Changes in landuse/landcover patterns and human population growth in the Lake Chivero catchment,Zimbabwe

The land use and land cover pattern of a region is a consequence of natural and socio-economic factors and their utilization by man in time and space. In this study, we hypothesized that land use and land cover change patterns in the Lake Chivero catchment, Zimbabwe, were related to its human population dynamics. Using nonparametric correlation coefficients (Spearman’s rho, ρ), we found that bareland, cropland and built-up land had positive relations with human population growth of ρ = 0.7, ρ = 0.9 and ρ = 1, respectively. Grassland/shrubland, water and forest, on the other hand, had a negative relationship with human population growth of ρ = ?0.9, ρ = ?0.7 and ρ = ?0.667, respectively. However, these relationships were only significant (p < 0.05) for cropland, grassland/shrubland and built-up land. Human population dynamics in the Lake Chivero catchment could be one of the major drivers of land use and land cover change in the catchment between 1986 and 2014.     

湖北省植被净初级生产力时空变化特征及其影响因素分析

基于改进的光能利用率模型,本文利用MODIS数据和同期气象数据估算分析了湖北省2001—2012年间植被净初级生产力(NPP)的时空变化特征并借助多元统计分析方法定量探究自然因素(气温、降水量、太阳辐射)和人为因素(土地覆被/土地利用、粮食播种面积、粮食产量、人口数量)对NPP变化的影响。结果表明:1)湖北省NPP呈波动上升趋势,年际增加趋势为8.19 g/m~2·a;2) NPP空间分布差异明显,呈现西高东低、北高南低、从西向东逐渐递减的态势;3)造林累计面积和太阳辐射变化是影响NPP变化的主要因素。     

Modeling for Prediction of Land Cover Changes Based on Bio-physical and Human Factors in Zagros Mountains, Iran

The rapid population growth and ongoing development activities has resulted in natural resources demolition. However, the dynamics of the natural resources in relation to different biophysical and socio-economic factors are still remains poorly understood. The present study investigates the basic natural resources i.e. forest, rangeland and surface water bodies’ status using satellite data for the years 1990, 1998, and 2006, and their change detection in relation to biophysical and socio-economic factors. Monitoring land-use/cover change detection using remotely sensed data has been a well recognized technique. The analysis of change detection revealed eleven important land cover changes, which occurred during the past 16 years (1990–2006) in the region. The rate of land cover change was observed to vary across the sub periods and a general decline of forest cover and increase in rangelands and water bodies was observed. Logistic regression model was employed to analyze the relationship between changes and explanatory factors. The land cover change results and logistic models developed in this study are useful in supporting natural resources management efforts and provide useful information for managers/policy makers in formulation of sustainable management strategies for the region.     

Geospatial Approach in Assessing Agro-Climatic Suitability of Soybean in Rainfed Agro-Ecosystem

The present study demonstrated the methodology to assess agro-climatic suitability of the soybean crop through integration of crop suitability based on FAO framework of land evaluation and biophysical (water limited) yield potential in the rainfed agro-ecosystem. A long term climatic database (1980–2003) was prepared to compute decadal rainfall and temperature variations of 13 IMD stations in part of Madhya Pradesh state. The climatic database was used in soil water balance software–BUDGET to compute crop specific length of growing period (LGP) and biophysical production potential such as water limited crop yield potential of each soil types for soybean crop. Water limited crop yield potential of soils were found to be varied from 33 to 100 and LGP ranged from 65 to 180 days in the area. FAO based land suitability was analyzed in association with the water limited yield potential for better appraisal of land potential and assess their suitability in rainfed area. FAO based land suitability indicated 2.45 % area as highly suitable and 57.49 % area as moderately suitable. However, integration of water limited crop yield potential with FAO based land suitability lead to agro-climatic suitability analysis indicated 17.60 % and 40.03 % area, respectively as highly suitable and moderately suitable. FAO based land evaluation showed 88.13 % of plains as moderately suitable whereas agro-climatic suitability indicated only 47.79 %. Agro-climatic suitability analysis revealed undulating plateau and undulating plains as most suitable for soybean crop.     

Estimating spatial variations of total evaporation using multispectral sensors within the uMngeni catchment,South Africa

Total evaporation is of importance in assessing and managing long-term water use, especially in water-limited environments. Therefore, there is need to account for water utilisation by different land uses for well-informed water resources management and future planning. This study investigated the feasibility of using multispectral Landsat 8 and moderate resolution imaging spectroradiometer (MODIS) remote sensing data to estimate total evaporation within the uMngeni catchment in South Africa, using surface energy balance system. The results indicated that Landsat 8 at 30 m resolution has a better spatial representation of total evaporation, when compared to the 1000 m MODIS. Specifically, Landsat 8 yielded significantly different mean total evaporation estimates for all land cover types (one-way ANOVA; F_4.964?=?87.011, p < 0.05), whereas MODIS failed to differentiate (one-way ANOVA; F_2.853?=?0.125, p = 0.998) mean total evaporation estimates for the different land cover types across the catchment. The findings of this study underscore the utility of the Landsat 8 spatial resolution and land cover characteristics in deriving accurate and reliable spatial variations of total evaporation at a catchment scale.     

Modelling Changes in Land Cover Patterns in Mtunzini,South Africa Using Satellite Imagery

Land is the basic resource that is needed by man in order to survive: It provides humans with living space, nutrition and energy resources. The rapid growth of the human population, climate change and pollution on a catastrophic scale has caused the quality of land resources to be compromised. Remote sensing is a useful tool in land cover change detection providing information to decision makers. The aim of this study was to evaluate land cover changes in the Mtunzini area in South Africa over the past 18 years; determine why changes have occurred and predict land cover patterns for future years. In this study a supervised classification was used to detect land cover classes of the Mtunzini area from 1992 to 2009 using four Landsat images in the time series analysis. The supervised classification had an accuracy of 80.80 % which was used to model land cover changes. Commercial sugar cane and forest plantation classes increased throughout the time series. It was estimated in the modelling procedure that bushland (42.11 %) and bare soil (35 %) would be changed to commercial sugar cane. This is indicative of the expanding agriculture sector in Mtunzini. Natural vegetation is predicted to be disturbed: 18 % of bushland and 15.07 % of dense bush are expected to be replaced by rural dwellings. This is owing to a potential increase in the rural population and a reduced local economic growth. This study highlights the need for increased vigilance of the forestry industry and commercial sugar cane farms which may be encroaching on natural vegetation and livelihoods of local residents. Strategic planning and proper management of natural vegetation types is needed as these land cover types are decreasing rapidly.     

Estimating crop net primary production using national inventory data and MODIS-derived parameters

National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux between land and atmosphere. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale as well as national and continental scales. Existing satellite-based NPP products tend to underestimate NPP on croplands. An Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP over large multi-state regions. The method is documented here and evaluated for corn (Zea mays L.) and soybean (Glycine max L. Merr.) in Iowa and Illinois in 2006 and 2007. The method includes a crop-specific Enhanced Vegetation Index (EVI), shortwave radiation data estimated using the Mountain Climate Simulator (MTCLIM) algorithm, and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that corresponds to the Cropland Data Layer (CDL) land cover product. Results from the modeling framework captured the spatial NPP gradient across croplands of Iowa and Illinois, and also represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 917 g C m⁻² yr⁻¹ and 409 g C m⁻² yr⁻¹, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Site comparisons with flux tower data show AgI-LUE NPP in close agreement with tower-derived NPP, lower than inventory-based NPP, and higher than MOD17A3 NPP. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.     

皖江城市带植被NPP时空变化及其气候响应

针对现有植被净初级生产力研究对城市圈、城市带尺度缺乏关注的问题,基于MODIS遥感数据、地面气象资料等,利用改进的CASA模型,结合回归分析、相关分析等方法探究了2000—2013年皖江城市带植被NPP的时空变化及其对气候因子的响应,为区域生态环境质量评价提供参考。结果表明:近14年来,皖江城市带植被NPP总体呈增加趋势;不同土地利用类型NPP差异显著,林地草地耕地建设用地未利用土地水体;年NPP均值呈现由南部向西北部减少的空间分布特征;植被NPP年际变化率较小,介于±10gC·m-2·a-1范围内;温度是影响研究区植被NPP时空变化的主要气候因子。     

Application of partial least squares regression and WetSpa model to determine factors controlling sediment yield in Chardavol watershed,Iran

This paper examines the effects of watershed complexity in terms of physiography and land use on the specific sediment yield of the Chardavol watershed (1012.946 km²) in Iran. First, specific sediment yield was simulated using spatially distributed hydrological WetSpa model, then the influential factors such as morphometric variables, land-use composition and pattern and soil properties of the watershed were calculated at the sub-watershed scale. Due to the inter-reliant of these watershed characteristics, a partial least squares regression (PLSR) was used to illustrate the relationship between the specific sediment yield and data of 15 selected watershed characteristics. The results showed that the land-use composition and soil properties had the maximum effects on the specific sediment yield and clarified 79% of the variation in the specific sediment yield. Regarding the availability of digital spatial database over the watershed, this simple PLSR procedure could be applied in different watersheds.     

An assessment of gully erosion along major armoured roads in south-eastern region of South Africa: a remote sensing and GIS approach

An assessment of gully erosion along road drainage-release sites is critical for understanding the contribution of roads to soil loss and for informed land management practices. Considering that road-related gully erosion has traditionally been measured using field methods that are expensive, tedious and limited spatially as well as temporally, it is important to identify affordable, timely and robust methods that can be used to effectively map and estimate the volume of gullies along the road networks. In this study, gullies along major roads were identified from remotely sensed data sets and their volumes were estimated in a Geographic Information Systems environment. Also, the biophysical and climatic factors such as vegetation cover, the road contributing surface area, the gradient of the discharge hillslope and rainfall were derived from remotely sensed data sets using Geographic Information Systems techniques to find out whether they could explain the morphology of gullies that existed in this area. The results of this study indicate that hillslope gradient (R²?=?0.69, α = 0.00) and road contributing surface area (R²?=?0.63, α = 0.00) have a strong influence on the volume of gullies along the major roads in the south-eastern region of South Africa, as might have been expected. However, other factors such as vegetation cover (R² = 0.52, α = 0.00) and rainfall (R² = 0.41 and α = 0.58) have a moderately weaker influence on the overall volume of gullies. Overall, the findings of this study highlight the importance of using remote sensing and Geographic Information Systems technologies in investigating gully erosion occurrence along major roads where detailed field work remains a challenge.     

Estimating net primary productivity in tropical forest plantations in India using satellite-driven ecosystem model

Net Primary Productivity (NPP) is a significant biophysical vegetation variable to understand the spatio-temporal distribution of carbon and source-sink nature of the ecosystem. This study was carried out in a forest plantation area and aimed to (i) estimate the spatio-temporal patterns of NPP during 2009 and 2010 using Carnegie-Ames-Stanford Approach [CASA] model and (ii) study the effects of climate variables on the NPP using generalized linear modelling (GLM) approach. The total annual NPP varied from 157.21 to 1030.89 gC m^?2 yr^?1 for the year 2009 and from 154.36 to 1124.85 g C m^?2 yr^?1 for the year 2010. The annual NPP was assessed across four major plantation types, where maximum NPP gain (106 and 139 g C m^?2 yr^?1 ) in October was noticed in teak (Tectona grandis) and minimum (77 and 109 g C m^?2 yr^?1 ) in eucalyptus (Eucalyptus hybrid) during 2009 and 2010.The validation, using field-estimated NPP, showed under-estimation of modelled NPP, with maximum MAPE of 34% for eucalyptus and minimum of 13% for teak. The dominant influence of precipitation on the NPP was revealed by GLM explaining more than 20% of variation. CASA model efficiently estimated the annual NPP of plantations. The accuracy could be improved further with inclusion of higher resolution data.     

A comparative assessment of catchment runoff generation and forest productivity in a semi-arid environment

Understanding the dynamic interactions between forest ecosystems and water in the Mediterranean region is essential for increasing ecosystem services. Even if many studies were implemented to analyse the variations of water and net primary productivity (NPP) in the last decade, this is still an important research question especially for the Eastern Mediterranean, where the research attempts are limited. The main objective of this study was to carry out a comparative analysis of catchment runoff generation and forest NPP and to reveal their temporal dynamics at basin scale in a semi-arid Mediterranean environment. The methodology consisted three steps: (i) estimating catchment runoff generation by implementing process-based J2000 modelling suite, (ii) modelling NPP of the land cover/use types by adapting an ecosystem-process model (BIOME-Biogeochemical cycles) and (iii) assessing the spatio-temporal variability of NPP and runoff dynamics by incorporating the modelling results with multiple regression analysis. Model simulations showed that temperature highly contributed to NPP variations of needle-leaf forests and grasslands. The multiple regression analysis also indicated that runoff was influenced by elevation, precipitation and forest cover. This relationship showed that the inter-annual variability in forest NPP would relate to the variations in runoff distribution across a small Mediterranean subcatchment.     

Approaches for Linking People,Place, and Environment for Human Dimensions Research

Abstract

Much of the human dimensions of environmental change research emphasize the mapping and modeling of land use and land cover patterns over space and time, and the linkages between people, place, and environment as proximate and distal forces of landscape dynamics. Spatial digital technologies, framed within a GIScience (GISc) context, figure prominently in the characterization of land use and land cover through remote sensing technologies, and in the assessment of social and demographic factors and local and regional site and situation considerations achieved through global positioning systems, data visualizations, and spatial and statistical analyses. Here, we describe some fundamental approaches for linking data across thematic domains, essential for the study of human‐environment interactions. The goal is to generate compatible data sets that extend across social, biophysical, and geographical domains so that the causes and consequences of land use and land cover dynamics might be explored within a spatially‐explicit context.     

Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales.     

Remote sensing-based assessment of veld fire trends in multiple interwoven land tenure systems in Zimbabwe

The study used Landsat imagery, MODIS fire data and in situ meteorological data to determine emerging fire trends in interwoven multiple tenure systems in Zimbabwe. Remote sensing enabled fire trends to be determined across terrain and official records barriers. The number of fires and area burnt increased from 2001 up to 2009 then fluctuated across tenure systems. Fire events rose from 9 to 80 per year in some of the tenure systems. Complex relationships among number of fires, area burnt and weather variables within and across tenure systems were identified. The fire situation was responsive to intervention; the positive fire trends were reversed from 2009 onwards. Projected trends show that fire events could be reduced to negative values in three systems, while in two they could double by 2026. The veld fire problem could be eliminated if a holistic approach is adopted to tackle it across sectoral and land tenure divides.     

Bayesian networks and agent-based modeling approach for urban land-use and population density change: a BNAS model

Land-use change models grounded in complexity theory such as agent-based models (ABMs) are increasingly being used to examine evolving urban systems. The objective of this study is to develop a spatial model that simulates land-use change under the influence of human land-use choice behavior. This is achieved by integrating the key physical and social drivers of land-use change using Bayesian networks (BNs) coupled with agent-based modeling. The BNAS model, integrated Bayesian network–based agent system, presented in this study uses geographic information systems, ABMs, BNs, and influence diagram principles to model population change on an irregular spatial structure. The model is parameterized with historical data and then used to simulate 20 years of future population and land-use change for the City of Surrey, British Columbia, Canada. The simulation results identify feasible new urban areas for development around the main transportation corridors. The obtained new development areas and the projected population trajectories with the“what-if” scenario capabilities can provide insights into urban planners for better and more informed land-use policy or decision-making processes.     

Vegetation response to intensive commercial horticulture and environmental changes within watersheds in central highlands,Kenya, using AVHRR NDVI data

Expansion and heterogeneous clustering of commercial horticulture within the central highlands of Kenya after the mid-1990s impact watersheds and the sustainable resource management. This is distressing since climate conditions for world horticultural regions are projected to change, making such farming extremely difficult and costly to the environment. To understand the scope of impact on vegetation, the study evaluated (1) interannual variability in averaged normalized difference vegetation index (NDVI); (2) trends in average annual NDVI before and after 1990 – the presumed onset of rapid horticulture; and (3) relationship between the average annual NDVI and large-scale commercial farms, population density, and mean annual rainfall in subwatersheds. Time-series analysis of long-term Global Inventory Modeling and Mapping Studies NDVI data were analyzed as indicator of vegetation condition. NDVI trends before 1990s (1982–1989) and after 1990s (1990–2006) were evaluated to determine the slope (sign), and the Spearman’s correlation coefficient (strength). Overall, results show considerable variations in vegetation condition due largely to mixed factors including intensive farming activities, drought, and rainfall variation. Statistical analysis shows significant differences in slopes before 1990 and after 1990 (p < 0.05 and p < 0.1 respectively). Negative (decline) trends were common after 1990, linked to increased commercial horticulture and related anthropogenic disturbances on land cover. There was decline in vegetation over densely populated subwatersheds, though low NDVI values in 1984 and 2000 were the effect of severe droughts. Understanding the linkage between vegetation responses to the effects of human-induced pressure at the subwatershed scale can help natural resource managers approach conservation measures more effectively.     

Regional land-use allocation using a coupled MAS and GA model: from local simulation to global optimization,a case study in Caidian District,Wuhan, China

China is facing the pressures of both rapid economic development and environmental protection, and land-use allocation optimization is an important way to manage the conflicts between these pressures and to achieve sustainable development. Optimization of land-use allocation is a nonlinear multiobjective spatial optimization problem, and a purely local simulation model or global optimization model is insufficient to solve it. It is essential to bridge the gap between the two models through the combination of top-down and bottom-up approaches. This study integrates a multiagent system (MAS) that simulates the behaviors of land-use stakeholders with regard to their choices of specific locations, with a genetic algorithm (GA) that simultaneously evaluates and optimizes land-use configurations to meet various regional development objectives. The model is expected to achieve the optimization of land use in terms of the composition and spatial configuration. Caidian District, Wuhan, China, was chosen as the study area to test the model in this paper. The results show that the performance of the coupled model is superior to a pure GA model or MAS model. The optimal configuration improves on the economic output, spatial compactness, and carbon storage of the current configuration and promotes sustainable regional land-use development from the local scale to the regional scale.     

Retrieving spatial variations of land surface temperatures from satellite data–Cairo region,Egypt

Cairo region is characterized by a range of physiographic features, including: flat agricultural lands, bare sandy deserts, highlands, calcareous terrains and urban land use. A time series data-set (300 images) acquired from the Moderate Resolution Imaging Spectroradiometer for the period July 2002–June 2015 were utilized to retrieve the spatial variations in the mean land surface temperature (LST) for the above-mentioned surface features. Results showed that vegetation, topography and surface albedo have negative correlations with LST. Vegetation/LST correlation has the maximum regression coefficient (R² = 0.68) and albedo/LST has the minimum (R² = 0.03). Cultivated lands reveal the lowest mean LST (<32 °C), whereas industrial lands exhibit the highest LST (>40 °C) of Cairo region. There is a considerable urban heat island formed at Helwan south of Cairo, where heavy industries are settled. Industrial activities raised the mean LST of the region by at least 4 °C than the surrounding urban lands.     

利用改进的CASA模型估算城市尺度NPP——以徐州城区为例

针对城市地物的特点,本文基于两种不同空间分辨率的遥感数据,利用原始与改进后的CASA估算了徐州城区的NPP,探讨了CASA模型的改进和遥感影像的空间分辨率对城市尺度NPP估算结果的影响.研究结果表明:①城市建筑用地对城市NPP的估算结果有较大的影响.改进的CASA模型将建筑用地的光合有效辐射(FPAR)归零,其估算值降...