Place-Based Evaluation for Integrated Land-Use Management

This book brings together experts in the fields of spatial planning, landuse and infrastructure management to explore the emerging agenda of spatially-oriented integrated evaluation. It weaves together the latest theories, case studies, methods, policy and practice to examine and assess the values, impacts, benefits and the overall success in integrated land-use management. In doing so, the book clarifies the nature and roles of evaluation and puts forward guidance for future policy and practice.

Assessing trends in land use change in the Borana rangeland Ethiopia as one cause of greenhouse gas emissions and carbon sequestration variations

Landnutzungsänderungen sind eine wesentliche Ursache von Treibhausgasemissionen. Die Umwandlung von Ökosystemen mit permanenter natürlicher Vegetation hin zu Ackerbau mit zeitweise vegetationslosem Boden (z.B. nach der Bodenbearbeitung vor der Aussaat) führt häufig zu gesteigerten Treibhausgasemissionen und verminderter Kohlenstoffbindung. Weltweit dehnt sich Ackerbau sowohl in kleinbäuerlichen als auch in agro-industriellen Systemen aus, häufig in benachbarte semiaride bis subhumide Rangeland Ökosysteme. Die vorliegende Arbeit untersucht Trends der Landnutzungsänderung im Borana Rangeland Südäthiopiens. Bevölkerungswachstum, Landprivatisierung und damit einhergehende Einzäunung, veränderte Landnutzungspolitik und zunehmende Klimavariabilität führen zu raschen Veränderungen der traditionell auf Tierhaltung basierten, pastoralen Systeme. Mittels einer Literaturanalyse von Fallstudien in ostafrikanischen Rangelands wurde im Rahmen dieser Studie ein schematisches Modell der Zusammenhänge von Landnutzung, Treibhausgasemissionen und Kohlenstofffixierung entwickelt. Anhand von Satellitendaten und Daten aus Haushaltsbefragungen wurden Art und Umfang von Landnutzungsänderungen und Vegetationsveränderungen an fünf Untersuchungsstandorten (Darito/Yabelo Distrikt, Soda, Samaro, Haralo, Did Mega/alle Dire Distrikt) zwischen 1985 und 2011 analysiert. In Darito dehnte sich die Ackerbaufläche um 12% aus, überwiegend auf Kosten von Buschland. An den übrigen Standorten blieb die Ackerbaufläche relativ konstant, jedoch nahm Graslandvegetation um zwischen 16 und 28% zu, während Buschland um zwischen 23 und 31% abnahm. Lediglich am Standort Haralo nahm auch „bare land“, vegetationslose Flächen, um 13% zu. Faktoren, die zur Ausdehnung des Ackerbaus führen, wurden am Standort Darito detaillierter untersucht. GPS Daten und anbaugeschichtlichen Daten von 108 Feldern auf 54 Betrieben wurden in einem Geographischen Informationssystem (GIS) mit thematischen Boden-, Niederschlags-, und Hangneigungskarten sowie einem Digitales Höhenmodell überlagert. Multiple lineare Regression ermittelte Hangneigung und geographische Höhe als signifikante Erklärungsvariablen für die Ausdehnung von Ackerbau in niedrigere Lagen. Bodenart, Entfernung zum saisonalen Flusslauf und Niederschlag waren hingegen nicht signifikant. Das niedrige Bestimmtheitsmaß (R²=0,154) weist darauf hin, dass es weitere, hier nicht erfasste Erklärungsvariablen für die Richtung der räumlichen Ausweitung von Ackerland gibt. Streudiagramme zu Ackergröße und Anbaujahren in Relation zu geographischer Höhe zeigen seit dem Jahr 2000 eine Ausdehnung des Ackerbaus in Lagen unter 1620 müNN und eine Zunahme der Schlaggröße (>3ha). Die Analyse der phänologischen Entwicklung von Feldfrüchten im Jahresverlauf in Kombination mit Niederschlagsdaten und normalized difference vegetation index (NDVI) Zeitreihendaten dienten dazu, Zeitpunkte besonders hoher (Begrünung vor der Ernte) oder niedriger (nach der Bodenbearbeitung) Pflanzenbiomasse auf Ackerland zu identifizieren, um Ackerland und seine Ausdehnung von anderen Vegetationsformen fernerkundlich unterscheiden zu können. Anhand der NDVI Spektralprofile konnte Ackerland gut Wald, jedoch weniger gut von Gras- und Buschland unterschieden werden. Die geringe Auflösung (250m) der Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI Daten führte zu einem Mixed Pixel Effect, d.h. die Fläche eines Pixels beinhaltete häufig verschiedene Vegetationsformen in unterschiedlichen Anteilen, was deren Unterscheidung beeinträchtigte. Für die Entwicklung eines Echtzeit Monitoring Systems für die Ausdehnung des Ackerbaus wären höher auflösende NDVI Daten (z.B. Multispektralband, Hyperion EO-1 Sensor) notwendig, um kleinräumig eine bessere Differenzierung von Ackerland und natürlicher Rangeland-Vegetation zu erhalten. Die Entwicklung und der Einsatz solcher Methoden als Entscheidungshilfen für Land- und Ressourcennutzungsplanung könnte dazu beitragen, Produktions- und Entwicklungsziele der Borana Landnutzer mit nationalen Anstrengungen zur Eindämmung des Klimawandels durch Steigerung der Kohlenstofffixierung in Rangelands in Einklang zu bringen.

A method for assessing ecological values to reconcile multiple land use needs

We present a new method for ecologically sustainable land use planning within multiple land use schemes. Our aims were (1) to develop a method that can be used to locate important areas based on their ecological values; (2) to evaluate the quality, quantity, availability, and usability of existing ecological data sets; and (3) to demonstrate the use of the method in Eastern Finland, where there are requirements for the simultaneous development of nature conservation, tourism, and recreation. We compiled all available ecological data sets from the study area, complemented the missing data using habitat suitability modeling, calculated the total ecological score (TES) for each 1 ha grid cell in the study area, and finally, demonstrated the use of TES in assessing the success of nature conservation in covering ecologically valuable areas and locating ecologically sustainable areas for tourism and recreational infrastructure. The method operated quite well at the level required for regional and local scale planning. The quality, quantity, availability, and usability of existing data sets were generally high, and they could be further complemented by modeling. There are still constraints that limit the use of the method in practical land use planning. However, as increasing data become available and open access, and modeling tools improve, the usability and applicability of the method will increase.

Using social network analysis to identify key stakeholders in agricultural biodiversity governance and related land-use decisions at regional and local level

In 2013 the European Commission launched its new green infrastructure strategy to make another attempt to stop and possibly reverse the loss of biodiversity until 2020, by connecting habitats in the wider landscape. This means that conservation would go beyond current practices to include landscapes that are dominated by conventional agriculture, where biodiversity conservation plays a minor role at best. The green infrastructure strategy aims at bottom-up rather than top-down implementation, and suggests including local and regional stakeholders. Therefore, it is important to know which stakeholders influence land-use decisions concerning green infrastructure at the local and regional level. The research presented in this paper served to select stakeholders in preparation for a participatory scenario development process to analyze consequences of different implementation options of the European green infrastructure strategy. We used a mix of qualitative and quantitative social network analysis (SNA) methods to combine actors’ attributes, especially concerning their perceived influence, with structural and relational measures. Further, our analysis provides information on institutional backgrounds and governance settings for green infrastructure and agricultural policy. The investigation started with key informant interviews at the regional level in administrative units responsible for relevant policies and procedures such as regional planners, representatives of federal ministries, and continued at the local level with farmers and other members of the community. The analysis revealed the importance of information flows and regulations but also of social pressure, considerably influencing biodiversity governance with respect to green infrastructure and biodiversity.

Predicting bee community responses to land-use changes: Effects of geographic and taxonomic biases

Land-use change and intensification threaten bee populations worldwide, imperilling pollination services. Global models are needed to better characterise, project, and mitigate bees' responses to these human impacts. The available data are, however, geographically and taxonomically unrepresentative; most data are from North America and Western Europe, overrepresenting bumblebees and raising concerns that model results may not be generalizable to other regions and taxa. To assess whether the geographic and taxonomic biases of data could undermine effectiveness of models for conservation policy, we have collated from the published literature a global dataset of bee diversity at sites facing land-use change and intensification, and assess whether bee responses to these pressures vary across 11 regions (Western, Northern, Eastern and Southern Europe; North, Central and South America; Australia and New Zealand; South East Asia; Middle and Southern Africa) and between bumblebees and other bees. Our analyses highlight strong regionally-based responses of total abundance, species richness and Simpson's diversity to land use, caused by variation in the sensitivity of species and potentially in the nature of threats. These results suggest that global extrapolation of models based on geographically and taxonomically restricted data may underestimate the true uncertainty, increasing the risk of ecological surprises.

Estimates of global sources and sinks of carbon from land cover and land use changes

The distribution of sources and sinks of carbon over the land surface is dominated by changes in land use such as deforestation, reforestation, and agricultural management. Despite, the importance of land-use change in dominating long-term net terrestrial fluxes of carbon, estimates of the annual flux are uncertain relative to other terms in the global carbon budget. The interaction of the nitrogen cycle via atmospheric N inputs and N limitation with the carbon cycle contributes to the uncertain effect of land use change on terrestrial carbon uptake. This study uses two different land use datasets to force the geographically explicit terrestrial carbon-nitrogen coupled component of the Integrated Science Assessment Model (ISAM) to examine the response of terrestrial carbon stocks to historical LCLUC (cropland, pastureland and wood harvest) while accounting for changes in N deposition, atmospheric CO2 and climate. One of the land use datasets is based on satellite data (SAGE) while the other uses population density maps (HYDE), which allows this study to investigate how global LCLUC data construction can affect model estimated emissions. The timeline chosen for this study starts before the Industrial Revolution in 1765 to the year 2000 because of the influence of rising population and economic development on regional LCLUC. Additionally, this study evaluates the impact that resulting secondary forests may have on terrestrial carbon uptake. The ISAM model simulations indicate that uncertainties in net terrestrial carbon fluxes during the 1990s are largely due to uncertainties in regional LCLUC data. Also results show that secondary forests increase the terrestrial carbon sink but secondary tropical forests carbon uptake are constrained due to nutrient limitation.

Previous land use and climate influence differences in soil organic carbon following reforestation of agricultural land with mixed-species plantings

Reforestation of agricultural land with mixed-species environmental plantings (native trees and shrubs) can contribute to mitigation of climate change through sequestration of carbon. Although soil carbon sequestration following reforestation has been investigated at site- and regional-scales, there are few studies across regions where the impact of a broad range of site conditions and management practices can be assessed. We collated new and existing data on soil organic carbon (SOC, 0–30 cm depth, N = 117 sites) and litter (N = 106 sites) under mixed-species plantings and an agricultural pair or baseline across southern and eastern Australia. Sites covered a range of previous land uses, initial SOC stocks, climatic conditions and management types. Differences in total SOC stocks following reforestation were significant at 52% of sites, with a mean rate of increase of 0.57 ± 0.06 Mg C ha−1 y−1. Increases were largely in the particulate fraction, which increased significantly at 46% of sites compared with increases at 27% of sites for the humus fraction. Although relative increase was highest in the particulate fraction, the humus fraction was the largest proportion of total SOC and so absolute differences in both fractions were similar. Accumulation rates of carbon in litter were 0.39 ± 0.02 Mg C ha−1 y−1, increasing the total (soil + litter) annual rate of carbon sequestration by 68%. Previously-cropped sites accumulated more SOC than previously-grazed sites. The explained variance differed widely among empirical models of differences in SOC stocks following reforestation according to SOC fraction and depth for previously-grazed (R2 = 0.18–0.51) and previously-cropped (R2 = 0.14–0.60) sites. For previously-grazed sites, differences in SOC following reforestation were negatively related to total SOC in the pasture. By comparison, for previously-cropped sites, differences in SOC were positively related to mean annual rainfall. This improved broad-scale understanding of the magnitude and predictors of changes in stocks of soil and litter C following reforestation is valuable for the development of policy on carbon markets and the establishment of future mixed-species environmental plantings.

Land use and zoning map, Jackson Brook watershed, Morris County, New Jersey /

Shows "1988 land use."

Typologies and spatialization of agricultural production systems in Rondônia, Brazil: linking land use, socioeconomics and territorial configuration.

The current Amazon landscape consists of heterogeneous mosaics formed by interactions between the original forest and productive activities. Recognizing and quantifying the characteristics of these landscapes is essential for understanding agricultural production chains, assessing the impact of policies, and in planning future actions. Our main objective was to construct the regionalization of agricultural production for Rondônia State (Brazilian Amazon) at the municipal level. We adopted a decision tree approach, using land use maps derived from remote sensing data (PRODES and TerraClass) combined with socioeconomic data. The decision trees allowed us to allocate municipalities to one of five agricultural production systems: (i) coexistence of livestock production and intensive agriculture; (ii) semi-intensive beef and milk production; (iii) semi-intensive beef production; (iv) intensive beef and milk production, and; (v) intensive beef production. These production systems are, respectively, linked to mechanized agriculture (i), traditional cattle farming with low management, with (ii) or without (iii) a significant presence of dairy farming, and to more intensive livestock farming with (iv) or without (v) a significant presence of dairy farming. The municipalities and associated production systems were then characterized using a wide variety of quantitative metrics grouped into four dimensions: (i) agricultural production; (ii) economics; (iii) territorial configuration, and; (iv) social characteristics. We found that production systems linked to mechanized agriculture predominate in the south of the state, while intensive farming is mainly found in the center of the state. Semi-intensive livestock farming is mainly located close to the southwest frontier and in the north of the state, where human occupation of the territory is not fully consolidated. This distributional pattern reflects the origins of the agricultural production system of Rondônia. Moreover, the characterization of the production systems provides insights into the pattern of occupation of the Amazon and the socioeconomic consequences of continuing agricultural expansion.

High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data.

Understanding spatial patterns of land use and land cover is essential for studies addressing biodiversity, climate change and environmental modeling as well as for the design and monitoring of land use policies. The aim of this study was to create a detailed map of land use land cover of the deforested areas of the Brazilian Legal Amazon up to 2008. Deforestation data from and uses were mapped with Landsat-5/TM images analysed with techniques, such as linear spectral mixture model, threshold slicing and visual interpretation, aided by temporal information extracted from NDVI MODIS time series. The result is a high spatial resolution of land use and land cover map of the entire Brazilian Legal Amazon for the year 2008 and corresponding calculation of area occupied by different land use classes. The results showed that the four classes of Pasture covered 62% of the deforested areas of the Brazilian Legal Amazon, followed by Secondary Vegetation with 21%. The area occupied by Annual Agriculture covered less than 5% of deforested areas; the remaining areas were distributed among six other land use classes. The maps generated from this project ? called TerraClass - are available at INPE?s web site (http://www.inpe.br/cra/projetos_pesquisas/terraclass2008.php)

FLOOD RISK ASSESSMENT UNDER HISTORICAL AND PREDICTED LAND USE CHANGE USING CONTINUOUS HYDROLOGIC MODELING

Current procedures for flood risk estimation assume flood distributions are stationary over time, meaning annual maximum flood (AMF) series are not affected by climatic variation, land use/land cover (LULC) change, or management practices. Thus, changes in LULC and climate are generally not accounted for in policy and design related to flood risk/control, and historical flood events are deemed representative of future flood risk. These assumptions need to be re-evaluated, however, as climate change and anthropogenic activities have been observed to have large impacts on flood risk in many areas. In particular, understanding the effects of LULC change is essential to the study and understanding of global environmental change and the consequent hydrologic responses. The research presented herein provides possible causation for observed nonstationarity in AMF series with respect to changes in LULC, as well as a means to assess the degree to which future LULC change will impact flood risk. Four watersheds in the Midwest, Northeastern, and Central United States were studied to determine flood risk associated with historical and future projected LULC change. Historical single framed aerial images dating back to the mid-1950s were used along with Geographic Information Systems (GIS) and remote sensing models (SPRING and ERDAS) to create historical land use maps. The Forecasting Scenarios of Future Land Use Change (FORE-SCE) model was applied to generate future LULC maps annually from 2006 to 2100 for the conterminous U.S. based on the four IPCC-SRES future emission scenario conditions. These land use maps were input into previously calibrated Soil and Water Assessment Tool (SWAT) models for two case study watersheds. In order to isolate effects of LULC change, the only variable parameter was the Runoff Curve Number associated with the land use layer. All simulations were run with daily climate data from 1978-1999, consistent with the 'base' model which employed the 1992 NLCD to represent 'current' conditions. Output daily maximum flows were converted to instantaneous AMF series and were subsequently modeled using a Log-Pearson Type 3 (LP3) distribution to evaluate flood risk. Analysis of the progression of LULC change over the historic period and associated SWAT outputs revealed that AMF magnitudes tend to increase over time in response to increasing degrees of urbanization. This is consistent with positive trends in the AMF series identified in previous studies, although there are difficulties identifying correlations between LULC change and identified change points due to large time gaps in the generated historical LULC maps, mainly caused by unavailability of sufficient quality historic aerial imagery. Similarly, increases in the mean and median AMF magnitude were observed in response to future LULC change projections, with the tails of the distributions remaining reasonably constant. FORE-SCE scenario A2 was found to have the most dramatic impact on AMF series, consistent with more extreme projections of population growth, demands for growing energy sources, agricultural land, and urban expansion, while AMF outputs based on scenario B2 showed little changes for the future as the focus is on environmental conservation and regional solutions to environmental issues.

Water Demand and Allocation in the Mara River Basin, Kenya/Tanzania in the Face of Land Use Dynamics and Climate Variability

The Mara River Basin (MRB) is endowed with pristine biodiversity, socio-cultural heritage and natural resources. The purpose of my study is to develop and apply an integrated water resource allocation framework for the MRB based on the hydrological processes, water demand and economic factors. The basin was partitioned into twelve sub-basins and the rainfall runoff processes was modeled using the Soil and Water Assessment Tool (SWAT) after satisfactory Nash-Sutcliff efficiency of 0.68 for calibration and 0.43 for validation at Mara Mines station. The impact and uncertainty of climate change on the hydrology of the MRB was assessed using SWAT and three scenarios of statistically downscaled outputs from twenty Global Circulation Models. Results predicted the wet season getting more wet and the dry season getting drier, with a general increasing trend of annual rainfall through 2050. Three blocks of water demand (environmental, normal and flood) were estimated from consumptive water use by human, wildlife, livestock, tourism, irrigation and industry. Water demand projections suggest human consumption is expected to surpass irrigation as the highest water demand sector by 2030. Monthly volume of water was estimated in three blocks of current minimum reliability, reserve (>95%), normal (80–95%) and flood (40%) for more than 5 months in a year. The assessment of water price and marginal productivity showed that current water use hardly responds to a change in price or productivity of water. Finally, a water allocation model was developed and applied to investigate the optimum monthly allocation among sectors and sub-basins by maximizing the use value and hydrological reliability of water. Model results demonstrated that the status on reserve and normal volumes can be improved to ‘low’ or ‘moderate’ by updating the existing reliability to meet prevailing demand. Flow volumes and rates for four scenarios of reliability were presented. Results showed that the water allocation framework can be used as comprehensive tool in the management of MRB, and possibly be extended similar watersheds.

Analysis of Land Use Change as a Method of Predicting Water Demands in an Urbanizing Environment: Redland, Miami-Dade County, Florida

The fluctuation in water demand in the Redland community of Miami-Dade County was examined using land use data from 2001 and 2011 and water estimation techniques provided by local and state agencies. The data was converted to 30 m mosaicked raster grids that indicated land use change, and associated water demand measured in gallons per day per acre. The results indicate that, first, despite an increase in population, water demand decreased overall in Redland from 2001 to 2011. Second, conversion of agricultural lands to residential lands actually caused a decrease in water demand in most cases while acquisition of farmland by public agencies also caused a sharp decline. Third, conversion of row crops and groves to nurseries was substantial and resulted in a significant increase in water demand in all such areas converted. Finally, estimating water demand based on land use, rather than population, is a more accurate approach.

Climate, Land Use and Hydrologic Sensitivities of Stormwater Quantity and Quality in Complex Coastal Urban Watersheds

The study analyzed hydro-climatic and land use sensitivities of stormwater runoff and quality in the complex coastal urban watershed of Miami River Basin, Florida by developing a Storm Water Management Model (EPA SWMM 5). Regression-based empirical models were also developed to explain stream water quality in relation to internal (land uses and hydrology) and external (upstream contribution, seawater) sources and drivers in six highly urbanized canal basins of Southeast Florida. Stormwater runoff and quality were most sensitive to rainfall, imperviousness, and conversion of open lands/parks to residential, commercial and industrial areas. In-stream dissolved oxygen and total phosphorus in the watersheds were dictated by internal stressors while external stressors were dominant for total nitrogen and specific conductance. The research findings and tools will be useful for proactive monitoring and management of storm runoff and urban stream water quality under the changing climate and environment in South Florida and around the world.