Land Cover Monitoring Research Articles

Spectral harmonization and red edge prediction of Landsat-8 to Sentinel-2 using land cover optimized multivariate regressors

Multi-sensor remote sensing applications consistently gain importance, boosted by a growing number of freely available earth observation data, increasing computing capacity, and increasingly complex algorithms that need as temporally dense data as possible. Using data provided by different sensors can greatly improve the temporal resolution of time series, fill data gaps and thus improve the quality of land cover monitoring applications. However, multi-sensor approaches are often adversely affected by different spectral characteristics of the sensing instruments, leading to inconsistencies in downstream products. Spectral harmonization, i.e., the transformation of one sensor into the spectral domain of another sensor, may reduce these inconsistencies. It simplifies workflows, increases the reliability of subsequently derived multi-sensor products and may also enable the generation of new products that are not possible with the initial spectral definition. In this paper, we compare the effect of multivariate spectral harmonization techniques on the inter-sensor reflectance consistency and derived products such as spectral indices or land cover classifications. We simulated surface reflectance data of Landsat-8 and Sentinel-2A from airborne hyperspectral data to eliminate any sources of error originating from unequal acquisition geometries, illumination or atmospheric state. We evaluate different methods based on linear, quadratic and random forest regression as well as linear interpolation, and predict not only matching but also unilaterally missing bands (red edge). We additionally consider material-dependent spectral characteristics in the harmonization process by using separate transformation functions for spectral clusters of the input dataset. Our results suggest that spectral harmonization is useful to improve multi-sensor consistency of remote sensing data and subsequently derived products, especially if multiple transformation functions are incorporated. There is a strong dependency between harmonization performance and the similarity of source and target sensor's spectral characteristics. For spectrally transforming Landsat-8 to Sentinel-2A, we achieved the lowest radiometric inter-sensor deviations with 50 spectral clusters and linear regression. Based on simulated data, deviations are below 1.7% reflectance within the red edge spectral region and below 0.3% reflectance for the remaining bands (RMSE). Regarding spectral indices, our results show a reduction of inter-sensor deviation (vegetation pixels only) to 38% of the initial error for NDVI (Normalized Difference Vegetation Index) and to 43% for EVI (Enhanced Vegetation Index). Furthermore, we computed the REIP (Red Edge Inflection Point) with an accuracy of 3.1 nm from Sentinel-2 adapted Landsat-8 data. An exemplary multispectral classification use case revealed an increasing inter-sensor consistency of classification results from 92.3% to 97.3% mean error. Applied to time series of real Landsat-8 and Sentinel-2 data, we observed similar trends, albeit intermingled with non-sensor-induced inconsistencies.

Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping

This paper reports recent advancements in the field of Synthetic Aperture Radar (SAR) for forest mapping by using interferometric short-time-series. In particular, we first present how the interferometric capabilities of the Sentinel-1 satellites constellation can be exploited for the monthly mapping of the Amazon rainforest. Indeed, the evolution in time of the interferometric coherence can be properly modeled as an exponential decay and the retrieved interferometric parameters can be used, together with the backscatter, as input features to the machine learning Random Forests classifier. Furthermore, we present an analysis on the benefits of the use of textural information, derived from Sentinel-1 backscatter, in order to enhance the classification accuracy. These textures are computed through the Sum And Difference Histograms methodology and the final classification accuracy, resulting by adding them to the aforementioned features, is a thematic map that exceeds an overall agreement of 85 % , when validated using the optical external reference Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) map. The experiments presented in the final part of the paper are enriched with a further analysis and discussion on the selected scenes using updated multispectral Sentinel-2 acquisitions.

Spatio-temporal Patterns of Land Use/Land Cover Change in the Bhutan–Bengal Foothill Region Between 1987 and 2019: Study Towards Geospatial Applications and Policy Making

Monitoring of land use and land cover (LULC) change is fundamental aspect of the landscape dynamics or environmental health evaluation at different spatio-temporal scales. Assessment of LULC change is highly imperative in evaluating the environmental and ecosystem management, conservation, land use planning, resource management and overall sustainable environmental management. The rich natural biodiversity zone of Bhutan–Bengal foothill has been considered to assess the LULC change from 1987 to 2019. The principal objective of this study is to identify the rate of transformation of land use and land cover change along with its causes and consequences. ETM, ETM+ and OLI Landsat satellite images of 1987, 2001 and 2019 are used to find out the magnitude of land use and land cover transformation. Maximum likelihood classifier or maximum likelihood classification method has been applied to classify the attributes of LULC change of Bhutan–Bengal foothill. The LULC components are further verified and rectified by reliable statistical error (confused) matrix accuracy assessment techniques to sort out the error incurred during preparation of final spatio-temporal LULC change maps. The result shows that there is a partial change of LULC during the last 3 decades (1987–2019). LULC data of 3 decades reveal a negative change or reduction of areas like vegetation (− 2.93%), agriculture (− 6.955) and plantation (− 0.5%), whereas other three important LULC components such as built-up area (6.44%), barren land (2.71%) and water body (1.29%) take slightly positive trend. Large-scale human encroachment, natural forest habitat fragmentation and conversion have brought rapid transformation from natural landscape to cultural landscape in Bhutan–Bengal foothill. This fundamental research will definitely help to make policy framing holistic management approach.

LAND COVER MONITORING OF LAGUNA LAKE WATERSHED USING MODIS NDVI DATA

Abstract. Land use and land cover monitoring is an important component in the management of Laguna Lake watershed due to its impacts on the lake’s water quality. Due to limitations caused by cloud cover, satellite systems with limited revisit capability fail to provide sufficient data to more effectively monitor the land surface. Normalized difference vegetation index (NDVI) derived from MODIS image data were used to generate land cover maps for the years 2001, 2005, 2009, 2013, and 2017. These were produced by classifying ISODATA classes using annual NDVI profiles, which resulted in land cover classes, namely, agricultural land, built-up, forest, rangeland, water, and wetland. The resulting maps were post-processed using multi-variate alteration detection (MAD), resulting in multi-temporal land cover maps with improved overall accuracies and kappa coefficients that indicate moderate agreement with ground truth data. Spatiotemporal hot spot analysis was also performed using NDVI data from 2001 to 2017 to identify vegetation hot spot areas, where clustering of low NDVI values were observed over the years. Results showed an increasing trend in built-up areas accompanied by decreasing trends in water and wetland areas, indicating impacts caused by land reclamation and expansion of residential subdivisions near the lakeshore. The decrease in total vegetation area from 2001 to 2017 could be attributed to conversion of land to built-up surface. Vegetated areas in identified hot spots decreased from 41% in 2001 to 19% in 2017. This suggests that vegetation cover in these hot spots was converted to non-vegetated surface during the time period studied.

Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping

The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.

Land–Use and Land-Cover Change Detection Using Dynamic Time Warping–Based Time Series Clustering Method

Accurate and timely monitoring of urban land-use and land-cover (LULC) change is useful for understanding the various impacts of human activity on the urban environment. In order to demonstrate the advantage of time series imaging for urban LULC change detection, we selected time series Landsat images over a two-year period to detect inter-annual changes. A time series trajectory for each pixel was developed by the biophysical composition index (BCI) and normalized vegetation index (NDVI) values extracted from Landsat images. Considering that temporal length of trajectories in different years might be different, the dynamic time warping (DTW) measure was selected as the LULC change magnitude indicator. After DTW-based change/unchanged detection, the DTW-based clustering method was used in LULC change type extraction. Finally, the overall accuracy of change/unchanged detection result was 92.3%, and the overall accuracy of all change types was 71%. Some change types that are difficult to extract by bi-temporal images were detected, such as inter-class changes between farmland and forest, and intra-class change of farmland, indicating the advantage of time series information in LULC change detection.

U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV Imagery

Mining activities are the leading cause of deforestation, land-use changes, and pollution. Land use/cover mapping in Vietnam every five years is not useful to monitor land covers in mining areas, especially in the Central Highland region. It is necessary to equip managers with a better tool to monitor and map land cover using high-resolution images. Therefore, the authors proposed using the U-Net convolutional network for land-cover classification based on multispectral Unmanned aerial vehicle (UAV) image in a mining area of Daknong province, Vietnam. An area of 0.5kmx0.8km was used for training and testing seven U-Net models using seven optimizer function types. The final U-Net model can interpret six land cover types: (1) open-case mining lands, (2) old permanent croplands, (3) young permanent croplands, (4) grasslands, (5) bare soils, (6) water bodies. As a result, two models using Nadam and Adadelta optimizer function can be used to classify six land cover types with accuracy higher than 83%, especially in open-case mining lands and polluted streams flowed out from the mining areas. The trained U-Net models can potentially update new land cover types in other mining areas towards monitoring land cover changes in real-time in the future.

APPLICATION OF SENTINEL-2A DATA AND PIXEL-BASED ALGORITHMS FOR LAND COVER MAPPING IN ILAM-IRAN

The western forests and rangelands of Iran in Zagros Mountain are valuable ecosystems for protecting the water and soil resources and providing the habitat for endemic fauna and flora. These ecosystems have widely destroyed by human interferences in the recent years. The land cover mapping using an accurate method is the first step to prevent the further destruction of these ecosystems. The aim of this study was to generate a land cover map of Ilam (Western Iran) using Sentinel-2A data at 10 m spatial resolution using the best suited classification algorithm. For this purpose, the supervised classification of Sentinel-2A image was performed by seven pixel-based algorithms (Maximum Likelihood, Minimum Distance, Spectral Angle Mapper, Spectral Correlation Mapper, Mahalanobis Distance, Neural Network and Support Vector Machine). For accuracy assessment of the land cover maps, the stratified random points were created and controlled in the field. After checking out the current land cover of each point in a plot area at the field, the real land cover of each one was compared with the defined land cover of the same point based on classification maps. Finally, the accuracy of the algorithms was evaluated by accuracy indices. The results showed that Support Vector Machine algorithm had the highest accuracy in classification of Sentinel-2A image with overall accuracy 79% and Kappa Index 0.70. This algorithm usually shows a good efficiency for land cover mapping in the heterogeneous regions like the study area. The analysis of the land cover map obtained from this algorithm showed that the dense, semi-dense and sparse forests have covered 319.64 ha, 361.44 ha and 1832.36 ha of the study area, respectively. The human-made land covers such as agriculture and understory agriculture have widely extended in the study area and have covered 658.42 and 4504.64 ha, respectively. The results of this study could be used as a baseline for managers to monitor land cover changes in the region. For the optimum management of the study area, land cover mapping using SVM algorithm in the certain temporal intervals is recommended to discover the forests change and to control the agriculture development.

Effectiveness of Sentinel-1-2 Multi-Temporal Composite Images for Land-Cover Monitoring in the Indochinese Peninsula

The Indochinese Peninsula, which contains two thirds of the world’s tropical forests, however, is one of the world’s most threatened habitat with some of the highest rates of deforestation and land use changes. Availability of higher resolution satellite data collected by the likes of Landsat 8 and Sentinel-1-2 has brought new opportunities for precise land cover monitoring in recent years. However, utilizing a massive volume of high spatial and temporal resolution data for ecological applications is challenging. One approach is to employ composite images generated from the multi-temporal satellite data. The research was conducted in two study sites located in the Indochinese Peninsula, Laos-Thailand and Vietnam-Cambodia, vulnerable to deforestation and land use changes. We assessed the potential of recently available composite images, such as Biophysical Image Composite (BIC), Forest Cover Composite (FCC), Enhanced Forest Cover Composite (EFCC), and Water Cover Composite (WCC) for the classification and mapping of land cover types. Three machine learning classifiers, k-Nearest Neighbors (KNN), Support Vector Machines (SVM) and Random Forests (RF) were employed and the performance of composite images was evaluated quantitatively with the support of ground truth data. The overall accuracies (Kappa coefficient) obtained from the combination of composite images were 0.92 (0.89) and 0.90 (0.86) for Laos-Thailand, and Vietnam sites respectively. These results highlight effectiveness of the composite images for the classification and mapping of land cover types.

Combining Unmanned Aerial Systems and Satellite Data to Monitor Phenological Changes in Tropical Forests: A Case Study from Costa Rica

The migration of vegetation under the influence of climate change is of great interest to ecologists, but can be difficult to quantify—especially in less accessible landscapes. Monitoring land cover change through remote sensing has become the best solution, especially with the use of unmanned aerial systems (UASs; drones) as low-cost remote sensing platforms are able to collect data at high spatial and spectral resolutions. Unfortunately, in the context of climate change studies, the lack of comparative UAS data sets over decadal timescales has been limiting. Here, we describe a technique for the integration of historical, low spatial resolution satellite-based Normalized Difference Vegetation Index (NDVI) data with short-term high-resolution multispectral UAS data to track the vegetation changes in a Costa Rican rainforest over a 33-year time frame. The study reveals the transition of a mixed forest from strongly deciduous to weakly deciduous phenology in the Hacienda Barú National Wildlife Refuge (HBNWR), southwestern Costa Rica. This case study presents an approach for researchers and forest managers to study and track vegetation changes over time in locations that lack detailed historical vegetation data. Vegetation migration due to climate change is not well documented and difficult to monitor, especially in remote or inaccessible locations. This case study presents researchers, students, and forest managers an approach for leveraging freely available satellite imagery and UASs to track these changes over time.

The Rate of Land Cover Change using Landsat Data in Coal Mining Area of Sawah Lunto City, Indonesia

Sawahlunto city is well-known for coal mining region. As in 2010, there have been at least 12 mining companies exploring coal resources in the region. As time passes, land cover conditions have gradually decreased due to mining activities. This region on which was originally covered by various vegetation and ecosystem have systematically transformed into open areas for coal mining. The use of remote sensing technology for land cover monitoring has been commonly well-developed in accordance with the need for improvement of detailed information about the changes of land use coverage. This study examines the land cover changes using supervised classification method based on Landsat data. The method focuses on four dominant classes of land cover in the region, namely forest, mining, settlement, and water resources. The classification processes were performed based on true-color composite satellite images. The results show that the overall accuracies of classification are 91.68 %, 92.49 %, 93.69 %, and 93.74 % in 2000, 2006, 2011, and 2016 respectively. It was also found that forest is the largest area in the coal mining area which, in some sense, tends to continuously decrease in terms of land cover in the last 15 years. The rate of forest area degradation achieved its maximum between 2006 and 2011.

Monitoring land cover change detection with NOAA-AVHRR and MODIS remotely sensed data in the North and West of Africa from 1982 to 2015.

Mapping land cover changes (LCC) cover three decades over North and West Africa regions provides critical insights for the climate research that inspects the land-atmosphere interaction. LCC is a serious problem in the Earth science domain for this impacts the regional climate by modifying the distribution of terrestrial carbon stocking and roughness of the Earth's surface. In this study, the normalized difference vegetation index (NDVI) generated from advanced very high resolution radiometer (AVHRR) was used to produce a continuous set of annual land cover (LC) maps of land cover over North and West Africa between 1982 and 2015, based on the random forest classification. We used the MODIS land cover product (MCD12Q1) as a reference data for training the classifier. The result has validated using annual LC maps listed by time series and the spatio-temporal dynamics of land cover has illustrated over the last three decades. The comparison with Google Earth image 2015 shows that the overall accuracy of the simpler nine-class type of our land cover 2015 map is 76% and 2% higher than that of the MODIS map of the same year. The detection of changes indicated that over the last three decades, the urban and built-up, barren or sparsely vegetated, savannas and deciduous broadleaf forest have increased; in contrast, the open shrublands, woody savannas and water bodies have decreased.

Analysis of land use land cover changes in Thirthahalli taluk using remote sensing and GIS

Mapping and monitoring of land use land cover is essential for many management and planning activities as it is considered as an important element for understanding the earth and its whole system. The present study focused on the utilization of remote sensing and GIS technology for land use land cover classification and its change analysis for local areas like Thirthahalli over the period of two decades. Multitemporal satellite imagery of the Landsat series was used to map land use land cover changes in the years 1997 and 2017. Supervised classification using Maximum Likelihood technique was used resulting in the classes: forest, agricultural plantations, agricultural croplands, wastelands, water bodies and settlement areas. The study showed the major changes occurred in forest, agricultural plantations and settlement classes. Modifications in the pattern of agricultural activities are the main reasons for the changes in land use land cover distributions in the study area during the study period. Keywords: LULC; Change Detection; Remote Sensing; GIS; Thirthahalli

Cartografia do Invisível: Revelando a Agricultura de Pequena Escala com Imagens Rapideye na Região do Baixo Tocantins, Pa.

Dados derivados de sensoriamento remoto aliados ao uso de técnicas de classificação digital de imagens fornecem uma visão sinóptica e informações sobre a dimensão temporal dos fenômenos espaciais, possibilitando gerar informações sobre a dinâmica e os padrões espaciais da paisagem em áreas de grandes extensões territoriais como a Amazônia. Nos mapeamentos extensivos, como os realizados com imagens de satélite pelo INPE para a Amazônia Legal, é comum a definição e descrição de classes de uso e cobertura da terra em função da resolução espacial e radiométrica dos sensores utilizados. Por essa razão, pequenas áreas que não atingem o tamanho mínimo da área de mapeamento e/ou com usos diversificados são, em geral, agregadas em uma única classe. Esse é o exemplo da classe denominada Mosaico de Ocupação presente na legenda do sistema de monitoramento do uso e cobertura da terra da Amazônia-TerraClass. Esta classe procura representar, em parte, a agricultura familiar, no entanto, como o mapeamento é feito pelo sensor TM ou OLI dos satélites da série Landsat, com resolução espacial de 30m e como o TerraClass define uma área mínima de mapeamento de 6,25 hectares (ha), a identificação de pequenas áreas agrícolas é comprometida, pois essas categorias são agregadas em classes mistas e não são mais distinguíveis. Para estudos que procuram dar visibilidade aos padrões espaciais de atividades de produção de pequena escala torna-se necessário realizar o refinamento dessas classes com dados de resolução espacial com melhor definição. Neste contexto, o objetivo desse trabalho é testar três algoritmos semiautomáticos de classificação, baseados em pixel e em regiões: os algoritmos MAXVER, Bhattacharya e K-Vizinho Mais Próximo (KNN), para avaliar a capacidade de refinamento da classe Mosaico de Ocupação do dado produzido pelo TerraClass-2014. A área de estudo compreende parte dos municípios de Cametá, Mocajuba e Baião, localizados na região Nordeste do Pará, onde a produção de mandioca, pimenta-do-reino, cacau e açaí têm grande importância econômica para população local. Para o mapeamento das categorias contidas na classe Mosaico de Ocupação, foram utilizadas imagens do RapidEye, sensor REIS, ortoimagens com 5m de resolução espacial. Foi estimada a exatidão global dos algoritmos testados obtendo-se índices de 26%, 38% e 78%, para os algoritmos MAXVER, Bhattacharya e K-Vizinho Mais Próximo, respectivamente. Além da maior exatidão Global (78%), o algoritmo K-Vizinho Mais Próximo apresentou melhores resultados relativo às classes de vegetação secundária, hidrografia, e pasto sujo, com mais de 90% de acerto. A classe agricultura anual de pequena escala apresentou acerto de 62%, enquanto o índice de acerto dos outros dois algoritmos testados não passou de 8%. A abordagem metodológica desenvolvida demonstrou a viabilidade do uso das imagens de alta resolução espacial e de métodos semiautomáticos para a classificação de classes de uso e cobertura da terra associadas à classe Mosaico de Ocupação do TerraClass. A metodologia pode ser utilizada para complementar às atuais bases de dados existentes para a Amazônia (TerraClass, MapBiomas e IBGE), explicitando as categorias agrícolas de produção em pequena escala, dando visibilidade a esses sistemas de produção, frequentemente negligenciados nos mapeamentos que abrangem grandes extensões territoriais.

Linking Earth Observations for Assessing the Food Security Situation in Vietnam: A Landscape Approach

Land cover change and its impact on food security is a topic that has major implications for development in population-dense Southeast Asia. The main drivers of forest loss include the expansion of agriculture and plantation estates, growth of urban centers, extraction of natural resources, and water infrastructure development. The design and implementation of appropriate land use policies requires accurate and timely information on land cover dynamics to account for potential political, economical, and agricultural consequences. Therefore, SERVIR-Mekong led the collaborative development of a Regional Land Cover Monitoring System (RLCMS) with key regional stakeholders across the greater Mekong region. Through this effort, a modular system was used to create yearly land cover maps for the period 1988 - 2017. In this study, we compared this 30-year land cover time-series with Viet Nam national forest resources and agricultural productivity statistics. We used remote sensing-derived land cover products to quantify landscape changes and linked those with food availability, one of food security dimension, from a landscape approach perspective. We found that agricultural production has soared while the coverage of agricultural areas has remained relatively stable. Land cover change dynamics coincide with important legislation regarding environmental management and sustainable development strategies in Viet Nam. Our findings indicate that Vietnam has made major steps towards improving its' food security. We demonstrate that RLCMS is a valuable tool for evaluating the relationship between policies and their impacts on food security, ecosystem services and natural capital.

An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data

Access to temporally dense time series such as data from the Landsat and Sentinel-2 missions has lead to an increase in methods which aim to monitor land cover change on a per-acquisition rather than a yearly basis. Evaluating the accuracy and limitations of these methods can be difficult because validation data are limited and often rely on human interpretation. Simulated time series offer an objective method for evaluating and comparing between change detection algorithms. A set of simulated time series was used to evaluate four change detection methods: (1) Breaks for Additive and Seasonal Trend (BFAST); (2) BFAST Monitor; (3) Continuous Change Detection and Classification (CCDC); and (4) Exponentially Weighted Moving Average Change Detection (EWMACD). In total, 151,200 simulations were generated to represent a range of abrupt, gradual, and seasonal changes. EWMACD was found to give the best performance overall, correctly identifying the true date of change in 76.6% of cases. CCDC performed worst (51.8%). BFAST performed well overall but correctly identified less than 10% of seasonal changes (changes in amplitude, length of season, or number of seasons). All methods showed some decrease in performance with increased noise and missing data, apart from BFAST Monitor which improved when data were removed. The following recommendations are made as a starting point for future studies: EWMACD should be used for detection of lower magnitude changes and changes in seasonality; CCDC should be used for robust detection of complete land cover class changes; EWMACD and BFAST are suitable for noisy datasets, depending on the application; and CCDC should be used where there are high quantities of missing data. The simulated datasets have been made freely available online as a foundation for future work.

Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry

Land cover monitoring is a major task for remote sensing. Comparing to traditional methods for forests monitoring which mostly use orthoimages from satellite or aircraft, there are very few researches use forest 3D canopy structure to monitor the forest growth. UAV aerial can be a novel and feasible platform to provide high resolution and more timely images that can be used to generate high resolution forest 3D canopy. In spring, the small forest is supposed to experience rapid growth. In this research, we used a small UAV to monitor campus forest growth in spring at 2days interval. Each time 140 images were acquired and the ground surface dense point cloud was reconstructed at high precision. Color indexes ExG (Excess Green) was used to extract the green canopy point. The segmented point cloud was triangulated using greedy projection triangulation method into a mesh and its area was calculated. Forest canopy growth was analyzed at 3 level: forest level, selected group level and individual tree level. Logistic curve was used to fit the time series canopy growth. Strong correlation was found R2 = 0.8517 at forest level, R2=0.9652 at selected group level and R2 = 0.9606 at individual tree level. Moreover, high correlation was found between canopy by observing these results, we can conclude that the ground 3D model can act as a useful data type as orthography to monitor the forest growth. Moreover the UAV aerial remote sensing has advantages at monitoring forest in periods when the ground vegetation is growing and changing fast.

Land-cover Monitoring Using Time Series of Satellite Images Based on Harmonic Analysis

Remote Sensing is a useful tool to monitor land covers observing the earth's surface. Multi-temporal approach based on the temporal profile allows us to get information about the earth and human activities from a long-term perspective. The goal of this study is to develop an efficient method for land-cover monitoring using time series of satellite images. A harmonic model can characterize the temporal variability with four components: mean level, frequency, phase and amplitude. The components of the harmonic function inherently contain temporal information about seasonal changes. Seasonal periodicity can be incorporates into multi-temporal classification. In this research, a classification method using harmonic model is proposed. The method is tested and evaluated with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data, one of the most useful remotely-sensed imagery. Satellite data inevitably contain disturbances caused by atmospheric effects and surface anisotropy scattering, which impede the analysis of time series data. The quality of data streams is important for the sequential data analysis. A filtering method was applied to reconstruct high-quality data stream. Then the proposed classification method was performed over the Korean peninsula from 2012 to 2016, where land-cover types were classified both with the estimated harmonic components using an unsupervised classification approach. The results of the classification show that the new approach has a great potential for land-cover monitoring

Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities

Land cover maps are a critical component to make informed policy, development, planning, and resource management decisions. However, technical, capacity, and institutional challenges inhibit the creation of consistent and relevant land cover maps for use in developing regions. Many developing regions lack coordinated capacity, infrastructure, and technologies to produce a robust land cover monitoring system that meets land management needs. Local capacity may be replaced by external consultants or methods which lack long-term sustainability. In this study, we characterize 9 and respond to the key land cover mapping gaps and challenges encountered in the Lower Mekong (LMR) and Hindu Kush-Himalaya (HKH) region through a needs assessment exercise and a collaborative system design. Needs were assessed using multiple approaches, including focus groups, user engagement workshops, and online surveys. Efforts to understand existing limitations and stakeholder needs resulted in a co-developed and modular land cover monitoring system which utilizes state-of-the-art cloud computing and machine learning which leverages freely available Earth observations. This approach meets the needs of diverse actors and is a model for transnational cooperation.

Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding

Efficient Land Use and Land Cover (LULC) monitoring and management require awareness of previous dynamics, current trends, and predictions of future developments. Understanding such an urban dynamics is, thus, necessary to deliberate a proper urban growth management approach. The study is aimed to simulate the LULC dynamics and develop a scenario-based LULC prediction for sustainable urban growth planning and management in the case of Addis Ababa and the surrounding area. The research employed a hybrid Cellular Automata, Markov chain (CA-Markov) and Multi-criteria Analytical Hierarchy Process (AHP) modeling approach. Accordingly, the research depicted continuous historical increment of Built-up spaces by consuming other ecologically valuable LULC classes. The quantitative measures of landscape metrics confirmed the benefit of Ecologically Sensitive Scenario (ESS) modeling as compared to Business As Usual Scenario (BAUS) as it keeps the dynamism of the city region more sustainable. ESS modeling enables an urban system to grow into a better way by making built-up augmentation relatively mild and controlling water bodies, forests and cultivated land losses. Therefore, this scenario-based simulation of the LULC dynamics providing decision-making options for those who strive for sustainable urban growth planning and management not only in the study region but also other similar cities.