Drivers Of Land Use Change Map
Mika Siljander, in, 2013 5 Discussion: Endangered Ecosystem ServicesLand-cover change has numerous ecological, physical and socioeconomic consequences. On the positive side, agricultural expansion may increase food production for a growing population, although it is unsure how productive the last exploited lands will be as they are typically the least favourable. There are numerous negative consequences with both known and unknown links and feedback mechanisms.Converting the natural vegetation to agricultural land is likely to change the radiation balance of the given unit of area. In principle, the albedo increases as land is without vegetation at least part of the year causing more solar energy to reflect back to the space. Other environmental impacts include the decrease in soil water-holding capacity.
As natural vegetation is replaced by agriculture, soil porosity may be reduced by soil compaction, decreasing infiltration capacity and increasing the risks of soil erosion. In mountainous areas, the conversion of the forests to agricultural lands decreases as does the occult precipitation as croplands capture less atmospheric moisture than multilayered indigenous forest or forest of any kind ( Holder, 2004). Cloud formation over the land unit also decreases as the evapotranspiration rate is less from fields than from forests causing evidently reduced precipitation.
Land-use change affects the provision of ecosystem services and wildlife habitat. We project land-use change from 2001 to 2051 for the contiguous United States under two scenarios reflecting continuation of 1990s trends and high crop demand more reflective of the recent past. 79(1) Seto and Kaufinann: Modeling the Drivers of Urban Land Use Change 107 with disparate. Use change.2 The map has nine classes: natu- ral vegetation. Mapping and Analysis of Land Use and Land Cover for a Sustainable Development Using High Resolution. The basic premise in using satellite images for change detection is that changes in land cover result in changes in radiance values that can be remotely sensed. Detect, identify, and map changes in landuse/land cover. Image differencing.
Histogram showing the cropland patches distribution during 1987, 2003 and 2030, in relation to the historical average potential evapotranspiration in the Taita Hills. Adapted from Maeda et al.
(2011).As soil water-holding capacity is reduced, the risk of hydrologic droughts during dry seasons is increased, while during the rainy seasons, soils are more susceptible to erosion. These soil loss and sediment-deposition processes may have a significant impact on agriculture, local economies and ecosystems ( Alcantara-Ayala et al., 2006).
Although increasing evidences indicate that anthropogenic changes in the landscape are likely to lead to regional and global climate change, the levels and scale of this relationship remain largely unknown. However, it is clear that converting forestland to agricultural land causes changes in local climate via the changes in radiation and water balance. Changes in precipitation and temperature patterns will likely have important impacts on the sustainability of agricultural systems.The land-cover change taking place in the Taita Hills and its surrounding has been continuing since human settlers arrived in the area and started to convert native vegetation to agricultural land. With ever-growing population and demand for land for cultivation of food crops and other crop types in addition to increase of reserved and protected areas in the study area, land is evidently becoming a valuable natural resource.
Land use conflicts have already taken place in the area between farmers, conservationists, settlers and sisal plantation managers ( Vanonen, 2008).Converting natural vegetation, forest or grassland to agricultural areas decreases biodiversity, reduces the capability of vegetation to capture atmospheric moisture and retain water in the vegetation cover, exposes land to be subject to water and wind erosion and changes the radiation balance of the land surface as land is exposed and barren part of the year. These all have still unknown impacts on regional climate. The land-cover change model (LCCM) consists of a set of discrete choice equations of site-based land-cover transitions derived from observed land-cover change ( Fig.
23-2) that are applied to geographic information system (GIS) layers to predict land-cover change at a 30 m resolution across four counties in western Washington, representing the central Puget Sound Region. A short description of the model follows; a complete description of the theoretical foundations of the model is available in Hepinstall et al.
Flow chart of steps performed as part of the land-cover change model and the avian richness and relative abundance models used to predict bird response to changes in land cover.The LCCM framework derives from the traditions of modeling landscape change as a dynamic interaction between socioeconomic and biophysical processes ( Turner et al. 1996, Wear and Bolstad 1998, Wear et al. The LCCM is written in Python and is designed as a module within the larger Open Platform for Urban Simulation (OPUS) and UrbanSim modeling platforms ( Waddell 2002, Waddell et al. UrbanSim consists of a series of modules that have been developed to, among other things, model land-use change in response to changes in transportation networks, household and business location, property development and intensity, infrastructure changes, and policy choices. UrbanSim is designed to aid regional land-use planning.Urban development models such as UrbanSim predict changes in land use (e.g., undeveloped, residential, commercial, mixed use, timberlands) and development intensity (number of residential units or square feet of commercial space), whereas avian communities respond to changes in vegetation type and structure. We must link models predicting change in land use to models of land-cover change, which then can be used to predict the effects of land development on avian communities.
Our LCCM predicts future land cover in response to land-use change and biophysical constraints.For our implementation of the LCCM, we simulated the potential change to one of eight land-cover classes: heavy urban (80% impervious surfaces), medium urban (20-80% impervious surfaces), low urban (a mixed class with. Laurens Bouwer. Marianne Zandersen, in, 2018 4.5.5 Carbon SequestrationMultiple available land use and land cover change scenarios at the European-scale show a potential increase of forested areas.
According to this results, a substantial space for reforestation may appear in the next several decades, which could be utilized to efficiently increase the level of carbon stocks. Although the aggregate results show an increase in carbon stocks, the level of increase is in fact rather low. Furthermore, the spatial pattern of potential change shows that the most substantial increase in forested areas and related carbon stocks occurs in the sparsely populated north of Europe, while the densely populated areas of Western Europe undergo a decrease in forest cover, mainly due to urban sprawl. Abhishek Gaur, Slobodan P. Simonovic, in, 2019 2.5 Previous Assessments of Future Land-Cover Change Impact on ClimateThe impact of future projected land-cover change on climate has been studied using GCMs at regional to global scales. The typical methodology has been to compare climate projections obtained from the GCMs with and without considering future land-cover change. For example, Hua et al.
(2015) analyzed future temperature projections obtained with and without considering LULC change from a GCM: CanESM2 under two RCPs: RCP2.6 and RCP8.5 and found small magnitudes of change at a global scale; however, changes of upto 0.1°C were obtained at regional scales. Quesada et al. (2017) investigated the impact of future LULC change on monsoonal precipitation by analyzing future projections under RCP8.5 from five earth system models (ESMs) and found significant changes in four (out of eight) of those regions.
A unique methodology to identify land-cover change–induced climatic changes is proposed by Kumar et al. They compared climatic projections made for two neighboring sites: one which has projected to experience land-cover change in future and another which is projected to remain unchanged in future.
From their analysis using 14 GCMs under RCP8.5, they found substantial land-cover change–induced summer warming in parts of North America and Eurasia.Studies have also evaluated land-cover change effects on climate at local scales. Malyshev et al. (2015) analyzed the effect of local-scale land-cover heterogeneity in the geophysical fluid dynamics laboratory (GFDL) ESM and found significant effects on climate. Georgescu et al.
Drivers Of Land Use Change Map In America
(2013) simulated mid- and end-of-century temperature for the Arizona city using the weather research and forecast (WRF) modeling system. They found that the projections are highly sensitive to the scenario-based land-cover trajectory. Both mid- and end-century temperature estimates were found to be strongly dependent on the built environment and future emission pathways in the Sun Corridor expansion projections. Chen and Frauenfeld (2015) downscaled future temperature and precipitation projections made by Community ESM under RCP4.5 using WRF modeling system over China. They obtained significant effects of future-projected urbanization on both temperature and precipitation across China. Argueso et al.
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(2014) used the WRF modeling system to downscale CSIRO MK3.5 GCM outputs to 2 Km grid-scale. They simulated the present (1990–2009) and future (2040–59) climates for Sydney area and concluded that coupling of future urbanization effects and climate change will significantly affect the local climatology. They projected more intense changes in minimum temperatures than in maximum temperatures, particularly in winter and spring season when urban effects will contribute almost equally toward temperature change as the changes in global emissions. Kusaka et al.
(2012) used dynamically downscaled projections from three GCMs MIROC3.2-medres, MRI-CGCM2.3.2a, and CSIRO-Mk3.0 climate models to access future (2070s) summertime (August) temperature for three largest urban settlements in Japan: Tokyo, Osaka, and Nagoya. They used WRF model to get high resolution temperature distributions in the cities and found that the city temperatures will increase by 2–3°C in future. Also the impact from UHI effect was found to be comparable to that from greenhouse gas emissions. (2014) used a high resolution limited area model ARPEGE-IFS coupled with Town Energy Balance model to estimate changes in UHI magnitudes as projected by the ARPEGE-Climat GCM in the timeslice 2071–2100.
The model was run at 4 km spatial resolution for SRES A1B scenario and it was found from the inline run (where TEB was activated for regional as well as urban runs) that the intensity of daytime UHI will decrease by 0.2–0.24°C. Also, strong UHI events were projected to decrease in the future by 1°C. McCarthy et al. (2012) used the latest version of the Hadley Centre Regional Climate model HadRM3 at 25-km resolution coupled to a simple urban land-surface scheme ( Best et al., 2006) to assess the sensitivity of UK urban climates to large-scale greenhouse gas–induced climate change, local forcing from urban land use, and anthropogenic heat flux resulting from energy use in the urban areas.
Adachi et al. (2012) also calculated future UHI intensities for Tokyo city by using five future projections from climate models downscaled using the TERC-RAMS regional model. After comparing the results obtained with and without incorporating urban effects, they concluded that the temperature change between 1990s and 2070s because of greenhouse gas emissions is projected to be around 2°C while due to land-cover changes is projected to be around 0.5°C. Several other studies have also evaluated local-scale changes in climate due to future projected urbanization ( Zong-Ci et al., 2013; Zhao et al., 2013; Cao et al., 2016; Bounoua et al., 2015; Zhang et al., 2016). Table 3 compares some focal land cover changes between the current 2010 land cover and all four future scenarios.
Both native and alien forest expanded from 2010 to the Conservation Focus (CF) and Balanced Conservation and Development (BCD) scenarios, but with transitions to lower native forest cover and higher alien forest cover for both the Status Quo (SQ) and Development Focus (DF) scenarios. Developed cover increased from 2010 in all future scenarios, while sugarcane cover decreases at least slightly (SQ) or disappears entirely (DF/BCD). As the 2010 land cover map shows ( Fig. 4), commercial sugarcane production currently dominates the low-elevation plane in central Maui, but the stakeholders consulted for this project almost unanimously agreed that alternative viable crops needed to be found for the region to reflect a reduced demand from the US mainland for Hawai‘i-produced sugarcane. The diversified agriculture category covered a very small area (7.4 km 2) of mostly organic farms in east Maui in the 2010 map. However, it also includes crops being considered for biofuel production in Maui, such as sorghum, in addition to many common food crops.
Thus, as sugarcane decreases and eventually disappears, the diversified agriculture land cover class increases dramatically to over 100 km 2 in some scenarios. Finally, areas that are fallow decrease with added grassland in the first two future scenarios, but expand in the final two as sugarcane transitions to fallow in non-IAL regions of central Maui.
Class2010Scenario change relative to 2010 cover, in km 2 (%)CFSQDFBDCAlien forest336.0347.20 (14)120.88 (36)278.36 (83)36.29 (11)Native forest380.88146.99 (39)− 4.50 (− 1)− 121.51 (− 32)143.55 (38)Developed (all classes)191.7746.26 (24)71.46 (37)119.23 (62)121.91 (64)Sugarcane161.80− 57.51 (− 36)− 5.78 (− 4)− 161.80 (− 100)− 161.80 (− 100)Diversified agriculture/biofuel7.36107.82 (1465)17.98 (244)95.70 (1300)115.04 (1563)Fallow/grassland42.29− 36.53 (− 86)− 33.25 (− 79)37.79 (89)18.87 (45)The changes relative to 2010 cover allow us to consider potential island-wide transitions through time. They also demonstrate how the use of remote sensing and GIS during the scenario development processes encouraged stakeholders to condense complex decisions and trade-offs into indicators of landscape-level properties.
The spatial pattern of those changes can also help stakeholders evaluate the outcomes of future decisions in the regions they manage. Furthermore, once the scenario maps are incorporated into the climate and water budget-modeling process, it will be possible to spatially compare the impacts of climate adaptation efforts through change detection as a function of groundwater recharge. The next section examines three potential land cover transitions that were of particular interest to the project participants. 9.18.3.5.1 Forest transitions.
Between 2010 and the DF scenario, alien forest expansion (“new alien forest,” Fig. 6A) was common throughout the island’s lower elevations, as well as the highest elevations of the mountains in west Maui. This reflects a significant loss of native forest in that scenario as budget cuts and a lack of restoration efforts resulted in 116 km 2 being converted to new alien forest. In the Balanced Development and Conservation scenario, however ( Fig. 6B), new alien forest only appears along the lower elevations of the island’s southeastern slope, while native forest populations are either maintained (as in west Maui and windward east Maui) or augmented through management efforts that prioritize fencing and active restoration (the upper elevations of southeastern Maui). This pattern of forest transition is due to the absence of a coherent conservation management plan for the lowland southeast, which is currently dry and barren. Climate projections and early alien species dispersal trends indicate that this should be a target area for future intervention. In all four future scenarios nearly every land cover class experienced at least some transition to a developed type, especially to the low- and medium-intensity classes, indicative of the addition of residential homes and apartment units.
In the highest development scenario (DFs), this new impervious surface cover is especially concentrated in west Maui near the existing resort communities of Lahaina and Kā‘anapali in west Maui, and Kihei along the island’s south central coast ( Fig. 7). Former pineapple and sugarcane production areas that were largely fallow in the Lahaina area by 2010 became new developments, reflecting sales and rezoning of land currently held by agricultural companies ( Fig. 7A). Development in the Kihei/Wailea area occurred on land that was mostly shrubland or grassland in 2010, as well as alien forest.
The town of Kihei is in one of the driest regions of the island, and its residents and tourists are also dependent on groundwater that is pumped across the central valley from an aquifer that is currently at 84% of its sustainable yield ( Johnson et al., 2014). Rising costs for pumping and uncertain groundwater supplies in general, combined with climate projections that imply the region will only get drier, make Kihei an area of great focus, particularly if development approaches the maximum zoned limit ( Fig. 7B). Of particular interest to project participants was the future of the last commercial sugarcane plantation in Hawai‘i, which comprises 36,000 acres in the central isthmus of Maui (28,000 of which are IALs). In scenario development discussions with stakeholders including the owner, Hawaiian Commercial and Sugar company (HC&S), different feasible options for alternative uses for sugarcane land were incorporated into the future land cover scenarios, including one featuring a partial conversion to biofuels, where suitable ( Fig. 8A), and one in which diversified agriculture and fallow regions completely replace sugarcane production ( Fig. 8B). HC&S had been faced with financial challenges for years, leading to discussions at the local and state level about how the land could be better utilized for environmental, agricultural, and economic purposes. Indeed, in Jan. 2016 HC&S announced a planned transition out of all sugarcane agriculture by the end of the year, citing a move to diversified agriculture and biofuels ( Hawai‘i News Now Staff, 2016 ).
Stakeholders accurately predicted this central Maui transition, and the use of remote sensing and GIS data in the scenario process was a powerful way to reflect on the magnitude of potential land cover change, especially in a region that currently experiences artificially high levels of groundwater recharge due to irrigation. There are two general approaches for land cover change mapping. The first is multitemporal land cover comparison (MT-LCC).
In this approach, a land cover product is developed for each time point. Differences among the products derived for different time points are mapped as change. In the second approach, changes are mapped the same way as static land cover classes through multitemporal spectral change analysis (MT-SCA). Table 1 provides a list of regional to global scale forest change products derived using these methods.
1,2, 3, 3 and 41School of Environment, Resources and International Trade, Hubei University of Economics, Wuhan 430205, China2Center of Hubei Cooperative Innovation for Emissions Trading System, Wuhan 430205, China3School of Public Administration, China University of Geosciences, Wuhan 430074, China4School of Economics and Management, China University of Geosciences, Wuhan 430074, ChinaReceived 3 December 2015; Revised 17 February 2016; Accepted 14 March 2016Academic Editor: Elmetwally Elabbasy. AbstractTo investigate precise nexus between land-use and land-cover changes (LUCC) and driving factors for rational urban management, we used remotely sensed images to map land use and land cover (LULC) from 1990 to 2010 for four time periods using Wuhan city, China, as a case study. Partial least squares (PLS) method was applied to analyze the relationships between LUCC and the driving factors, mainly focusing on three types of LULC, that is, arable land, built-up area, and water area. The results were as follows: during the past two decades, the land-use pattern in Wuhan city showed dramatic change. Arable land is made up of the largest part of the total area. The increased built-up land came mainly from the conversion of arable land for the purpose of economic development. Based on the Variable Importance in Projection (VIP), the joint effects of socioeconomic and physical factors on LUCC were dominant, though annual temperature, especially annual precipitation, proved to be less significant to LUCC.
Population, tertiary industry proportion, and gross output value of agriculture were the most significant factors for three major types of LULC. This study could help us better understand the driving mechanism of urban LUCC and important implications for urban management.