Mapping fallow lands in Vietnam's north-central mountains using yearly Landsat imagery and a land-cover succession model

The objective of this article is to investigate whether it is possible to use Landsat data together with ancillary data and temporal context to accurately identify land covers found in the fallow areas of Montane Mainland Southeast Asia's (MMSEA's) difficult-to-map swidden landscapes. A rule-based non-parametric hybrid classification method that integrates knowledge about the vegetation regrowth patterns in these landscapes with analysis of Landsat imagery is developed. The method is applied to three upland districts of the Nghe An Province, Vietnam. The results show that the hybrid classification approach, with an overall accuracy of 90%, is superior to using a traditional maximum likelihood classifier, which generated an overall accuracy of 68%. The hybrid classification results indicate that the landscape is dominated by bush and bamboo, while the maximum likelihood classification suggests a landscape that is predominantly grass covered. The hybrid classification results are in agreement with local knowledge and information from fieldwork-based reports and articles on swidden systems in the study area and other parts of MMSEA.     

Use of fraction imagery,segmentation and masking techniques to classify land-use and land-cover types in the Brazilian Amazon

This work presents a procedure for classifying land-use and land-cover (LULC) types in the Brazilian Amazon. Fraction imagery representing proportions of green vegetation, soil, and shade was estimated using all six reflective bands of the Landsat-5 Thematic Mapper (TM1 to TM5 and TM7) through the linear spectral mixing model (LSMM). The fraction information registered at pixel level was then related to different types of land classes following three principal procedures: (1) selecting an image or image group as input for segmentation; (2) application of sequences of masking techniques to address the segmentation of preselected areas in order to obtain better image partitioning; and (3) application of an unsupervised classifier by region, named Isoseg, to group the segmented regions. Isoseg is a clustering algorithm that calculates the centre of each class using the covariance matrix and the average vector of the regions. An assessment of the classification was performed visually and by error matrix, relating reference data points to classification results. The results showed that fraction images were effective in highlighting the different types of LULC. Several tests were conducted to evaluate the efficacy of the masking technique in the process for extracting information. The results showed that the use of masks significantly improves the segmentation results. However, in the Isoseg classification process, the masking technique was not able to avoid omission and commission errors between classes of similar structures. On comparing the results obtained in this work with a Maximum Likelihood classification, it was found that adopting the procedures described resulted in increases of 10% in average and global accuracy, and 18% in average reliability. Furthermore, a reduction was observed in the variability of errors created in the classification.     

Patterns and trends in land-use land-cover change research explored using self-organizing map

Research on land change has a long history, has generated numerous publications and continues to receive international research attention. To facilitate the understanding of the patterns and trends of land-change research, this article uses a content-based text-retrieval approach and self-organizing map to analyse more than 700 peer-reviewed remote-sensing and natural-science papers on land-use/cover change (LUCC) from the past two decades. We present the results in map-like displays and discuss papers within the identified clusters to examine the research activities. A new cluster of research, which has emerged in the last 5 years of analysis, has focused on mixed-pixel issues for land-use/cover mapping, particularly in the context of forest catchments. Studies of LUCC consequences after 2000 have been concerned with the effects of forest conversion on soil-nutrient pools and nitrate cycling. Incorporating information on resolutions and extents into the representations reveals a dominant scale of analysis for some research activities. Analysing time frames of examination in the papers suggests that research on long-term LUCC consequences started to use presettlement land survey records. Few attempts, however, have been made to investigate the uncertainties in the historical sources of information for LUCC research, thereby presenting a future research topic.     

Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City

Rapid global economic development has resulted in a corresponding intensification of urbanization, which has in turn impacted the ecology of vast regions of the world. A series of problems have thus been introduced, such as changes in land-use/land-cover (LULC) and changes in local climate. The process of urbanization predominantly represents changes in land-use, and is deemed by researchers to be the chief cause of climate change and ecological change. One of the principal purposes of the research in this field is to find ways to mitigate the influence of land-use change on local or global environments. In the study presented in this article, satellite images were utilized to extract information regarding land-use in Beijing City, and to develop maps of land surface temperature (LST) during two different periods of time: 2 August 1999 and 8 August 2010. A supervised classification scheme, a support vector machine, was used to derive the land-use change map for the above periods. Maps of surface temperature are derived from the thermal band of Landsat images using the mono-window algorithm. Results from post-classification comparison indicated that an increase in impervious surface areas was found to be dramatic, while the area of farmland decreased rapidly. The changes in LULC were found to have led to a variation in surface temperature, as well as a spatial distribution pattern of the urban heat island phenomenon. This research revealed that the hotspots were mainly located in areas dominated by three kinds of material: bare soil, rooftops, and marble surfaces. Results from the local Moran's I index indicated that the use of lower surface temperature materials will help to mitigate the influence of the urban heat island phenomenon. The results of this research study provide a reference for government departments involved in the process of designing residential regions. Such a reference should enable the development of areas sympathetic to environmental changes and hence mitigate the effects of the growing intensity of urbanization.     

Spectral analysis of civil conflict-induced forced migration on land-use/land-cover change: the case of a primate and lower-ranked cities in Sierra Leone

Involuntary migration triggered by war has the capacity to generate substantial socioeconomic and environmental changes in cities of developing countries, resulting in aberrant alterations to land use and land cover (LULC). This scenario has the potential to diminish the quality of life of inhabitants of a city and present administrative challenges for government and other officials. Gauging the scope and trajectory of urban LULC changes in a war-related environment is pivotal for urban and regional planning, the sustainability of natural resources, and information needs of policy makers. Scholarships that link remote sensing to social science mostly focus on a non-conflict environment, resulting in little or no information on the ramifications of conflict-induced forced migration on changes in LULC. As a result, the role of civil conflict-induced forced migration on the composition and configuration of urban landscape in developing countries remains elusive. This study employs a dense time stack of Landsat-5 Thematic Mapper (TM) images and a hybrid classification approach that integrates linear spectral unmixing and an ensemble decision-tree classifier to characterize LULC in a primate city and two lower-ranked cities in Sierra Leone. The study examined three time-steps which span 1986–1991, 1991–2002, and 2002–2010 with the overarching goal of elucidating changes in LULC conditioned by civil conflict. Image classification accuracy (overall accuracy) ranged between 84.0% and 90.2%. The study demonstrated that civil conflict has the capacity to trigger notable growth in urban agricultural land (37.4%) in a primate city, while the expansion of residential (112.7%) and industrial/commercial (18.7%) lands is more prominent in a lower-ranked city. The study further revealed that population expansion does not necessarily result in significant growth in residential area in a primate city that has experienced civil conflict.     

Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains,California

Treatments to reduce forest fuels are often performed in forests to enhance forest health, regulate stand density, and reduce the risk of wildfires. Although commonly employed, there are concerns that these forest fuel treatments (FTs) may have negative impacts on certain wildlife species. Often FTs are planned across large landscapes, but the actual treatment extents can differ from the planned extents due to operational constraints and protection of resources (e.g. perennial streams, cultural resources, wildlife habitats). Identifying the actual extent of the treated areas is of primary importance to understand the environmental influence of FTs. Light detection and ranging (lidar) is a powerful remote-sensing tool that can provide accurate measurements of forest structures and has great potential for monitoring forest changes. This study used the canopy height model (CHM) and canopy cover (CC) products derived from multi-temporal airborne laser scanning (ALS) data to monitor forest changes following the implementation of landscape-scale FT projects. Our approach involved the combination of a pixel-wise thresholding method and an object-of-interest (OBI) segmentation method. We also investigated forest change using normalized difference vegetation index (NDVI) and standardized principal component analysis from multi-temporal high-resolution aerial imagery. The same FT detection routine was then applied to compare the capability of ALS data and aerial imagery for FT detection. Our results demonstrate that the FT detection using ALS-derived CC products produced both the highest total accuracy (93.5%) and kappa coefficient (κ) (0.70), and was more robust in identifying areas with light FTs. The accuracy using ALS-derived CHM products (the total accuracy was 91.6%, and the κ was 0.59) was significantly lower than that using ALS-derived CC, but was still higher than using aerial imagery. Moreover, we also developed and tested a method to recognize the intensity of FTs directly from pre- and post-treatment ALS point clouds.     

Classification of Landsat images based on spectral and topographic variables for land-cover change detection in Zagros forests

Detection of land-cover changes through time can be complicated because of sensor-specific differences in spatial and spectral resolutions; classified land-cover changes can be due to either real changes on the ground or a switch in sensors used to collect data. This study focused on two objectives: (1) selecting the best predictor variables for the classification of semi-arid Zagros forests given the characteristics of the study area and available data sets and (2) evaluating the application of the random forest (RF) algorithm as a unified technique for the classification of data sets acquired from different sensors. Three images of the same study area were acquired from the Landsat-5 Thematic Mapper (TM) sensor in 2009, the Landsat-7 Enhanced Thematic Mapper (ETM+) sensor with Scan Line Corrector (SLC) in 1999 and the Landsat-2 Multispectral Scanner (MSS) sensor in 1975. Following image preprocessing, the RF algorithm was applied for variable selection and classification. A test of equivalence was used to compare the overall accuracy of the classified maps from the three sensors. Slope, normalized difference vegetation index (NDVI) and elevation were determined to be the most important predictor variables for all three images. High overall classification accuracies were achieved for all three images (97.90% for MSS, 95.43% for TM and 95.29% for ETM). The ETM- and TM-derived maps had equivalent overall accuracy and even significantly higher overall accuracy was obtained for the MSS-derived map. The post-classification comparison showed an increase in agriculture and a decrease in forest cover. The selected predictor variables were consistent with ecological reality and showed more details on the changes of the land-cover classes across biophysical variables of the study area through time.     

A hybrid approach to mapping land-use modification and land-cover transition from MODIS time-series data: A case study from the Bolivian seasonal tropics

This study uses a combination of satellite imagery and GIS data, a vegetation map, interview data, and on-site field studies to map detailed natural vegetation to land-use conversion pathways (~ 22,000 possible combinations) in the seasonal tropics of Santa Cruz Department in southeastern Bolivia from 1994 to 2008. We mapped a suite of land-use classes based on the seasonal phenology of double- and single season cropping regimes; pasture; and bare soil cropland (fallow). Analyses focus specifically on the Corredor Bioceánico, which bisects some of the most sensitive and poorly understood ecosystems in the world and indirectly creating one of the most important agricultural region-deforestation hotspots in South America at the present time. Training data to predict class membership were based on MODIS NDVI annual mean, maximum, minimum, and amplitude derived from field observations, semi-structured interviews, and aerial videography. Results show that over 8,000 km² of forest was lost during the 14-year study period. In the first years of cultivation, pasture is the dominant land use, but quickly gives way to cropland. The main findings according to forest type is that transitional forest types on deep and poorly drained soils of alluvial plains have lost the most in terms of percentage area cleared. The resulting transition pathways can potentially provide decision-makers with more detailed insight as to the proximate causes or driving forces of land change in addition to the most threatened forests remaining in the Tierras Bajas and those most likely to be cleared in the Brazilian Shield and Pantanal.     

Land-cover change dynamics and coastal aquaculture development: a case study in the East Godavari delta,Andhra Pradesh,India using multi-temporal satellite data

The present study focuses on the identification and quantification of land-cover changes occurring in the coastal stretches of the East Godavari delta, Andhra Pradesh, India. The analysis of series of multi-temporal satellite data provides an accurate quantification and therefore a better understanding of the process of land-cover changes during 1990–2005. Land-cover changes were quantified based on normalized difference vegetation index (NDVI) image differencing and a post-classification comparison approach. The change detection results were examined in terms of the proportion of land-cover classes and change trajectories with particular emphasis on coastal aquaculture development within the study area. The study shows that the total area under aquacultural ponds increased from 2985 ha in 1990 to 7067 ha in 2005. The major changes in the study area occurred during 1990–1994, when 2873 ha of agricultural land and 762 ha of degraded mangroves were converted into aquacultural ponds. The prediction of land-cover distribution in 2010 on the basis of a Markov chain shows a continuing upward trend of the aquaculture area (8267 ha) with less impact on the mangrove area. The analysis predicts that the agricultural land area will continue to decrease from 50 122 to 46 978 ha during 2005–2010.     

Dambo mapping for environmental monitoring using Landsat TM and SAR imagery: Case study in the Zambian Copperbelt

The purpose of this study was to monitor the impact of mining in the Zambian Copperbelt, specifically using dambos as an environmental indicator for pollution. Data fusion using a Brovey transform was used for combining speckle filtered radar data with optical data to effectively map natural dambos and dambos that have degraded due to human impact. Comparative analysis of raw images and fusion product reveals that, whereas natural dambos show low values on Landsat reflective bands and low backscatter response in SAR imagery, degraded dambos have mixed spectral responses. Degraded dambos are difficult to identify in either optical or SAR images alone, but a fusion product highlights complimentary spectral information, making these environmental indicators uniquely identifiable.     

Assessment of urban growth in Guangzhou using multi-temporal,multi-sensor Landsat data to quantify and map impervious surfaces

Over the past few decades, one of the most important global environmental issues has been the increasing transformation of natural landscape cover to impervious surface (IS) as a result of urbanization. A wide range of urban ecology fields, such as urban hydrology, urban climatology, urban planning, and urban ecosystem monitoring, demand accurate and timely information on land use and land cover in urban areas. As part of one of the fastest growing regions in China, Guangzhou is experiencing rapid urban expansion. We developed a combined method that integrates spectrum normalization, normalized spectral mixture analysis (NSMA), threshold segmentation, and a spatiotemporal modification approach to map the Guangzhou urban extent, through quantification of IS as a continuous variable using Landsat series images. We also developed a method to automatically extract water from Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) imagery. The results indicated that the integrated approach is accurate, powerful, and effective. The Guangzhou assessment showed that from 1973 to 2013 the amount of urban area with sub-pixel IS larger than or equal to 55% increased from 0.27% to 30.58%, an increase in urban extent of more than 100-fold over 40 years.     

Mapping land cover change in a reindeer herding area of the Russian Arctic using Landsat TM and ETM+ imagery and indigenous knowledge

Much of Russia north of the treeline is grazed by reindeer, and this grazing has materially altered the vegetation cover in many places. Monitoring vegetation change in these remote but ecologically sensitive regions is an important task for which satellite remote sensing is well suited. Further difficulties are imposed by the highly dynamic nature of arctic phenology, and by the difficulty of obtaining accurate official data on land cover in arctic Russia even where such data exist. We have approached the problem in a novel fashion by combining a conventional multispectral analysis of satellite imagery with data on current and historical land use gathered by the techniques of social anthropology, using a study site in the Nenets Autonomous Okrug (NAO). A Landsat-7 ETM+ image from the year 2000 was used to generate a current land cover classification. A Landsat-5 TM image was used to generate a land-cover classification for 1988, taking due account of phenological differences and between the two dates. A cautious comparison of these two classifications, again taking account of possible effects of phenological differences, shows that much of the study area has already undergone a notable transformation to grass-dominated tundra, almost certainly as a result of heavy grazing by reindeer. The grazing pattern is quite heterogeneous, and may have reached unsustainable levels in some areas. Finally, we suggest that this situation is unlikely to be unique to our study area and may well be widespread throughout the Eurasian tundra zone, particularly in the west.     

Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images

The homogeneity of time series of satellite images is crucial when studying abrupt or gradual changes in vegetation cover via remote sensing data. Various sources of noise affect the information received by satellites, making it difficult to differentiate the surface signal from noise and complicates attempts to obtain homogeneous time series. We compare different procedures developed to create homogeneous time series of Landsat images, including sensor calibration, atmospheric and topographic correction, and radiometric normalization. Two seasonal time series of Landsat images were created for the middle Ebro Valley (NE Spain) covering the period 1984–2007. Different processing steps were tested and the best option selected according to quantitative statistics obtained from invariant areas, simultaneous medium-resolution images, and field measurements. The optimum procedure includes cross-calibration between Landsat sensors, atmospheric correction using complex radiative transfer models, a non-lambertian topographic correction, and a relative radiometric normalization using an automatic procedure. Finally, three case studies are presented to illustrate the role of the different radiometric correction procedures when analyzing and explaining gradual and abrupt temporal changes in vegetation cover, as well as temporal variability. We have shown that to analyze different vegetation processes with Landsat data, it is necessary to accurately ensure the homogeneity of the multitemporal datasets by means of complex radiometric correction procedures. Failure to follow such a procedure may mean that the analyzed processes are non-recognizable and that the obtained results are invalid.     

Long-term monitoring of citrus orchard dynamics using time-series Landsat data: a case study in southern China

ABSTRACT

Citrus orchard planting is a typical land-use change process that can impact terrestrial ecosystem services both locally and globally. Long-term monitoring of citrus orchard dynamics is critical for understanding its change patterns as well as the potential driving factors. Satellite remote-sensing imagery has been a primary data source for this purpose. However, most previous studies with multi-year intervals only captured some, but not all detailed information on citrus orchard expansion. In this study, we developed a framework for mapping annual citrus orchard extent and track its long-term dynamics in Xunwu County, China, using the historical Landsat repository from 1990 to 2016. The results suggested that the average overall accuracy of original annual mapping was 87.73%, and its performance was significantly improved after the temporal filtering approach (91.46%). Several features (e.g. elevation, slope, normalized difference vegetation index) played more important roles in citrus orchard identification. With the achieved annual mapping layers, we found a rapid citrus orchard expansion trend during the study interval (i.e. from 22.18 to 697.21 km²). Moreover, this expansion process was unevenly distributed in time. Spatially, emerging citrus orchards were primarily transformed from forests and croplands and mainly distributed in areas with elevations from 200 to 500 m and slopes range from 5° to 20°. This study demonstrated the potential of mapping citrus orchard dynamics at a higher temporal frequency with remote-sensing time-series, which can contribute to providing reference for sustainable land-use policy.     

Characterizing changes in cropping patterns using sequential Landsat imagery: an adaptive threshold approach and application to Phoenix,Arizona

Since the 1970s, the Phoenix Active Management Area has experienced rapid urbanization, mostly through land conversions from agricultural lands to urban land use. Rapid urban expansion and population growth have placed unprecedented pressure on agricultural production in this region. Agricultural intensification, in particular double cropping, has been observed globally as an important response to the growing pressure on land. However, the intensification has a number of negative impacts on water quality, biodiversity, and biogeochemical cycles. Thus, quantifying the spatial pattern of cropping intensity is important for natural resource management. In this study, we developed an adaptive threshold approach to map cropping intensity using time series Landsat data and examined the spatiotemporal patterns of cropping intensity in the Phoenix Active Management Area from 1995 to 2010 at 5-year intervals. To map cropping intensity accurately, the adaptive threshold algorithm was designed specifically to address several issues caused by the complex cropping patterns in the study area. The adaptive threshold method has abilities to (1) distinguish true crop cycles from multiple false phenological peaks, (2) minimize errors caused by data noise and missing data, (3) identify alfalfa and interyear crops and to distinguish alfalfa from double crops, and (4) adapt to temporal profiles with different numbers of observations. The adaptive threshold algorithm is effective in characterizing cropping intensity with overall accuracies exceeding 97%. Results show that there is a dramatic decline in the area of total croplands (46.1%), single crops (46.3%), and double crops (43.4%) during the study period. There was a small conversion (1.9%) from single to double crop from 1995 to 2000, whereas a reverse conversion (1.3%) was observed from 2005 to 2010. Updated and accurate information on the spatial distribution of cropping intensity provide important implications on effective and sustainable cropping practices. In addition, joint investigation on cropping patterns and irrigation water use can shed light on future agricultural water demand, which is of paramount importance in this rapidly expanding arid region.     

The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana,USA

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented. It was found that the VCI is sensitive to changes in land cover, especially deforestation, the land cover changes from evergreen and deciduous forests to other land-cover classes. However, because the scale of land-cover changes was very small across the study area, only trivial drought alerts were observed in the VCI-based drought maps during non-drought years. Because drought is a large-scale climate event, it is reasonable to neglect these alerts. Besides, when the VCI was averaged to climate division scale, the results obtained through the VCI method were in good agreement with those acquired by the meteorological data-based drought indices such as the Palmer drought severity index and standardized precipitation index.     

Remote sensing of land-cover change and landscape context of the National Parks: A case study of the Northeast Temperate Network

National park units and protected areas face critical management challenges because of changing land-cover types and variability of landscape contexts within and adjacent the park boundaries. In this study we developed and implemented a multi-scale protocol for detecting and monitoring land-cover change in and adjacent to National Parks and ten segments of the Appalachian National Scenic Trail (AT) in the northeastern United States. We used Landsat imagery from 1970 to 2002 and recent ground-based photography to evaluate changes within park boundaries and within 0.5, 1, and 5 km buffers. The study concluded that all of the studied park units, except one segment of AT in Maine, experienced increases of urban land and declines of forest cover in the immediately adjacent areas and extended buffer zones. Over 30 years and across all parks and trail segments, urban land increased 172% and 181% within 0.5 and 1 km, respectively, of the park boundary or trail centerline. Over the same time period, forested area decreased by 5% and 6% within 0.5 and 1 km, respectively, of the park boundary or trail centerline, with more loss of forest near the parks (18%) than the trail segments (2%). This study provided baseline data demonstrating land-cover alteration over the past three decades and a foundation for a land-cover change and landscape context protocol suitable for monitoring future changes of National Parks and protected areas.     

On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: A case study of the Honghu Lake,PR China

The visible and near infrared bands of the Landsat thematic mapper (TM) were used in an empirical assessment of submerged vegetation biomass in Honghu Lake in the middle reaches of the Yangtse River, in the People's Republic of China. The method used here was based on eigenvector rotation of the four bands to enhance submerged vegetation biomass variations. Field measurement of spectral reflectance of submerged vegetation was taken for various biomass and vegetation types to determine the possibility of estimating submerged vegetation biomass using remote sensing. The locations of sample points were determined by global positioning system (GPS) and field biomass was obtained at the same time as the TM image. Regression analyses were performed between the principal components and biomass, and a marked linear relationship between submerged vegetation biomass and first two principal components (PC) was revealed. This was used to determine the total biomass of submerged vegetation.     

Risk assessment for wildfire occurrence in high-voltage power line corridors by using remote-sensing techniques: a case study in Hubei Province,China

Wildfires are a significant problem for power line maintenance that can lead to widespread power outages and economic loss in China. Remote sensing (RS) and geographic information system (GIS) technologies and their derived applications are effective tools for power line surveillance and disaster prevention. Using multi-source data and remote-sensing techniques, a risk assessment of wildfire occurrence in high-voltage power line corridors in Hubei Province, China, is presented in this study. Both natural and human causes are considered in the assessment, including a traditional Chinese spring custom. Historical ignition points along power line corridors in Hubei Province that occurred from 2009 to 2014 were collected as training data to create a quantitative analysis. Next, a logistic regression model was applied, and ignition probability maps were produced for power lines. Several wildfire accidents that occurred in 2015 were used as validation data to evaluate the performance of the proposed method. The experimental results indicated that the drought conditions significantly influenced wildfires as natural cause, and human activities play important roles in causing wildfires during specific time periods. The effectiveness and robustness of our method is thereby demonstrated, and our method can be used to provide valuable suggestions for wildfire management for the electrical department in Hubei Province.