Suitability mapping for rice cultivation in Benue State,Nigeria using satellite data

ABSTRACT

With rising population, decline in soil productivity and land-based conflicts, the per-capita land availability for cultivation is rapidly decreasing within Benue State, a largely agrarian and small-holder setting. This study attempts a local-level support for the actualisation of Sustainable Development Goal Number 2 (“end hunger, achieve food security and improved nutrition, and promote sustainable agriculture”) by 2030. Using Multi-Criteria Decision Making (MCDM) method, remote sensing data from Climate Research Unit (CRU) and in-situ data from Nigeria Meteorological Agency (NIMET) were analyzed by GIS techniques to map the suitability of rice cultivation in the study area, with the integration of Normalized Difference Vegetation Index (NDVI), land cover, slope, temperature, precipitation and soil parameters (cation exchange capacity, pH, bulk density, organic carbon). We apply the various statistical parameters that include mean spatial NDVI; correlation coefficient, standard deviation and Root Mean Square (RMS) between CRU and NIMET data. Spatial regression trend analysis is conducted between CRU precipitation and NDVI and between CRU temperature and NDVI from 1985 to 2015. The results reveal that NDVI in highly suitable rice planting regions is higher than marginally suitable regions except in the months of October and November, which shows that the highly suitable regions will yield better than the marginally suitable regions during the dry season. Additionally, NDVI is seasonally bimodal in response to precipitation, meaning that vegetation vigor is more dependent on precipitation than temperature. Finally, the correlation coefficient, standard deviation and RMS between CRU and NIMET precipitation data shows 0.42, 108, and 110, respectively, while these three factors between CRU and NIMET temperature data shows 0.88, 1.60, and 0.86, respectively. In conclusion, the MCDM approach reveals that upland is more suitable for rice cultivation in Benue State when comparing with the area provided by the Global Land Cover and National Mappings Organization (GLCNMO) data.     

利用时变重力数据反演地下水储量变化

为了反映中国陆地区域地下水储量的变化情况,该文利用2003—2019年间GRACE、GRACE-FO重力卫星数据,对8个典型区域地下水储量变化情况进行了研究,并结合气象资料从相关性上分析各区域地下水储量显著变化的原因。结果表明,中国东南大部分地区地下水储量逐年增加,地下水主要靠降水补给;华北平原等人口稠密区地下水亏损严重,研究时段内持续呈下降趋势,降水仅能缓解地下水储量的亏损速度;天山山脉、念青唐古拉山脉等冰川区质量变化和温度异常的相关性较好,这些地区的质量亏损可能是冰川消融引起的。     

A space–time parallel framework for fine-scale visualization of pollen levels across the Eastern United States

ABSTRACT

Allergic rhinitis (hay fever) resulting from seasonal pollen affects 15–30% of the population in the United States, and can exacerbate several related conditions, including asthma, atopic eczema, and allergic conjunctivitis. Timely monitoring, accurate prediction, and visualization of pollen levels are critical for public health prevention purposes, such as limiting outdoor exposure or physical activity. The low density of pollen detecting stations and complex movement of pollen represent a challenge for accurate prediction and modeling. In this paper, we reconstruct the dynamics of pollen variation across the Eastern United States for 2016 using space–time interpolation. Pollen levels were extracted according to a stratified spatial sampling design, augmented by additional samples in densely populated areas. These measurements were then used to estimate the space–time cross-correlation, inferring optimal spatial and temporal ranges to calibrate the space–time interpolation. Given the computational requirements of the interpolation algorithm, we implement a spatiotemporal domain decomposition algorithm, and use parallel computing to reduce the computational burden. We visualize our results in a 3D environment to identify the seasonal dynamics of pollen levels. Our approach is also portable to analyze other large space–time explicit datasets, such as air pollution, ash clouds, and precipitation.     

时序变差函数特征驱动下的月平均降水空间分布模拟

高精度降水场是水文、气象以及环境分析的重要数据支撑,直接影响相关服务的准确性。传统降水分布模拟大多依赖站点空间维的驱动因素,而忽略了降水时序变化特征对其空间分布的影响。使用2015—2017年中国湖北省83个国家气象观测站点和28个省级观测站点近3 a月平均累积降水资料,通过相关性分析,引入站点降水时序理论变差函数模型的拱高值(C)和块金值(C₀)作为影响因素,使用地理加权回归(geographically weighted regression, GWR)建立湖北省月平均降水分布模型。结果表明:（1）各站点降水的时序变差函数曲线与降水的季节性基本吻合。站点时序理论变差函数模型中,有25.3%能够在4个月内达到平稳,36.14%在6个月内达到平稳。（2）站点降水时序理论变差函数模型的C和C₀与逐年12月平均累积降水在0.01水平（双侧）上显著相关,平均相关系数分别为0.745和0.526,大于地理位置和高程对降水的影响。（3）引入C和C₀ 有助于提升GWR模型的整体拟合优度和插值精度。对比仅使用经纬度的GWR模型和引入时序理论变差函数特征的GWR模型,3 a平均整体拟合优度从0.852提升至0.912。验证集站点插值精度评价显示,3 a绝对误差、均方根误差和平均绝对百分误差下降幅度均大于60%。因此,引入时序理论变差函数特征的时空GWR模型能够获得较高精度的降水模拟结果,更适合具有丰富历史降水资料地区的降水空间分布估算。     

SURVEYING AND MAPPING IN CANADA

Abstract

In a country of the size of Canada, the third largest in the world, comprising more than three and a half million square miles and extending through eighty degrees in longitude and a range of latitude from 42° to the North Pole, there is every variety of topography. There are the characteristic accidented regions of the Maritime Provinces and the lower St. Lawrence, vast treeless areas in the Canadian West, and park lands spreading out northwards to merge eventually into the forests, which again give way to the open northern plains that lie towards the Arctic. There are level fertile expanses, hilly areas, marshes, rocky regions of low relief, and localities that display mountainous scenery rivalling anything that exists in other parts of the world. In some places industry has taken its firm foothold with a consequent density of population. In the agricultural areas the population spreads itself out more thinly.     

Time series analysis of vegetation-cover response to environmental factors and residential development in a dryland region

Land-use changes as a result of residential development often lead to degradation and alter vegetation cover (VC). Although these are worldwide phenomena, sufficient knowledge about anthropogenic effects caused by various populated areas in dryland ecosystems is lacking. This study explored anthropogenic development in rural areas and its effects on the conservation of protected areas in drylands, focusing on the change in VC, the reasons, extent, and the drivers of change. We propose a novel framework for exploring VC change (VCC) as a function of environmental and human-driven factors including different types of populated areas in drylands. As a case study, we used a 30-year time series of Landsat satellite images over the arid region of Israel to analyze spatiotemporal VCC. The temporal analysis involved the Contextual Mann-Kendall significance test and spatial analysis to model clustering of VCC. A Gradient Boosted Regression machine learning algorithm was applied to study the relative influence of environmental and human-driven factors on VCC. In addition, we used ANOVA to examine differences between the effects of three types of populated areas on the spatiotemporal trends of VC. The results show that the most influential environmental variable on VCC was elevation (relative contribution of 17%), followed by slope (14.8%) and distance from populated areas (14.6%). Moreover, different types of populated areas affected VC differently with varying distances from residential centroids. The nature reserves increased VC positively and significantly, while livestock settlements had a negative effect. Change in vegetation was mostly confined to the stream network and occurred in lower elevations. The study demonstrates how different land-use practices alter the landscape in terms of VC and differ in their extents, patterns, and effects. With the expected growth in population and residential development worldwide, the proposed framework may assist conservation managements and policy makers in minimizing environmental degradation in drylands.     

稀疏植被净初级生产力时空变化及气象因素关系分析

本文探讨了2001-2018年古尔班通古特沙漠植被NPP时空格局,基于改进的CASA模型,采用空间分析、相关性分析及地理探测器模型等方法,揭示了研究区NPP气候驱动因子及其影响。结果表明:①古尔班通古特沙漠近18年植被NPP变化总体呈现波动增加趋势,增速为0.56 gC· a^-1,NPP均值为46.90 gC· m^-2· a^-1;②2001-2018年,年均NPP整体呈西低东高、北低南高的空间分布格局,但从动态上而言,基本呈现沙漠腹地较稳定、四周较活跃的格局;③古尔班通古特沙漠植被NPP主要受降水因子的影响,与降水、气温因子均呈正相关关系,从各因子驱动力分析而言,降水因子(0.614 4)为限制荒漠植被生长的主导因素。     

Establishing Classification and Hierarchy in Populated Place Labeling for Multiscale Mapping for The National Map

Current standards for federal mapping call for use of the Geographic Names Information System (GNIS) point layer for placement of United States populated place labels. However, this point layer contains limited classification information and hierarchy information, resulting in problems of map quality for database-driven, multi-scale, reference mapping, such as maps served by The National Map Viewer from USGS. Database-driven mapping often relies simply on what labels fit best in the map frame. Our research investigates alternative sources for labeling populated places, including polygons defined by the U.S. Census Bureau, such as incorporated place, census designated place (CDP), and economic place. Within each of these polygon layers we investigate relevant attributes from the decennial and economic censuses, such as population for incorporated places and CDPs, and the number of employees for economic places. The data selected are available for the entire country to serve national mapping requirements. This combination of data allows a more refined classification of populated places on maps that better represents relative importance. Visual importance on maps through scale should derive from more than simply residential population, but also economic importance, though comparison is made to this simpler case. We differentiate a fourth category of GNIS populated place points, essentially “neighborhoods” and related features—which are not incorporated places, CDPs, nor economic places. Populated places in this fourth class do not have federally defined boundaries, necessitating an alternative method for determining hierarchy in label presentation through scale.     

Coupling of remote sensing data and environmental-related parameters for dengue transmission risk assessment in Subang Jaya,Malaysia

In Malaysia, the endemic level of dengue fever (DF) has already changed morbidity indicators, and the magnitude of these incidences in the last few years has surpassed the incidences of all other diseases of compulsory notification. The reasons for the dramatic emergence of DF are complex and not well understood. There are many factors that contribute to the epidemiological conditions that favour viral transmission by the main mosquito vector. This study, therefore, is filling this gap by analysing the impact of dengue incidence at a local (Subang Jaya) scale using environmental factors. Meteorological data and land-use pattern were consolidated using geographic information system (GIS) and its components as an analytical tool. We have shown that weather variables (relative humidity, temperature and precipitation) have significant correlation with DF incidence with seasonal variation. Besides land-use pattern, DF incidence shows the higher distribution in the residential area, followed by commercial and industrial area. This is due to the higher population density in residential area as well as favourable places for the breeding of dengue-carrying Aedes mosquitos created by humans in the residential area, especially one-storey houses. The analysis on the trends of DF incidence towards various housing types indicate that most of the victims’ houses fall into interconnection houses and mixed houses types compared to the independent houses area. The outcome driven from this analysis suggested that each character of the environmental factors has their own risk towards dengue incidence. In line with that, it is possible to develop a dynamic model of DF transmission using the knowledge produced by this comprehensive time series data and the results provided by the different analyses.     

区域大气加权平均温度模型确定及其应用

大气加权平均温度Tm是决定GPS水汽反演精度的关键参数,不同地区的Tm具有区域性差异.本文基于河南省Nanyang探空站2015—2018年的气象数据,建立了适用于河南亚热带季风气候地区的单因子和多因子的大气加权平均温度Tm模型,同时按照四季划分构建了季节模型,并对比经验模型分析其精度.结果表明,新建立的加权平均温度模...     

Global analysis of time-lag and -accumulation effects of climate on vegetation growth

Climate dominantly controls vegetation over most regions at most times, and vegetation responses to climate change are often asymmetric with temporal effects. However, systematic analysis of the time-lag and time-accumulation effects of climate on vegetation growth, has rarely been conducted, in particular for different vegetation growing phases. Thus, this study aimed to leverage normalized difference vegetation index (NDVI) to determine the spatiotemporal patterns of climatic effects on global vegetation growth considering various scenarios of time-lag and/or accumulation effects. The results showed that (i) climatic factors have time-lag and -accumulation effects as well as their combined effects on global vegetation growth for the whole growing season and its subphases (i.e., the growing and senescent phases). However, these effects vary with climatic factors, vegetation types, and regions. Compared with those of temperature, both precipitation and solar radiation display more significant time-accumulation effects in the whole growing season worldwide, but behave differently in the growing and senescent phases in the middle-high latitudes of the Northern Hemisphere; (ii) compared to the scenario without time effects, considering time-lag and -accumulation effects as well as their combined effects increased by 17 %, 15 %, and 19 % the overall explanatory power of vegetation growth by climate change for the whole growing season, the growing phase, and senescent phase, respectively; (iii) considering the time-lag and -accumulation effects as well as their combined effects, climate change controls 70 % of areas with a significant NDVI variation from 1982 to 2015, and the primary driving factor was temperature, followed by solar radiation and precipitation. This study highlights the significant time-lag and -accumulation effects of climatic factors on global vegetation growth. We suggest that these effects need to be incorporated into dynamic vegetation models to better understand vegetation growth under accelerating climate change.     

Satellite-observed changes in terrestrial vegetation growth trends across the Asia-Pacific region associated with land cover and climate from 1982 to 2011

The Asia-Pacific (AP) region has experienced faster warming than the global average in recent decades and has experienced more climate extremes, however little is known about the response of vegetation growth to these changes. The updated Global Inventory Modeling and Mapping Studies third-generation global satellite Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index dataset and gridded reanalysis climate data were used to investigate the spatiotemporal changes in both trends of vegetation dynamic indicators and climatic variables. We then further analyzed their relations associated with land cover across the AP region. The main findings are threefold: (1) at continental scales the AP region overall experienced a gradual and significant increasing trend in vegetation growth during the last three decades, and this NDVI trend corresponded with an insignificant increasing trend in temperature; (2) vegetation growth was negatively and significantly correlated with the Pacific Decadal Oscillation index and the El Niño/Southern Oscillation (ENSO) in AP; and (3) at pixel scales, except for Australia, both vegetation growth and air temperature significantly increased in the majority of study regions and vegetation growth spatially correlated with temperature; In Australia and other water-limited regions vegetation growth positively correlated with precipitation.     

A power-law-based approach to mapping COVID-19 cases in the United States

This paper examines the spatial and temporal distribution of all COVID-19 cases from January to June 2020 against the underlying distribution of population in the United States. It is found that, as time passes, COVID-19 cases become a power law with cutoff, resembling the underlying spatial distribution of populations. The power law implies that many states and counties have a low number of cases, while only a few highly populated states and counties have a high number of cases. To further differentiate patterns between the underlying populations and COVID-19 cases, we derived their inherent hierarchy or spatial heterogeneity characterized by the ht-index. We found that the ht-index of COVID-19 cases persistently approaches that of the populations; that is, 5 and 7 at the state and county levels, respectively. Mapping the ht-index of COVID-19 cases against that of populations shows that the pandemic is largely shaped by the underlying population with the R-square value between infection and population up to 0.82.     

Dasymetric Estimation of Population Density and Areal Interpolation of Census Data

This paper describes techniques to compute and map dasymetric population densities and to areally interpolate census data using dasymetrically derived population weights. These techniques are demonstrated with 1980-2000 census data from the 13-county Atlanta metropolitan area. Land-use/land-cover data derived from remotely sensed satellite imagery were used to determine the areal extent of populated areas, which in turn served as the denominator for dasymetric population density computations at the census tract level. The dasymetric method accounts for the spatial distribution of population within administrative areas, yielding more precise population density estimates than the choroplethic method, while graphically representing the geographic distribution of populations. In order to areally interpolate census data from one set of census tract boundaries to another, the percentages of populated areas affected by boundary changes in each affected tract were used as adjustment weights for census data at the census tract level, where census tract boundary shifts made temporal data comparisons difficult. This method of areal interpolation made it possible to represent three years of census data (1980, 1990, and 2000) in one set of common census tracts (1990). Accuracy assessment of the dasymetrically derived adjustment weights indicated a satisfactory level of accuracy. Dasymetrically derived areal interpolation weights can be applied to any type of geographic boundary re-aggregation, such as from census tracts to zip code tabulation areas, from census tracts to local school districts, from zip code areas to telephone exchange prefix areas, and for electoral redistricting.     

Defining hydroclimatic regions using daily rainfall characteristics in the southern Appalachian Mountains

ABSTRACT

This paper addresses warm season hydroclimatic variability in the southern Appalachian region of the southeastern U.S., where precipitation can vary as much as 127?mm or more, with maximum seasonal totals exceeding 736?mm in extreme cases. Despite the occurrence of droughts, floods, and their socioecological impacts, hydroclimate variability is still poorly understood. This study characterizes the regional scale variations in the hydroclimate by examining the daily distribution of precipitation patterns in different topographic environments. Parameter-elevation relationships on independent slopes model (PRISM) gridded precipitation estimates are used to identify the location and frequency of different types of rainfall events. Several types of clustering algorithms are used as a regionalization approach to define areas where the precipitation regime exhibits similarities in its frequency of occurrence. The results are compared with internal validation statistics and a visualization is used to assess how well the resulting hydroclimatic regions align with different topographic environments. This study reveals the intricate spatial footprint of dry and wet regimes and demonstrates how clustering applications can be used with gridded climate data to determine where extremes are most likely to develop across mountain catchments.     

Estimation of forest aboveground biomass from HJ1B imagery using a canopy reflectance model and a forest growth model

Accurately estimating the spatial distribution of forest aboveground biomass (AGB) is important because of its carbon budget forms part of the global carbon cycle. This paper presented three methods for obtaining forest AGB based on a forest growth model, a Multiple-Forward-Mode (MFM) method and a stochastic gradient boosting (SGB) model. A Li-Strahler geometric-optical canopy reflectance model (GOMS) with the ZELIG forest growth model was run using HJ1B imagery to derive forest AGB. GOMS-ZELIG simulated data were used to train the SGB model and AGB estimation. The GOMS-ZELIG AGB estimation was evaluated for 24 field-measured data and compared against the GOMS-SGB model and GOMS-MFM biomass predictions from multispectral HJ1B data. The results show that the estimation accuracy of the GOMS-MFM model is slightly higher than that of the GOMS-SGB model. The GOMS-ZELIG and GOMS-MFM models are considerably more accurate at estimating forest AGB in arid and semiarid regions.     

Evaluating the Long-term Urban Expansion of Ranchi Urban Agglomeration,India Using Geospatial Technology

The present study analyzes the built-up expansion of Ranchi urban agglomeration over a period of about 8 decades from 1927–2005. Satellite images and topographical maps were used to evaluate land use dynamics during these periods. Built-up growth of 473.7% during 1927–2005 was primarily at the expense of agricultural land along with reduction of natural water bodies reflects negative impacts of built-up expansion, which increased many folds in recent decades. The built-up growth is also analyzed with reference to population growth, land consumption rate and land transformation. The land loss due to increasing built-up growth of Ranchi were compared with other capital regions and cities along with population increase to provide insight into the possible scenario of built-up expansion in Ranchi urban area.     

Spatial risk assessment of opium poppy cultivation in Afghanistan: integrating environmental and socio-economic drivers

ABSTRACT

Recently the cultivation of opium poppy in Afghanistan reached unprecedented levels. It is agreed that the complex and coupled interactions of social, economic and environmental drivers are crucial for understanding the spatial and temporal dynamics of opium poppy cultivation in Afghanistan. In this context, we present an integrated risk concept, which considers environmental and socio-economic drivers of opium poppy cultivation. A set of spatially explicit indicators for the environmental suitability and socio-economic vulnerability was established and populated from a variety of databases. Subsequently, novel methods of modelling homogeneous and spatially explicit regions of opium poppy cultivation suitability, socio-economic vulnerability and risk are developed and applied. The risk assessment results demonstrate the complex nature of the illicit crops production in Afghanistan and prompt a more profound examination of the drivers of opium poppy cultivation in a spatial context. The study also confirms what has already been widely discussed in literature: that reasons for cultivation are spatially diverse and often distinct, meaning that any formulation of generalized explanations cannot be drawn without ignoring a more complex reality. Thus, an integrative spatial view of risk, which integrates the social dimension as well as environmental parameters, is required to better identify context-specific intervention measures.     

利用偏最小二乘回归反演冬小麦条锈病严重度

通过人工田间诱发不同等级条锈病，在不同生育期测定冬小麦感染条锈病严重度和冠层光谱，采用偏最小二乘（PLS）方法建立了冠层光谱和条锈病严重度的回归模型。结果显示： PLS反演冬小麦条锈病严重度的效果很好，与文献［4］中提出的利用高光谱指数进行反演的结果相比，精度更高； 通过对PLS回归系数的分析，发现叶绿素吸收谷两边（505~550 nm，640~670 nm，680~700 nm）的一阶微分光谱可用于诊断冬小麦条锈病病情，条锈病病害冬小麦在叶绿素吸收谷两边的一阶微分光谱的绝对值会比健康冬小麦的更大。