Integration of remote sensing and GIS in monitoring the dynamics of land cover in Oroba-Kibos catchment area, Kenya

Land cover in Kenya is in a state of fl ux at different spatial and temporal scales. This compromises environmental integrity and socioeconomic stability of the population hence increasing their vulnerability to the externalities of environmental change. The Oroba-Kibos catchment area in western Kenya is one locality where rapid land use changes have taken place over the last 30 years. The shrubs, swamps, natural forests and other critical ecosystems have been converted on the altar of agriculture, human settlement, fuel wood and timber. This paper presents the results of a study that aimed at providing spatially-explicit information for effective remedial response through (a) Mapping the land cover; (b) Identifying the spatial distribution of land cover changes; (c) Determining the nature, rates and magnitude of the land cover changes, and; (d) Establishing the drivers of land use leading to land cover changes in Oroba-Kibos catchment area. Bi-temporal Landsat TM imagery, fi eld observation, household survey and ancillary data were obtained. Per-fi eld classifi cation of the Landsat TM imagery was performed in a GIS and the resultant land cover maps assessed using the fi eld observation data. Post-classifi cation comparison of the maps was then done to detect changes in land cover that had occurred between 1994 and 2008. SPSS was used to analyze the household survey data and attribute the detected land cover changes to their causes. The fi ndings showed that 9 broad classes characterize the catchment area including the natural forests, swamps, natural water bodies, woodlands, shrublands, built-up lands, grasslands, bare lands and croplands. Croplands are dominant and accounted for about 65% (57122 ha) of the total land in 1994, which increased at the rate of 0.89% to 73% (64772 ha) in 2008, while natural water bodies has the least spatial coverage accounting for about 0.6% (561 ha) of the total land in 1994, which diminished at the rate of 3.57% to 0.3% (260 ha) in 2008. Climate, altitude, access and rights to land, demographic changes, poverty, political governance, market availability and economic returns are the interacting mix of proximate and underlying factors that drive the land cover changes in Oroba-Kibos catchment area.     

Modelling the impacts of civil war on land use and land cover change within Kono District,Sierra Leone: a socio-geospatial approach

Capturing the scope and trajectory of changes in land use and land cover (LULC) is critical to urban and regional planning, natural resource sustainability and the overall information needs of policy makers. Studies on LULC change are generally conducted within peaceful environments and seldom incorporate areas that are politically volatile. Consequently, the role of civil conflict on LULC change remains elusive. Using a dense time stack of Landsat Thematic Mapper images and a hybrid classification approach, this study analysed LULC changes in Kono District between 1986–1991, 1991–2002 and 2002–2007 with the overarching goal of elucidating deviations from typical changes in LULC caused by Sierra Leone's civil war (1991–2002). Informed by social survey and secondary data, this study engaged the drivers that facilitated LULC changes during war and non-war periods in a series of spatial regression models in exploring the interface between civil conflict and LULC change.     

Quantifying spatiotemporal dynamics of agricultural landscapes using remotely sensed data and landscape metrics

This study focuses on the spatiotemporal dynamics of agricultural lands and differences in rapidly developing urban and declining rural counties in Iowa, USA between 1984 and 2000. The study presents an analysis of land-cover maps derived from Landsat TM and ETM+ satellite imagery and different landscape metrics using FRAGSTATS and IDRISI software. The study provides evidence of both loss of croplands and change in fragmentation between 1984 and 2000. Fragmentation in agriculture-dominated areas increased with the development of urban centres and diversification of land uses. Fragmentation of landscapes, including agricultural land, was found to be higher in the urbanized counties, but was stable or even declined over time in these counties. In contrast, in the context of remote rural areas, agricultural landscapes experienced rapid increase in fragmentation and farmland loss. The urban–rural gradient analysis used in this study showed that the highest fragmentation occurred on the city edges. These findings suggest that farmland fragmentation is a complex process associated with socio-economic trends at regional and local scales. In addition, socio-economic determinants of landscape fragmentation differ between areas with diverging development trajectories. Intensive cropland fragmentation in remote agricultural regions, detected by this research, should be further studied and its possible effects on both agricultural productivity and biodiversity should be carefully considered.     

Optimization of spatial statistical approaches to identify land use/land cover change hot spots of Pune region of Maharashtra using remote sensing and GIS techniques

This study investigated land use/land cover change (LULCC) dynamics using temporal satellite images and spatial statistical cluster analysis approaches in order to identify potential LULCC hot spots in the Pune region. LULCC hot spot classes defined as new, progressive and non-progressive were derived from Gi* scores. Results indicate that progressive hot spots have experienced high growth in terms of urban built-up areas (20.67% in 1972–1992 and 19.44% in 1992–2012), industrial areas (0.73% in 1972–1992 and 3.46% in 1992–2012) and fallow lands (4.35% in 1972–1992 and ?6.38% in 1992–2012). It was also noticed that about 28.26% of areas near the city were identified as new hot spots after 1992. Hence, non-significant change areas were identified as non-progressive after 1992. The study demonstrated that LULCC hot spot mapping through the integrated spatial statistical approach was an effective approach for analysing the direction, rate, spatial pattern and spatial relationship of LULCC.     

Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop

Abstract

In this study, we tested whether GLS field symptoms on maize can be detected using hyperspectral data re-sampled to WorldView-2, Quickbird, RapidEye and Sentinel-2 resolutions. To achieve this objective, Random Forest algorithm was used to classify the 2013 re-sampled spectra to represent the three identified disease severity categories. Results showed that Sentinel-2, with 13 spectral bands, achieved the highest overall accuracy and kappa value of 84% and 0.76, respectively, while the WorldView-2, with eight spectral bands, yielded the second highest overall accuracy and kappa value of 82% and 0.73, respectively. Results also showed that the 705 and 710 nm red edge bands were the most valuable in detecting the GLS for Sentinel-2 and RapidEye, respectively. On the re-sampled WorldView 2 and Quickbird sensor resolutions, the respective 608 and 660 nm in the yellow and red bands were identified as the most valuable for discriminating all categories of infection.     

Predicting the spatial distribution of hunted and non-hunted Sable antelope (Hippotragus niger niger) using remotely sensed woody cover in a Southern African savanna

From remotely sensed woody cover, we tested whether sables under hunting pressure preferred closed woodland habitats and whether those not under hunting preferred more open woodland habitats. We applied a two factorial logistic regression analysis to model the probability of occurrence of sable antelope in hunted and non-hunted areas of northwest Zimbabwe as a function of vegetation cover density (estimated by a normalized difference vegetation index (NDVI)). We validated the results by high-spatial resolution imagery derived tree canopy area. We subsequently compared the predictions from the two models in order to compare sable cover selection between hunted and non-hunted areas. Our results suggest that hunted sables are likely to select closed woodland, while non-hunted ones would prefer more open woodland habitats. We also established a significant positive relationship between NDVI and tree canopy cover, thus emphasizing the importance of remote sensing in studies that measure the impact of hunting on habitat selection of targeted species.     

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.     

应用图像卷积运算提取城市范围

通过一种卷积算法,从遥感分类数据中计算城市用地比例,并以该比例作为划分城市和非城市区的阈值,进而提取城市范围。方法构建了评价提取结果的指标体系,以确定卷积模板大小和阈值的选取。以北京为研究区,利用2007年SPOT 5多光谱影像分类数据,对方法的可行性进行了验证。结合统计数据中的建成区面积,采用504种模板和阈值组合进行提取试验,讨论了模板和阈值对提取结果的影响。最终选择(205, 51)的模板和阈值组合,对城市范围进行提取。研究结果表明：(1)该方法解决了遥感监测的城市用地分布区与城市范围存在差异的问题,为城市研究基础数据的准备提供了新的方法;(2)阈值过高或过低会造成城市范围的明显缩小或扩大;较小的模板不利于消除随机误差,较大的模板则会导致结果过于平滑;(3)对于SPOT 5多光谱影像分类数据,模板大小在193—205像元(约4 km²)、阈值接近50时,提取结果最好。     

Application of Earth Remote Sensing and GIS in Mapping Land Cover Patterns in Kinangop Division, Kenya

<正>Land cover is a fundamental variable that links many facets of the natural environment and a key driver of global environmental change.Alterations in its status can have significant ramifications at local,regional and global levels.Hence,it is imperative to map land cover at a range of spatial and temporal scales with a view to understanding the inherent patterns for effective characterization,prediction and management of the potential environmental impacts.This paper presents the results of an effort to map land cover patterns in Kinangop division,Kenya,using geospatial tools.This is a geographic locality that has experienced rapid land use transformations since Kenya's independence culminating in uncontrolled land cover changes and loss of biodiversity.The changes in land use/cover constrain the natural resource base and presuppose availability of quantitative and spatially explicit land cover data for understanding the inherent patterns and facilitating specific and multi-purpose land use planning and management.As such,the study had two objectives viz.(i) mapping the spatial patterns of land cover in Kinangop using remote sensing and GIS and;(ii) evaluating the quality of the resultant land cover map.ASTER satellite imagery acquired in January 23,2007 was procured and field data gathered between September l0 and October 16,2007.The latter were used for training the maximum likelihood classifier and validating the resultant land cover map.The land cover classification yielded 5 classes,overall accuracy of 83.5%and kappa statistic of 0.79,which conforms to the acceptable standards of land cover mapping. This qualifies its application in environmental decision-making and manifests the utility of geospatial techniques in mapping land resources.     

Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis

Automatic change detection of land cover features using high-resolution satellite images, is a challenging problem in the field of intelligent remote sensing data interpretation, and is becoming more and more effective for its applications viz. urban planning and monitoring, disaster assessment etc. In the present study, a change in detection approach based on the image morphology that analyses change in the local image grids is proposed. In this approach, edges from both the images are extracted and grid wise comparison is made by probabilistic thresholding and power spectral density analysis for identifying change area. One of the advantages of the proposed methodology is that the temporal images used in the change analysis need not be radiometrically corrected as analysis is based on edge extractions. The grid-based analysis further reduces the error, which might have been introduced by image mis-registration. The proposed methodology is validated by finding the temporal changes in the linear land cover features in parts of Kolkata city, India using three different image data-sets from LISS IV, Cartosat-1 and Google earth having varied spatial resolutions of 5.8 m, 2.5 m and about 1 m, respectively. The overall accuracy in identifying changes is found to be 64.82, 73.86 and 80.93% for LISS IV, Cartosat-1 and Google earth data-set, respectively.     

典型喀斯特城市的热岛时空变化及其成因分析

利用1989、2003、2018年的Landsat影像对安顺市西秀区的地表温度进行反演,分析研究区在30年发展中的热岛时空变化及其成因。使用基于影像的反演算法,结合分类回归树算法进行地表温度的反演,用气象站数据对反演结果进行精度验证,并建立缓冲区对研究区进行相关性分析等定量分析。结果表明：研究区受喀斯特地貌的影响,除主城区外,郊区也存在大量高温区;近30年研究区热效应与不透水面、绿地的面积有极显著相关;1989-2003年研究区城市热岛面积随城市扩张逐渐增大,但2018年主城区城市热岛现象几乎完全消失,排除气象因素和城市形态因子影响的可能后,发现这与安顺市城市绿化的大力进行有密切关系。     

陆地表层覆盖变化对地表反照率影响的四国对比

陆表覆盖变化影响地表特征从而改变地表能量平衡是理解人类活动对全球气候变化影响的关键环节。选择国际气候谈判主要国家的美国、印度和巴西作为中国的对比国,对比分析不同国别、不同气候带典型陆表覆盖类型的地表反照率时空差异,进而模拟开垦和城市化等陆表覆盖变化对反照率的影响差异。结果表明：(1) 2000年—2015年,中国、美国的地表反照率年际变化存在明显的气候带空间分异特征,中国干旱半干旱区和美国中低纬湿润区表现出降低趋势,而中国亚热带湿润和美国高纬与中部干旱区则表现出明显的升高趋势,印度的地表反照率年际变化呈微弱下降趋势,而巴西为微弱上升趋势。(2)无雪覆盖时,耕地、林地、草地和人造地表反照率具有夏高、冬低的时间变化特征,干旱半干旱区反照率明显高于湿润区。4种类型的国别差异体现在,中国亚热带湿润区地表反照率均以上升为主,干旱半干旱区则相反;美国除耕地在干旱区呈较强的升高趋势外,其余类型基本为降低趋势;印度均表现为降低趋势;巴西则表现为略微升高趋势。(3)与无雪覆盖相比,有雪覆盖时不同陆表覆盖类型地表反照率均有所提高,林地提高幅度最小,约0.06—0.26,耕地提高最大,约为0.17—0.38,且中国林地反照率提高幅度略高于美国。(4)原陆表覆盖为林地时,开垦和城镇化均导致地表反照率升高,且干旱区升高幅度高于湿润区,湿润区的升高幅度随纬度降低而减弱;为草地时,开垦主要在巴西、印度和中、美亚热带湿润区引起地表反照率升高。而城镇化引起的反照率变化则受到原有地表覆盖、季节和气候背景影响存在较复杂的国别和气候带差异。     

Optimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal,South Africa

The objective of this study was to identify an appropriate spatial resolution for discriminating forest vegetation at subspecies level. WorldView-2 imagery was progressively resampled to coarser spatial resolutions. At a compartment level, 30 × 30-m subsets were generated across forest compartments to represent the five forest subspecies investigated in this study. From the centre of each subset, the spatial resolution of the original WorldView-2 image was resampled from 6 to 34-m, with increments of 4-m. The variance was then calculated at every resampled spatial resolution using each of the eight WorldView-2 bands. Based on the sampling theorem, the 3-m spatial resolution provided an appropriate resolution for all subspecies investigated. The WorldView-2 image was subsequently classified using the partial least squares linear discriminant analysis algorithm and the appropriate spatial resolution. An overall classification accuracy of 90% was established with an allocation disagreement of 9 and a quantity disagreement of 1.     

Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South

ABSTRACT

Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods. These advances in urban feature extraction and built-area detection can refine the mapping of human population densities, especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data. However, in these contexts it is unclear how best to use built-area data to disaggregate areal, count-based census data. Here we tested two methods using remotely sensed, built-area land cover data to disaggregate population data. These included simple, areal weighting and more complex statistical models with other ancillary information. Outcomes were assessed across eleven countries, representing different world regions varying in population densities, types of built infrastructure, and environmental characteristics. We found that for seven of 11 countries a Random Forest-based, machine learning approach outperforms simple, binary dasymetric disaggregation into remotely-sensed built areas. For these more complex models there was little evidence to support using any single built land cover input over the rest, and in most cases using more than one built-area data product resulted in higher predictive capacity. We discuss these results and implications for future population modeling approaches.     

Examining the utility of random forest and AISA Eagle hyperspectral image data to predict Pinus patula age in KwaZulu-Natal,South Africa

High-data dimensionality is a common problem in hyperspectral data processing. Consequently, remote sensing techniques that reduce the number of bands are considered essential tools for most hyperspectral applications. The aim of this study was to examine the utility of the random forest ensemble to select the optimal subset of hyperspectral bands to predict the age of Pinus patula stands. Airborne AISA Eagle hyperspectral image data were collected over the study area. The random forest ensemble was used to test whether the forward or backward variable selection methods could identify the optimal subset of bands. Results indicate that both the selection methods produced high-predictive accuracies (root mean square error = 3.097 years). However, the backward variable selection method utilized 206 bands for the final model, while the forward variable selection utilized only a small subset of non-redundant bands (n = 9) while preserving the highest model accuracy (R ² = 0.6).     

利用MODIS多通道反照率产品估算OCO-2氧气A吸收带陆表反照率方法

Land surface reflection depends on land surface albedo and interferes with the retrieval of cloud geometrical thickness from OCO-2 oxygen A-band observations due to its second-strongest reflection after the cloud. However, no product can provide the land surface albedo of the OCO-2 oxygen A-band required for the retrieval. Therefore, the accurate estimation of land surface albedo is necessary and beneficial to the retrieval quality. In this study, we proposed a method for estimating land surface albedo in the oxygen A-band from multichannel black/white albedos from MODIS/MCD43C3 products. Although the estimation (MODIS→OCO-2) is related to land cover type, the comparison based on Shannon entropy proved that the multichannel albedo data contains the type information and is sufficient to achieve the same accuracy as land cover-type estimation. In addition, we implement the estimation model by BP neural network, and the accuracy is consistent with that of the analysis based on the Shannon entropy. We verified the multichannel-based estimation model by tests in different times and spaces. Its correlation coefficients were all over 0.93, and the root-mean-squared errors were 0.026. In addition, the multichannel-based model was always superior to the single-channel linear model on all land cover types, whether applied to the best-performing type of barren or sparsely vegetated land or the worse-performing type of snow and ice. The quality of MODIS albedo data is the most important for the accuracy of estimation. The root-mean-squared error with the best inputs was slightly better than 0.02 and increased to more than 0.05 as the quality of the inputs decreased. The method of estimating the land surface albedo in the OCO-2 oxygen A-band from MODIS multichannel black/white albedo data is feasible and can resist the disturbance caused by unknown land cover type. The estimation accuracy depends on the quality of the input MODIS albedo data. © 2023 Science Press. All rights reserved.     

条件随机场框架下基于随机森林的城市土地利用/覆盖遥感分类

针对"基于像素的条件随机场(conditional random fields,CRFs)模型能否在m级分辨率的多光谱遥感图像分类中表现良好"的问题,提出了集成图像的光谱、方向梯度直方图和多尺度多方向Texton纹理等多种线索的CRFs模型定义方法。利用上述特征,选择随机森林(random forests,RF)定义CRFs关联势函数;利用特征对比度加权的Potts函数定义CRFs交互势函数,并且建立了多标签的RF-CRFs模型;对该模型进行分项参数训练以及基于图割的α-膨胀算法推理;利用典型城区的Quick Bird多光谱图像进行模型的验证与精度评价。结果表明RF-CRFs模型的分类精度可达82.52%以上,比RF分类器的分类精度提高了3.35%。     

Changing landscape in the Three Gorges Reservoir Area of Yangtze River from 1977 to 2005: Land use/land cover,vegetation cover changes estimated using multi-source satellite data

The eco-environment in the Three Gorges Reservoir Area (TGRA) in China has received much attention due to the construction of the Three Gorges Hydropower Station. Land use/land cover changes (LUCC) are a major cause of ecological environmental changes. In this paper, the spatial landscape dynamics from 1978 to 2005 in this area are monitored and recent changes are analyzed, using the Landsat TM (MSS) images of 1978, 1988, 1995, 2000 and 2005. Vegetation cover fractions for a vegetation cover analysis are retrieved from MODIS/Terra imagery from 2000 to 2006, being the period before and after the rising water level of the reservoir. Several analytical indices have been used to analyze spatial and temporal changes. Results indicate that cropland, woodland, and grassland areas reduced continuously over the past 30 years, while river and built-up area increased by 2.79% and 4.45% from 2000 to 2005, respectively. The built-up area increased at the cost of decreased cropland, woodland and grassland. The vegetation cover fraction increased slightly. We conclude that significant changes in land use/land cover have occurred in the Three Gorges Reservoir Area. The main cause is a continuous economic and urban/rural development, followed by environmental management policies after construction of the Three Gorges Dam.     

安徽省含山县土地整理耕地质量评价及其变化研究

土地整理是改善土地利用条件,提高土地质量的有效措施,对土地整理后的耕地质量进行评价是土地整理重要工作之一。从耕地质量的内涵和土地整理项目的基本要求出发,选取耕地自然条件和耕地利用条件2个基本因素和12个影响因子,将特尔斐法与层次分析法相结合,确定各因子权重,构建基于土地整理的耕地质量评价指标体系。以安徽省含山县铜闸镇土地整理项目为例,采用传统的土壤调查与3S技术相结合的方法,分析土地整理复垦过程中耕地质量时空格局变化,得出整理前、后耕地质量的级别变化。研究结果表明,通过土地整理,研究区的耕地质量空间分布和级别发生了很大变化,整理后一级、二级、三级质量的土地比整理前分别增加了7.20%,11.95%和0.65%,四级、五级则分别减少了15.65%和4.16%,整理后的耕地质量总体水平是整理前的1.291倍。     

Remotely-sensed assessment of the impact of century-old biochar on chicory crop growth using high-resolution UAV-based imagery

In recent years, special attention has been given to the long-term effects of biochar on the performance of agro-ecosystems owing to its potential for improving soil fertility, harvested crop yields, and aboveground biomass production. The present experiment was set up to identify the effects on soil-plant systems of biochar produced more than 150 years ago in charcoal mound kiln sites in Wallonia (Belgium). Although the impacts of biochar on soil-plant systems are being increasingly discussed, a detailed monitoring of the crop dynamics throughout the growing season has not yet been well addressed. At present there is considerable interest in applying remote sensing for crop growth monitoring in order to improve sustainable agricultural practices. However, studies using high-resolution remote sensing data to focus on century-old biochar effects are not yet available. For the first time, the impacts of century-old biochar on crop growth were investigated at canopy level using high-resolution airborne remote sensing data over a cultivated field. High-resolution RGB, multispectral and thermal sensors mounted on unmanned aerial vehicles (UAVs) were used to generate high frequency remote sensing information on the crop dynamics. UAVs were flown over 11 century-old charcoal-enriched soil patches and the adjacent reference soils of a chicory field. We retrieved crucial crop parameters such as canopy cover, vegetation indices and crop water stress from the UAV imageries. In addition, our study also provides in-situ measurements of soil properties and crop traits. Both UAV-based RGB imagery and in-situ measurements demonstrated that the presence of century-old biochar significantly improved chicory canopy cover, with greater leaf lengths in biochar patches. Weighted difference vegetation index imagery showed a negative influence of biochar presence on plant greenness at the end of the growing season. Chicory crop stress was significantly increased by biochar presence, whereas the harvested crop yield was not affected. The main significant variations observed between reference and century-old biochar patches using in situ measurements of crop traits concerned leaf length. Hence, the output from the present study will be of great interest to help developing climate-smart agriculture practices allowing for adaptation and mitigation to climate.