Understanding land use/land cover (LULC) effects on tropical soil infiltration is crucial for maximizing watershed scale hydro‐ecosystem services and informing land managers. This paper reports results from a multiyear investigation of LULC effects on soil bulk infiltration in steep, humid tropical, and lowland catchments. A rainfall simulator applied water at measured rates on 2 × 6 m plots producing infiltration through structured, granulated, and macroporous Ferralsols in Panama's central lowlands. Time‐lapse electrical resistivity tomography (ERT) helped to visualize infiltration depth and bulk velocity. A space‐for‐time substitution methodology allowed a land‐use history investigation by considering the following: (a) a continuously heavy‐grazed cattle pasture, (b) a rotationally grazed traditional cattle pasture, (c) a 4‐year‐old (y.o.) silvopastoral system with nonnative improved pasture grasses and managed intensive rotational grazing, (d) a 7 y.o. teak (
Linking Global Land Use/Land Cover to Hydrologic Soil Groups From 850 to 2015
The characteristics of soils in part determine human‐induced land use and land cover (LULC) change, and together, soil properties and LULC directly impact global water, energy, and biogeochemical cycles. Plant health and the exchange of energy, water, and biogeochemical components at the surface interface are partly controlled by soil properties. Different soil types modify vegetation responses to existing climate forcings, and each soil type also responds differently to the same land‐use practice. Currently, Earth system models often use single soil columns with averaged properties. This leads to uncertainties in assessing LULC impacts. To improve the estimates of land surface change in Earth System Models, we link an existing LULC data set to four hydrologic soil groups from 850 to 2015, based on demonstrated soil preferences for eight LULCs under current conditions. We conclude that humans prefer hydrologic soil groups in order from B, D, C, to A. This ranking was applied to construct the history of LULC on each soil type at the 0.5° grid resolution. Results primarily distribute croplands to hydrologic soil group B in 850, while hydrologic soil group A has the most undisturbed area. Over time, good soils (hydrologic soil groups B and D) experience increased use for cropland areas, while poor soils (hydrologic soil groups C and A) are occupied predominantly by increasing areas in grazing land and secondary nonforests. The results provide better land surface characteristics to improve Earth systems modeling.
more » « less- NSF-PAR ID:
- 10458622
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Global Biogeochemical Cycles
- Volume:
- 34
- Issue:
- 3
- ISSN:
- 0886-6236
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Tectona grandis ( ), (g) an approximately 30 y.o. secondary succession forest, and (h) a >100 y.o. secondary succession forest. Within a land cover, unique plot sites totalled two at (a), (c), (d), (e), and (g); three at (b); and one at (f) and (h). Our observations confirmed measured infiltration scale dependency by comparing our 12 m2plot‐scale measurements against 8.9 cm diameter core‐scale measurements collected by others from nearby sites. Preferential flow pathways (PFPs) significantly increased soil infiltration capacity, particularly in forests greater than or equal to 10 y.o. Time‐lapse ERT observations revealed shallower rapid bulk infiltration and increased rapid lateral subsurface flow in pasture land covers when compared with forest land covers and highlighted how much subsurface flow pathways can vary within the Ferralsol soil class. Results suggest that LULC effects on PFPs are the dominant mechanism by which LULC affects throughfall partitioning, runoff generation, and flow pathways.Coffea canephora -
Accurate estimation of land use/land cover (LULC) areas is critical, especially over the semi-arid environments of the southwestern United States where water shortage and loss of rangelands and croplands are affecting the food production systems. This study was conducted within the context of providing an improved understanding of New Mexico’s (NM’s) Food–Energy–Water Systems (FEWS) at the county level. The main goal of this analysis was to evaluate the most important LULC classes for NM’s FEWS by implementing standardized protocols of accuracy assessment and providing bias-corrected area estimates of these classes. The LULC data used in the study was based on National Land Cover Database (NLCD) legacy maps of 1992, 2001, 2006, 2011, and 2016. The analysis was conducted using the cloud-based geospatial processing and modeling tools available from System for Earth Observation Data Access, Processing, and Analysis for Land Monitoring (SEPAL) of the Food and Agricultural Organization. Accuracy assessment, uncertainty analysis, and bias-adjusted area estimates were evaluated by collecting a total of 11,428 reference samples using the Open Foris Collect Earth tool that provided access to high spatial and temporal resolution images available in Google Earth. The reference samples were allocated using a stratified random sampling approach. The results showed an overall accuracy that ranged from 71%–100% in all six study counties. The user’s and producer’s accuracy of most LULC classes were about or above 80%. The obtained bias-adjusted area estimates were higher than those based on pixel counting. The bias-adjusted area estimates simultaneously showed decreasing and increasing trends in grassland and shrubland, respectively in four counties that include Curry, Roosevelt, Lea, and Eddy during the 1992–2016 period. Doña Ana county experienced increasing and decreasing trends in grassland and shrubland areas, respectively. San Juan county experienced decreasing trends in both grassland and shrubland areas. Cultivated cropland areas showed decreasing trends in three counties in southeast NM that rely on groundwater resources including Curry, Roosevelt, and Lea. Similarly, cultivated cropland areas showed increasing trends in the other three counties that rely on surface water or conjunctive use of surface and groundwater resources including San Juan, Doña Ana, and Eddy. The use of SEPAL allowed for efficient assessment and production of more accurate bias-adjusted area estimates compared to using pixel counting. Providing such information can help in understanding the behavior of NM’s food production systems including rangelands and croplands, better monitoring and characterizing NM’s FEWS, and evaluating their behavior under changing environmental and climatic conditions. More effort is needed to evaluate the ability of the NLCD data and other similar products to provide more accurate LULC area estimates at local scales.more » « less
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Land-use and land-cover (LULC) change is a primary driver of terrestrial carbon release, often through the conversion of forest into agriculture or expansion of urban areas. Classification schemes are a key component of landscape analyses. This study creates a novel LULC classification scheme by incorporating ecological data to redefine classes of an existing LULC classification based on variation in above-ground tree carbon. A tree inventory was conducted for 531 plots within a subbasin of the Tampa Bay Watershed, Florida, USA. Above-ground tree carbon was estimated using the i-Tree model. Plots were classified using the Florida Land Use Cover Classification System. Mean quantities of above-ground tree carbon, by class, were tested for statistical differences. A reclassification was conducted based on these differences. Sub-classes within a given “land cover” class were similar for six of the seven classes. Significant differences were found within the “Wetlands” class based on vegetation cover, forming two distinct groups: “Forested Wetlands” and “Non-forested and Mangrove Wetlands”. The urban “land use” class showed differences between “Residential” and “Non-residential” sub-classes, forming two new classes. LULC classifications can sometimes aggregate areas perceived as similar that are in fact distinct regarding ecological variables. These aggregations can obscure the true variation in a parameter at the landscape scale. Therefore, a study’s classification system should be designed to reflect landscape variation in the parameter(s) of interest.more » « less
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Through integrating multi-source data including high-resolution remote sensing image-based land use and land cover (LULC) data, model-based land use products, and historical land archives, we reconstructed historical LULC at an annual time scale and 1 km x 1 km resolution in the contiguous United States (CONUS) from 1630 to 2020. Compared to other historical LULC datasets, our data can capture the major characters of LULC as well as provide more accurate information with higher spatial and temporal resolution. The LULC data can be used for regional studies in a wide range of topics including LULC impacts on the ecosystem, biodiversity, water resource, carbon and nitrogen cycles, and greenhouse gas emissions.
