Landcover Change Database
Land use and land cover (LULC) data is a term used about data derived from raw satellite information about the land use in a given jurisdiction. On this note, landcover represents the physical or actual presence of vegetation or other objects where the vegetation is not existent on the land surface. Besides, the definition of landcover can be extended to include any object or organism that can be viewed above the land but present in the land. The primary goal of designing a landcover database is to describe the landscape characteristics and patterns crucial in understanding various environmental aspects that incorporate the availability of habitat, changes of habitat, dispersion of chemicals and other pollutants potential, and the forecasting major contributors to the climate change (Wickham, Stehman, and Homer 2018). On this note, the dataset is essential in the wellbeing of the environment an determining the ideal approaches of preventing its degradation.
There are various ways of using the information derived from landcover databases. In this regard, various jurisdictions have come up with their means of collecting and analyzing landcover databases. In 1985, the European Union initiated the CORINE Land Cover (CLC), which have continued to produce updates in the year 2000, 2006, 2012, and 2018. The data derived from the system incorporates an inventory of land cover categorized in 44 classes. The system uses the Minimum Mapping Unit (MMU) of 25 ha for areal phenomena. The system also uses a minimum width of 100m for linear phenome. The data is enhanced by the introduction of change layers, which underline alterations in landcover with an MMU of 5 ha (Helber et al. 2019). Different MMUs indicate the change layer benefits from a higher resolution than the previous data derived from the status layer. However, the difference in MMUs between the status layers should not be the same as the CLC-Change Layer. Other types of landcover datasets include MRLC and NLCD, which have been adopted in the United States that have different datasets covering different periods.
The National Land Cover Database (NLCD) is one primary landcover database applied in the United States. The system offers information about national landcover. The land cover of information derived from NLCD is offered through a 30-meter resolution Landsat data, which is the definitive database for the country. The country has used the system for a variety of applications that support the activities of various organizations that include federal, state, local, and non-governmental agencies (Maclaurin and Leyk 2016). The primary goal of the system is to provide support for various systems that are designed to improve the environmental protection agenda of the country. Primary, the assessment of the status and health of the ecosystem is one of the means through which the NLCD has benefitted the country (Perez-Hoyos et al., 2017). Other applications include the understanding of biodiversity spatial patterns, predict impacts on climate change, and the development of policies associated with land management. The products produced by NLCD are created through the use of Multi-Resolution Land Characteristic (MRLC) Consortium made by the collaboration between the U.S. Geological Survey and other federal agencies.
National Landcover Database Landsat Image
The NLCD provides a variety of data that can be used in various ecosystem monitoring. One of the influential data provided by the NLCD is land use and land cover. The MRLC has pooled its resources to acquire Landsat data and subsequently created the national land cover database in the early 1990s. The initial NLCD data was provided in 1993 and represented the data that was derived from 1992 observations. The consortium has repeated this effort almost a decade later to provide the NLCD 2001 data. The MRLC is not focused on the provision of the data after every five years, which means that it has released the information in 2006, 2011, and 2016. The latest data (2016) was released in 2019 (Helber et al. 2019). On this note, any entity interested in the data can access it for free at the online MRLC data, which means that agencies and organizations interested in identifying the environmental changes including government and non-government organization are offered a means through which they can easily identify the patterns (Williamson and Claggett 2019).
The other crucial data that is released by the NLCD include land cover change. The system has a formidable means of detecting land cover change over varying periods. On this note, the system detected that between 2001 and 2006, the country’s land cover had changed by 1.68% (135,560) of the total area (Yang et al. 2018). The dataset also revealed that the largest proportion of land was in the Southeast and Pacific Northwest. The NLCD dataset provides multiple products that range from 1992 to 2001 in different periods in between. The land cover change dataset also provides information trends through a project of the USGS that seeks to have a more informed perception of the rates, causes, trends, and consequences of the modern use of American land. In this regard, the USGS released a report covering 30 American ecological regions titled Status and Trends of Land Change in the Western United States 1973 – 2001, which is a comprehensive and detailed analysis of the types and rates of landcover and land-use that have altered in the region (Jin et al. 2017). In this regard, land cover change is one of the critical datasets provided by the NLCD, which enables federal, state, and national agencies to identify the means through which land change has affected the environment and ecological aspects of the country (Homer et al. 2020). Thus, NLCD has been influential in ensuring that environmental agencies and organizations can come up with the ideal policies that will safeguard the country from the effects of climate change.
A Landsat Image of Landcover Change
The NLCD also provides information about tree canopy cover. One of the federal agencies that have used this information in the United States Forest Service (USFS), which developed a production of the NLCD titled Tree Canopy Cover in 2011. The database applies a file pixel ranging from 0 to 100%. Each pixel value represents the proportion or area of a 30m cell that is covered by a tree canopy (Dong, Kuang, and Liu 2017). The data is influential by the use of various organizations including wildland fire, wildlife habitat, and carbon emission estimation uses.
NLCD also provides critical information about the percentage of developed impervious surfaces. The data focus on a given area and is provided using a Landsat satellite applying classification schemes. The data specification is provided in values ranging from 0 – 100%, which indicates the degree of the amount of man-made impervious surfaces across the country. On this note, one of the environmental risks that affect the country is impervious surface as a result of human activity (Lark et al. 2017). With this kind of data, government agencies and non-governmental organizations can identify the dangers associated with human activity in different regions and come up with the ideal policies that will protect the environment from human activities.
Landcover datasets are influential in the provision of information that will help policymakers in making sustainable decisions. Without the datasets from various countries, the government agencies and non-governmental organizations would not have an inkling of which areas are being highly affected by the ongoing environmental phenomena (Smucker et al. 2016). Moreover, the organizations will be provided with the ideal information on how to handle a given situation and prevent future environmental devastations (Bohn and Vivoni 2019). On this note, the datasets have been influential in highlighting the trends and changes in landcover, which is one of the means that the government can mitigate environmental issues.
Bibliography
Bohn, T.J. and Vivoni, E.R., 2019. MOD-LSP, MODIS-based parameters for hydrologic modeling of North American land cover change. Scientific data, 6(1), pp.1-13.
Dong, J., Kuang, W. and Liu, J., 2017. Continuous land cover change monitoring in the remote sensing big data era. Science China Earth Sciences, 60(12), pp.2223-2224.
Helber, P., Bischke, B., Dengel, A. and Borth, D., 2019. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), pp.2217-2226.
Homer, C., Dewitz, J., Jin, S., Xian, G., Costello, C., Danielson, P., Gass, L., Funk, M., Wickham, J., Stehman, S. and Auch, R., 2020. Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database. ISPRS Journal of Photogrammetry and Remote Sensing, 162, pp.184-199.
Jin, S., Yang, L., Zhu, Z. and Homer, C., 2017. A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sensing of Environment, 195, pp.44-55.
Lark, T.J., Mueller, R.M., Johnson, D.M. and Gibbs, H.K., 2017. Measuring land-use and land-cover change using the US department of agriculture’s cropland data layer: Cautions and recommendations. International journal of applied earth observation and geoinformation, 62, pp.224-235.
Maclaurin, G.J. and Leyk, S., 2016. Temporal replication of the national land cover database using active machine learning. GIScience & Remote Sensing, 53(6), pp.759-777.
Pérez-Hoyos, A., Rembold, F., Kerdiles, H. and Gallego, J., 2017. Comparison of global land cover datasets for cropland monitoring. Remote Sensing, 9(11), p.1118.
Smucker, N.J., Kuhn, A., Charpentier, M.A., Cruz-Quinones, C.J., Elonen, C.M., Whorley, S.B., Jicha, T.M., Serbst, J.R., Hill, B.H. and Wehr, J.D., 2016. Quantifying urban watershed stressor gradients and evaluating how different land cover datasets affect stream management. Environmental management, 57(3), pp.683-695.
Wickham, J., Stehman, S.V. and Homer, C.G., 2018. Spatial patterns of the United States National Land Cover Dataset (NLCD) land-cover change thematic accuracy (2001–2011). International journal of remote sensing, 39(6), pp.1729-1743.
Williamson, T.N. and Claggett, P., 2019. Sensitivity of streamflow simulation in the Delaware River Basin to forecasted land‐cover change for 2030 and 2060. Hydrological processes, 33(1), pp.115-129.
Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Bender, S.M., Case, A., Costello, C., Dewitz, J., Fry, J. and Funk, M., 2018. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS journal of photogrammetry and remote sensing, 146, pp.108-123.