irrigation land use dynamics affect the environment, water use, and crop yields. Regionally, we found that the watersheds with the largest irrigation gains (based on percent of area) included the Missouri, Upper Mississippi, and Lower Mississippi watersheds. Conversely, the California and the Texas–Gulf watersheds experienced fairly consistent irrigation losses during these mapping intervals. Various drivers for irrigation dynamics included regional climate fluctuations and drought events, demand for certain crops, government land or water policies, and economic incentives like crop pricing and land values. The MIrAD-US (Version 4) was assessed for accuracy using a variety of existing regionally based reference data. Accuracy ranged between 70% and 95%, depending on the region.

scholarly journals Modeling Land Use and Land Cover Change: Using a Hindcast to Estimate Economic Parameters in gcamland v2.0

2020 ◽  
Author(s):  
Katherine V. Calvin ◽  
Abigail Snyder ◽  
Xin Zhao ◽  
Marshall Wise
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Agricultural Production ◽  
Agricultural Land ◽  
Crop Yields ◽  
Economic Models ◽  
The United States ◽  
Historical Period ◽  
Large Set

Abstract. The world has experienced a vast increase in agricultural production since the middle of the last century. Agricultural land area has also increased at the expense of natural lands over this period, though at a lower rate than production. Future changes in land use and cover have important implications not only for agriculture but for energy, water use, and climate. However, these future changes are driven by a complex combination of uncertain socioeconomic, technological, and other factors. Estimates of future land use and land cover differ significantly across economic models of agricultural production, and efforts to evaluate these economic models over history have been limited. In this study, we use an economic model of land use, gcamland, to systematically explore a large set of model parameter perturbations and alternate methods for forming expectations about uncertain crop yields and prices. We run gcamland simulations with these parameter sets over the historical period in the United States to explore model fitness and to identify combinations that improve fitness. We find that an adaptive expectation approach minimizes the error between simulated outputs and observations, with parameters that suggest that for most crops landowners put a significant weight on previous information. Interestingly, for corn, where ethanol policies have led to a rapid growth in demand, the resulting parameters show that a larger weight is placed on more recent information. We conclude with the observation that historical modeling exercises such as this study are valuable both for understanding real world drivers of land use change and for informing modeling of future land use change.

scholarly journals Substantially Greater Carbon Emissions Estimated Based on Annual Land-Use Transition Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1126
Author(s):  
Jiaojiao Diao ◽  
Jinxun Liu ◽  
Zhiliang Zhu ◽  
Mingshi Li ◽  
Benjamin M. Sleeter
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Chesapeake Bay ◽  
Carbon Emissions ◽  
Agricultural Land ◽  
Carbon Sources ◽  
The United States ◽  
Wood Products ◽  
Forest Land ◽  
Ecosystem Carbon

Quantifying land-use and land-cover change (LULCC) effects on carbon sources and sinks has been very challenging because of the availability and quality of LULCC data. As the largest estuary in the United States, Chesapeake Bay is a rapidly changing region and is affected by human activities. A new annual land-use and land-cover (LULC) data product developed by the U.S. Geological Survey Land Change Monitoring and Analysis Program (LCMAP) from 2001 to 2011 was analyzed for transitions between agricultural land, developed land, grassland, forest land and wetland. The Land Use and Carbon Scenario Simulator was used to simulate effects of LULCC and ecosystem disturbance in the south of the Chesapeake Bay Watershed (CBW) on carbon storage and fluxes, with carbon parameters derived from the Integrated Biosphere Simulator. We found that during the study period: (1) areas of forest land, disturbed land, agricultural land and wetland decreased by 90, 82, 57, and 65 km2, respectively, but developed lands gained 293 km2 (29 km2 annually); (2) total ecosystem carbon stock in the CBW increased by 13 Tg C from 2001 to 2011, mainly due to carbon sequestration of the forest ecosystem; (3) carbon loss was primarily attributed to urbanization (0.224 Tg C·yr−1) and agricultural expansion (0.046 Tg C·yr−1); and (4) estimated carbon emissions and harvest wood products were greater when estimated with the annual LULC input. We conclude that a dense time series of LULCC, such as that of the LCMAP program, may provide a more accurate accounting of the effects of land use change on ecosystem carbon, which is critical to understanding long-term ecosystem carbon dynamics.

scholarly journals Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer

2021 ◽  
Vol 13 (5) ◽  
pp. 968 ◽  
Author(s):  
Tyler J. Lark ◽  
Ian H. Schelly ◽  
Holly K. Gibbs
Keyword(s):  
United States ◽  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
The United States ◽  
Error Rates ◽  
Agricultural Landscapes ◽  
Cropland Area ◽  
Data Layer ◽  
Cropland Data Layer

The U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL) is a 30 m resolution crop-specific land cover map produced annually to assess crops and cropland area across the conterminous United States. Despite its prominent use and value for monitoring agricultural land use/land cover (LULC), there remains substantial uncertainty surrounding the CDLs’ performance, particularly in applications measuring LULC at national scales, within aggregated classes, or changes across years. To fill this gap, we used state- and land cover class-specific accuracy statistics from the USDA from 2008 to 2016 to comprehensively characterize the performance of the CDL across space and time. We estimated nationwide area-weighted accuracies for the CDL for specific crops as well as for the aggregated classes of cropland and non-cropland. We also derived and reported new metrics of superclass accuracy and within-domain error rates, which help to quantify and differentiate the efficacy of mapping aggregated land use classes (e.g., cropland) among constituent subclasses (i.e., specific crops). We show that aggregate classes embody drastically higher accuracies, such that the CDL correctly identifies cropland from the user’s perspective 97% of the time or greater for all years since nationwide coverage began in 2008. We also quantified the mapping biases of specific crops throughout time and used these data to generate independent bias-adjusted crop area estimates, which may complement other USDA survey- and census-based crop statistics. Our overall findings demonstrate that the CDLs provide highly accurate annual measures of crops and cropland areas, and when used appropriately, are an indispensable tool for monitoring changes to agricultural landscapes.

scholarly journals Land Use/Land Cover Change Impact on Hydrological Process in the Upper Baro Basin, Ethiopia

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tewodros Getu Engida ◽  
Tewodros Assefa Nigussie ◽  
Abreham Berta Aneseyee ◽  
John Barnabas
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Swat Model ◽  
The United States ◽  
United States Geological Survey ◽  
Hydrological Process ◽  
Land Use Land Cover ◽  
Validation Period ◽  
The Impact

Understanding the hydrological process associated with Land Use/Land Cover (LU/LC) change is vital for decision-makers in improving human wellbeing. LU/LC change significantly affects the hydrology of the landscape, caused by anthropogenic activities. The scope of this study is to investigate the impact of LU/LC change on the hydrological process of Upper Baro Basin for the years 1987, 2002, and 2017. The Soil Water Assessment Tool (SWAT) model was used for the simulation of the streamflow. The required data for the SWAT model are soils obtained from the Food and Agriculture Organization; Digital Elevation Model (DEM) and LU/LC were obtained from the United States Geological Survey (USGS). The meteorological data such as Rainfall, Temperature, Sunshine, Humidity, and Wind Speeds were obtained from the Ethiopian National Meteorological Agency. Data on discharge were obtained from Ministry of Water, Irrigation and Electricity. Ecosystems are deemed vital. Landsat images were used to classify the LU/LC pattern using ERDAS Imagine 2014 software and the LU/LC were classified using the Maximum Likelihood Algorithm of Supervised Classification. The Sequential Uncertainty Fitting (SUFI-2) global sensitivity method within SWAT Calibration and Uncertainty Procedures (SWAT-CUP) was used to identify the most sensitive streamflow parameters. The calibration was carried out using observed streamflow data from 01 January 1990 to 31 December 2002 and a validation period from 01 January 2003 to 31 December 2009. LU/LC analysis shows that there was a drastic decrease of grassland by 15.64% and shrubland by 9.56% while an increase of agricultural land and settlement by 18.01% and 13.01%, respectively, for 30 years. The evaluation of the SWAT model presented that the annual surface runoff increased by 43.53 mm, groundwater flow declined by 27.58 mm, and lateral flow declined by 5.63 mm. The model results showed that the streamflow characteristics changed due to the LU/LC change during the study periods 1987–2017 such as change of flood frequency, increased peak flows, base flow, soil erosion, and annual mean discharge. Curve number, an available water capacity of the soil layer, and soil evaporation composition factor were the most sensitive parameters identified for the streamflow. Both the calibration and validation results disclosed a good agreement between measured and simulated streamflow. The performance of the model statistical test shows the coefficient of determination (R2) and Nash–Sutcliffe (NS) efficiency values 0.87 and 0.81 for calibration periods of 1990–2002 and 0.84 and 0.76 for the validation period of 2003 to 2009, respectively. Overall, LU/LC significantly affected the hydrological condition of the watershed. Therefore, different conservation strategies to maintain the stability and resilience of the ecosystem are vital.

scholarly journals Seasonal land use land cover change and the drivers in Kafta-Sheraro National Park, Tigray, Ethiopia

2021 ◽  
Author(s):  
Fitsum Temesgen ◽  
Bikila Warkineh ◽  
Alemayehu Hailemicael
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
National Park ◽  
Vegetation Index ◽  
Local Community ◽  
Agricultural Land ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Cultivated Land

AbstractKafta-sheraro national park (KSNP) is one of the homes of the African elephant has experienced extensive destruction of woodland following regular land use & land cover change in the past three decades, however, up to date, data and documentation detailing for these changes are not addressed. This study aims to evaluate the land use land cover change and drivers of change that occurred between 1988 and 2018. Landsat 5(TM), Landsat7 (ETM+), and Landsat 8 (OLI/TIRs) imagery sensors, field observation, and socio-economic survey data were used. The temporal and spatial Normalized difference vegetation index (NDVI) was calculated and tested the correlation between NDVI and precipitation/temperature. The study computed a kappa coefficient of the dry season (0.90) and wet season (0.845). Continuous decline of woodland (29.38%) and riparian vegetation (47.11%) whereas an increasing trend of shrub-bushland (35.28%), grassland (43.47%), bareland (27.52%), and cultivated land (118.36 km2) were showed over thirty years. More results showed bare land was expanded from wet to drier months, while, cultivated land and grazing land increased from dry to wet months. Based on the NDVI result high-moderate vegetation was decreased by 21.47% while sparse & non-vegetation was expanded by 19.8% & 1.7% (36.5 km2) respectively. Settlement & agricultural expansion, human-induced fire, firewood collection, gold mining, and charcoal production were the major proximate drivers that negatively affected the park resources. Around KSNP, the local community livelihood depends on farming, expansion of agricultural land is the main driver for woodland dynamics/depletion and this leads to increase resources competition and challenges for the survival of wildlife. Therefore, urgent sustainable conservation of park biodiversity via encouraging community participation in conservation practices and preparing awareness creation programs should be mandatory.

Analysis of Land Use Land Cover in Annapurna Conservation Area in Gandaki Province, Nepal using Vegetation Index and Land Use Land Cover Datasets

2019 ◽  
Vol 2 (2) ◽  
pp. 87-99
Author(s):  
Shiva Pokhrel ◽  
Chungla Sherpa
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Index ◽  
National Level ◽  
Climate Mitigation ◽  
Land Use Land Cover ◽  
Conservation Area ◽  
Barren Land ◽  
Wide Range ◽  
Annapurna Conservation Area

Conservation areas are originally well-known for protecting landscape features and wildlife. They are playing key role in conserving and providing a wide range of ecosystem services, social, economic and cultural benefits as well as vital places for climate mitigation and adaptation. We have analyzed decadal changes in land cover and status of vegetation cover in the conservation area using both national level available data on land use land cover (LULC) changes (1990-2010) and normalized difference vegetation index (NDVI) (2010-2018) in Annapurna conservation area. LULC showed the barren land as the most dominant land cover types in all three different time series 1990, 2000 and 2010 with followed by snow cover, grassland, forest, agriculture and water body. The highest NDVI values were observed at Southern, Southwestern and Southeastern part of conservation area consisting of forest area, shrub land and grassland while toward low to negative in the upper middle to the Northern part of the conservation area.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals Geospatial analysis of wetlands based on land use/land cover dynamics using remote sensing and GIS in Sindh, Pakistan

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110261
Author(s):  
Hamza Islam ◽  
Habibuulah Abbasi ◽  
Ahmed Karam ◽  
Ali Hassan Chughtai ◽  
Mansoor Ahmed Jiskani
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Kappa Statistics ◽  
Natural Ecosystem ◽  
Land Use Land Cover ◽  
Dry Land ◽  
Lulc Changes ◽  
Manchar Lake

In this study, the Land Use/Land Cover (LULC) change has been observed in wetlands comprises of Manchar Lake, Keenjhar Lake, and Chotiari Reservoir in Pakistan over the last four decades from 1972 to 2020. Each wetland has been categorized into four LULC classes; water, natural vegetation, agriculture land, and dry land. Multitemporal Landsat satellite data including; Multi-Spectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images were used for LULC changes evaluation. The Supervised Maximum-likelihood classifier method is used to acquire satellite imagery for detecting the LULC changes during the whole study period. Soil adjusted vegetation index technique (SAVI) was also used to reduce the effects of soil brightness values for estimating the actual vegetation cover of each study site. Results have shown the significant impact of human activities on freshwater resources by changing the natural ecosystem of wetlands. Change detection analysis showed that the impacts on the land cover affect the landscape of the study area by about 40% from 1972 to 2020. The vegetation cover of Manchar Lake and Keenjhar Lake has been decreased by 6,337.17 and 558.18 ha, respectively. SAVI analysis showed that soil profile is continuously degrading which vigorously affects vegetation cover within the study area. The overall classification accuracy and Kappa statistics showed an accuracy of >90% for all LULC mapping studies. This work demonstrates the LULC changes as a critical monitoring basis for ongoing analyses of changes in land management to enable decision-makers to establish strategies for effectively using land resources.

scholarly journals How Can We Represent Seasonal Land Use Dynamics in SWAT and SWAT+ Models for African Cultivated Catchments?

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1541
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Anna Msigwa ◽  
Hans C. Komakech ◽  
Boud Verbeiren ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Swat Model ◽  
Agricultural Practices ◽  
Area Index ◽  
Land Use Dynamics ◽  
Cropping Seasons ◽  
Decision Tables ◽  
Land Cover Maps

In SWAT and SWAT+ models, the variations in hydrological processes are represented by Hydrological Response Units (HRUs). In the default models, agricultural land cover is represented by a single growing cycle. However, agricultural land use, especially in African cultivated catchments, typically consists of several cropping seasons, following dry and wet seasonal patterns, and are hence incorrectly represented in SWAT and SWAT+ default models. In this paper, we propose a procedure to incorporate agricultural seasonal land-use dynamics by (1) mapping land-use trajectories instead of static land-cover maps and (2) linking these trajectories to agricultural management settings. This approach was tested in SWAT and SWAT+ models of Usa catchment in Tanzania that is intensively cultivated by implementing dominant dynamic trajectories. Our results were evaluated with remote-sensing observations for Leaf Area Index (LAI), which showed that a single growing cycle did not well represent vegetation dynamics. A better agreement was obtained after implementing seasonal land-use dynamics for cultivated HRUs. It was concluded that the representation of seasonal land-use dynamics through trajectory implementation can lead to improved temporal patterns of LAI in default models. The SWAT+ model had higher flexibility in representing agricultural practices, using decision tables, and by being able to represent mixed cropping cultivations.

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