Land Cover Change Mapping Research Articles

Monitoring Approach for Tropical Coniferous Forest Degradation Using Remote Sensing and Field Data

Current estimates of CO2 emissions from forest degradation are generally based on insufficient information and are characterized by high uncertainty, while a global definition of ‘forest degradation’ is currently being discussed in the scientific arena. This study proposes an automated approach to monitor degradation using a Landsat time series. The methodology was developed using the Google Earth Engine (GEE) and applied in a pine forest area of the Dominican Republic. Land cover change mapping was conducted using the random forest (RF) algorithm and resulted in a cumulative overall accuracy of 92.8%. Forest degradation was mapped with a 70.7% user accuracy and a 91.3% producer accuracy. Estimates of the degraded area had a margin of error of 10.8%. A number of 344 Landsat collections, corresponding to the period from 1990 to 2018, were used in the analysis. Additionally, 51 sample plots from a forest inventory were used. The carbon stocks and emissions from forest degradation were estimated using the RF algorithm with an R2 of 0.78. GEE proved to be an appropriate tool to monitor the degradation of tropical forests, and the methodology developed herein is a robust, reliable, and replicable tool that could be used to estimate forest degradation and improve monitoring, reporting, and verification (MRV) systems under the reducing emissions from deforestation and forest degradation (REDD+) mechanism.

Response of topographic control on nearest-neighbor diffusion-based pan-sharpening using multispectral MODIS and AWiFS satellite dataset

Remote sensing plays a significant role in monitoring of the undulating the Himalayas. With continuous monitoring, the preservation of natural resources and mitigation of natural hazards is possible. Currently, satellite sensors are not capable enough to deliver the earth surface image at a very high temporal, spectral, and spatial resolution, simultaneously. Therefore, it is essential to perform the pan-sharpening of spatially high-resolution (HR) panchromatic (PAN) spectral band with low-resolution (LR) multispectral (MS) imagery which must be acquired on the same temporal date from multiple sensors. On the other hand, due to the rugged topography of the Himalayas, topographic effects are generally induced in the form of shadow and affect the spatial information or spectral information. Therefore, the focus of the present work is to implement and analyze the performance of topographic correction on nearest-neighbor diffusion (NND)-based pan-sharpening algorithm. In order to evaluate the effectiveness, K-mean classification (KMC) has been implemented over topographically corrected and topographically uncorrected NND pan-sharpened images. From the experimental outcomes, it is inferred that topographically corrected NND pan-sharpened classified image has achieved better accuracy (81.33%) as compared with topographically uncorrected NND pan-sharpened classified imagery (78%). It is expected that the integration of topographic correction and NND algorithm facilitates the better extraction of spectral and spatial information and leads to improvement in results for further analysis such as change detection procedure and classification. The applications of the present study are in monitoring of climate change and mapping land cover changes over rugged terrain regions.

A spatiotemporal structural graph for characterizing land cover changes

ABSTRACT Characterizing landscape patterns and revealing their underlying processes are critical for studying climate change and environmental problems. Previous methods for mapping land cover changes largely focused on the classification of remote sensing images. Therefore, they could not provide information about the evolutionary process of land cover changes. In this paper, we developed a spatiotemporal structural graph (STSG) technique for a comprehensive analysis of land cover changes. First, a land cover neighborhood graph was generated for each snapshot to quantify the spatial relationship between adjacent land cover objects. Then, an object-based temporal tracking algorithm was designed to monitor the temporal changes between land cover objects over time. Finally, land cover evolutionary trajectories, pixel-level land cover change trajectories, and node-wise connectivity changes over time were characterized. We applied the proposed method to analyze land cover changes in Suffolk County, New York from 1996 to 2010. The results demonstrated that STSG can not only characterize and visualize detailed land cover changes spatially but also maintain the temporal sequence and relations of land cover objects in an integrated space-time environment. The proposed STSG provides a useful framework for analyzing land cover changes and can be adapted to characterize and quantify other spatiotemporal phenomena.

A Hierarchical Clustering Method for Land Cover Change Detection and Identification

A method to detect abrupt land cover changes using hierarchical clustering of multi-temporal satellite imagery was developed. The Autochange method outputs the pre-change land cover class, the change magnitude, and the change type. Pre-change land cover information is transferred to post-change imagery based on classes derived by unsupervised clustering, enabling using data from different instruments for pre- and post-change. The change magnitude and change types are computed by unsupervised clustering of the post-change image within each cluster, and by comparing the mean intensity values of the lower level clusters with their parent cluster means. A computational approach to determine the change magnitude threshold for the abrupt change was developed. The method was demonstrated with three summer image pairs Sentinel-2/Sentinel-2, Landsat 8/Sentinel-2, and Sentinel-2/ALOS 2 PALSAR in a study area of 12,372 km2 in southern Finland for the detection of forest clear cuts and tested with independent data. The Sentinel-2 classification produced an omission error of 5.6% for the cut class and 0.4% for the uncut class. Commission errors were 4.9% for the cut class and 0.4% for the uncut class. For the Landsat 8/Sentinel-2 classifications the equivalent figures were 20.8%, 0.2%, 3.4%, and 1.6% and for the Sentinel-2/ALOS PALSAR classification 16.7%, 1.4%, 17.8%, and 1.3%, respectively. The Autochange algorithm and its software implementation was considered applicable for the mapping of abrupt land cover changes using multi-temporal satellite data. It allowed mixing of images even from the optical and synthetic aperture radar (SAR) sensors in the same change analysis.

An assessment of potential climate impact during 1948–2010 using historical land use land cover change maps

AbstractEarlier studies of land use land cover change (LULCC) normally used only a specified LULCC map with no interannual variations. In this study, using an Atmospheric General Circulation Model (AGCM) coupled with a land surface model, biophysical impacts of LULCC on global and regional climate are investigated by using a LULCC map which covers 63 years from 1948 to 2010 with interannual variation. A methodology has been developed to convert a recently developed LULCC fraction map with 1° × 1° resolution to the AGCM grid points in which only one dominant type is allowed. Comprehensive evaluations are conducted to ensure consistency of the trend of the original LULCC fraction change and the trend of the fraction of grid point changes over different regions. The model was integrated with a potential vegetation map (CTL) and the map with LULCC, in which a set of surface parameters such as leaf area index, albedo and other soil and vegetation parameters were accordingly changed with interannual variation. The results indicate that the interannual LULCC map simulation is able to reproduce better interannual variability of surface temperature and rainfall when compared to the control simulation. LULCC causes negative effect on global precipitation, with the strongest significant signals over degraded regions such as East Asia, West Africa and South America, and some of these changes are consistent with observed regional anomalies for certain time periods. LULCC causes reduction in net radiation and evapotranspiration which leads to changes in monsoon circulation and variation in magnitude and pattern of moisture flux convergence and subsequent reduction in precipitation. Meanwhile, LULCC enhances surface warming during the summer in the LULCC regions due to greatly reduced evapotranspiration. In contradiction to the surface, upper troposphere temperatures are cool because of less latent heat released into the upper troposphere, which leads to weaker circulation in LULCC regions.

Survey of Community Livelihoods and Landscape Change along the Nzhelele and Levuvhu River Catchments in Limpopo Province, South Africa

Landscape-change studies have attracted increasing interest because of their importance to land management and the sustainable livelihoods of rural communities. However, empirical studies on landscape change and its drivers are often poorly understood, particularly, in small rural communities in developing countries such as South Africa. The present study surveyed local community livelihoods and perceptions of landscape change in the Nzhelele and Levuvhu river catchments in Limpopo Province, South Africa. These areas have experienced land reform and are also characterized by environmental degradation, poverty, inequality and environmental justice concerns among other issues. Land-cover maps derived from Landsat satellite imagery were used for purposes of correlating and validating the survey data findings and results. The survey results showed that education levels, working status and marital status have statistically significant effects on community livelihoods (indicated by levels of income, p < 0.05). Maize, fruits and vegetables are the main cultivated crop varieties in the study area, and these crops are mainly used for subsistence to meet household self-consumption requirements. Moreover, local community members and stakeholders argue that the landscape has changed over the past 20 years mainly as a result of urban expansion, deforestation, agricultural diversification and forestry intensification. These landscape changes were largely confirmed by the land-cover change maps derived from satellite imagery. Soil erosion as a result of landscape changes was identified as a major threat and hazard in the study area. Political, natural, economic and cultural factors have been identified as the major underlying drivers for the observed landscape changes. These results have implications for understanding landscape change, coupled with human–nature relationships as well as informing government policy with respect to advancing land management and further promotion of the sustainable livelihoods of rural communities. Overall, the study proposes a multiple stakeholders’ approach and ecosystem-based approach to promote the sustainable management of landscapes in rural areas.

Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery

Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.

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.

Mapping Land Cover Change over a 25-Year Period (1993–2018) in Sri Lanka Using Landsat Time-Series

Land use and land cover change (LULCC) are dynamic over time and space due to human and biophysical factors. Accurate and up-to-date LULCC information is a mandatory part of environmental change analysis and natural resource management. In Sri Lanka, there is a significant temporal gap in the existing LULCC information due to the civil war that took place from 1983 to 2009. In order to fill this gap, this study presents a whole-country LULCC map for Sri Lanka over a 25-year period using Landsat time-series imagery from 1993 to 2018. The LandTrendr change detection algorithm, utilising the normalised burn ratio (NBR) and normalised difference vegetation index (NDVI), was used to develop spectral trajectories over this time period. A land cover change and disturbance map was created with random forest, using 2117 manually interpreted reference pixels, of which 75% were used for training and 25% for validation. The model achieved an overall accuracy of 94.14%. The study found that 890,003.52 hectares (ha) (13.5%) of the land has changed, while 72,266.31 ha (1%) was disturbed (but not permanently changed) over the last 25 years. LULCC was found to concentrate on two distinct periods (2000 to 2004 and 2010 to 2018) when social and economic stability allowed greater land clearing and investment opportunities. In addition, LULCC was found to impact forest reserves and protected areas. This new set of Sri Lanka-wide land cover information describing change and disturbance may provide a reference point for policy makers and other stakeholders to aid in decision making and for planning purposes.

Mapping Annual Land Use and Land Cover Changes in the Yangtze Estuary Region Using an Object-Based Classification Framework and Landsat Time Series Data

A system understanding of the patterns, causes, and trends of long-term land use and land cover (LULC) change at the regional scale is essential for policy makers to address the growing challenges of local sustainability and global climate change. However, it still remains a challenge for estuarine and coastal regions due to the lack of appropriate approaches to consistently generate accurate and long-term LULC maps. In this work, an object-based classification framework was designed to mapping annual LULC changes in the Yangtze River estuary region from 1985–2016 using Landsat time series data. Characteristics of the inter-annual changes of LULC was then analyzed. The results showed that the object-based classification framework could accurately produce annual time series of LULC maps with overall accuracies over 86% for all single-year classifications. Results also indicated that the annual LULC maps enabled the clear depiction of the long-term variability of LULC and could be used to monitor the gradual changes that would not be observed using bi-temporal or sparse time series maps. Specifically, the impervious area rapidly increased from 6.42% to 22.55% of the total land area from 1985 to 2016, whereas the cropland area dramatically decreased from 80.61% to 55.44%. In contrast to the area of forest and grassland, which almost tripled, the area of inland water remained consistent from 1985 to 2008 and slightly increased from 2008 to 2016. However, the area of coastal marshes and barren tidal flats varied with large fluctuations.

Remote Sensing Mapping of Land Use and Land Cover Changes in Upper Casamance, Senegal, 1987-2018

In order to study the evolution of land use and the changes in land cover in Upper Casamance, from Landsat images acquired in 1987 and 2018, we used a method of multistage unsupervised classification. Recently developed to mapping and quantification of vegetation land cover changes in West-African coastal, this method is based on the following stages: first classification (K-means) over 15 classes, interpretation of the spectral signatures of the resulting classes followed by reclassification according to the chosen nomenclature, creation of thematic masks, classifications on masks, and finally reclassification. With the only difference, our study aims also to contribute, methodologically, to the knowledge of the spectral profiles of plant formation types. On a thematic level, the results show that between 1987 and 2018, forest areas decreased by 377,118.7 ha (or 27.4%), while wooded areas and agricultural and soil surfaces increased by 263,172.4 ha (or 19.1%) and 1560 75.5 ha (11.3%). In terms of change, deforestation by increasing agricultural and soil surfaces is noted along the border with the Gambia, savanization and anthropization is noted along the central part, and stability of forest surfaces followed by a slight increase in savannas and agricultural and soil surfaces is noted in the south.

Zmeny krajinnej pokrývky vo veľkých mierkach s využitím starých máp: prípadová štúdia mestskej časti Bratislava-Devín

The maps record a generalized state of a landscape at a particular time. Therefore, the old maps are useful for the study of land cover changes. More recent land cover state is captured by the orthophotomaps showing the reality without generalisation. However, there are limitations with this source of spatial information to identify certain objects in the forest, such as rivers or unpaved roads. The paper aims to present a detailed reconstruction of land cover changes, including the characteristics of each change that has occurred in a case study of the Bratislava-Devín city district. Five different map sources were used covering the period from 1920 to 2009 from the newest to the oldest: Slovak state database for the geographic information system (ZBGIS), digital orthophotomap of the Slovak Republic (DGO SR), Basic map of the Slovak Republic (ZM SR), Military topographic map in the Gauss-Krüger projection (TM G-K), and the Third military survey maps of the Habsburg Empire in an updated version (VTM). Mapping of land cover changes was performed at the fifth hierarchical level of the Corine Land Cover at a scale of 1:10,000. First, the maps were individually visually interpreted and individual land cover classes were identified. Further, the land cover changes were identified by retrospective comparison of the individually interpreted maps approaching from youngest to oldest. The results show a significant occurrence of orchards and vineyards in the monitored area and the occurrence of several processes: urbanization, intensification of agriculture, and afforestation.

Evaluation of tree-base data mining algorithms in land used/land cover mapping in a semi-arid environment through Landsat 8 OLI image; Shiraz, Iran

Land Use Land Cover (LULC) mapping has been used in different environmental applications including disaster management, risk analysis, heat island mapping, and the effects of urbanization on environmental changes such as floods and droughts in the recent decade. The earth’s natural surface coverage including urban infrastructure, surface vegetation, bare soil can be identified with LULC maps. Besides, LULC is one of the most important tasks in natural hazards, planning activities, resource management, and global monitoring studies. The present study aimed to improve the level of land used and land cover change mapping in a semi-arid environment using Landsat 8 Operational Land Imager (OLI) image. In this research, several tree-based algorithms for LULC mapping are used and compared. These algorithms are used in a combination model implementation of WEKA 3.8 and R programming language to provide a method of a fit-for-purpose algorithms for LULC mapping. It is found that Reduced Error Pruning Tree (REP Tree) is the best approach according to the training and test datasets which are four classes including the build-up, soil, roads, and vegetation region pixels in a semi-arid environment. For the training dataset, the best values of Overall Accuracy (OA), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) is determined, 99.7313, 0.002 and 0.0354 as well as for the test dataset are 99.7313, 0.002 and 0.0354, respectively. Finally, trained models of the implemented data mining algorithms are used for the classification of a different dataset without using any training data where the Logical Analysis of Data Tree (LAD Tree) algorithm shows the best performance in terms of OA, MAE, and RMSE with values of 99.2815, 0.0057, and 0.0557 respectively.

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.

Land cover change effects on land surface temperature trends in an African urbanizing dryland region

Land use-land cover (LULC) association with land surface temperature (LST) is well known. Knowledge about land change effects on LST in urbanizing African drylands is very limited. We examined LST and vegetation variations in semi-arid Gaborone (Botswana's capital) using MODIS daytime and night-time LST (DNLST), and Normalized Difference Vegetation Index (NDVI) between 2000 and 2018. Significant land transitions were identified in the land cover change map using Change Vector Analysis of Landsat-based biophysical indices of vegetation, water and bare soil. Artificial surface and tree-covered areas were net gaining categories, whereas cropland and grassland were net losing categories. Detailed profiling of DNLST trends and breakpoints was conducted in five relatively homogenous sites representing land cover/transitions. Increasing NDVI and DNLST trends found were significant. Per class, LST change at daytime and night-time are as follows: built-up areas (1.8 K, 2.2 K), Gaborone dam (5.7 K, 0.2 K), settlement expansion areas (4.6 K, 2.2 K), and rural settlement (2.0 K, 1.5 K). The cooling effect of irrigation on daytime LST was higher than night-time LST as daytime LST trend as low as −0.4 K was found in areas of irrigated croplands. Validation with synoptic station temperature data and dam water levels provides empirical evidence that MODIS gave credible DNLST estimates in this urbanizing dryland area. Our results also suggest the role of climate variability in urbanizing drylands alongside land cover change in controlling the LST. Regardless, coupling DNLST and land cover changes can provide useful information for spatial planning of drylands to create smart cities that are resilient to climate change.

Multi-temporal Land-Cover Classification of Kinabalu Eco-Linc Site and the Protected Park Areas

The Crocker Range Park and Kinabalu Park are Sabah’s largest terrestrial parks physically separated by approximately 10 km. Both parks are involved in a set up plan of ecological linkages to connect and further strengthen the biodiversity conservation efforts in the State of Sabah. The part of ecological linkages project is known as Kinabalu Eco-Linc (Kinabalu Ecological Linkage). This study is designed to monitor the land use change of the area between year 1991 - 2018 using Landsat imagery. Maximum likelihood classifier was used to create the land cover change map of both protected areas. Seven land cover type were identified in the area which comprises of primary forest, secondary forest, shrubland/grassland, barren land, agriculture, plantation, and river. The result illustrated a drastic declined of primary forest and increased secondary forest and agricultural over the period of 28-years assessed. The spatial changes that occur throughout the period within state park and KEL area is drives by natural and anthropogenic activities. To support the increase in local population and their demand, the natural environment underwent changes for their welfare improvement. It is concluded that, monitoring protected area using remote sensing technique provide useful spatiotemporal data to locate key areas that are vulnerable to threat and can be utilized for better management of both protected areas and human use resources in adjacent area.

Spatial–Temporal Analysis of Land Cover Change at the Bento Rodrigues Dam Disaster Area Using Machine Learning Techniques

Disasters are an unpredictable way to change land use and land cover. Improving the accuracy of mapping a disaster area at different time is an essential step to analyze the relationship between human activity and environment. The goals of this study were to test the performance of different processing procedures and examine the effect of adding normalized difference vegetation index (NDVI) as an additional classification feature for mapping land cover changes due to a disaster. Using Landsat ETM+ and OLI images of the Bento Rodrigues mine tailing disaster area, we created two datasets, one with six bands, and the other one with six bands plus the NDVI. We used support vector machine (SVM) and decision tree (DT) algorithms to build classifier models and validated models performance using 10-fold cross-validation, resulting in accuracies higher than 90%. The processed results indicated that the accuracy could reach or exceed 80%, and the support vector machine had a better performance than the decision tree. We also calculated each land cover type’s sensitivity (true positive rate) and found that Agriculture, Forest and Mine sites had higher values but Bareland and Water had lower values. Then, we visualized land cover maps in 2000 and 2017 and found out the Mine sites areas have been expanded about twice of the size, but Forest decreased 12.43%. Our findings showed that it is feasible to create a training data pool and use machine learning algorithms to classify a different year’s Landsat products and NDVI can improve the vegetation covered land classification. Furthermore, this approach can provide a venue to analyze land pattern change in a disaster area over time.

Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran

Land use land cover change mapping has been used for monitoring environmental changes as an essential factor to study on the earth’s surface land cover in the field of climate change phenomena such as floods and droughts. Remote sensing images have been suggested to present inexpensive and fine-scale data offering multi-temporal coverage. This tool is useful in the field of environmental monitoring, land-cover mapping, and urban planning. This study aims to evaluate eight machine learning algorithms for image classification implemented in WEKA and R programming language. Firstly, Landsat 8 OLI/TIRS Level-2 images based on eight machine learning techniques including Random Forest, Decision Table, DTNB, J48, Lazy IBK, Multilayer Perceptron, Non-Nested Generalized Exemplars (NN ge), and Simple Logistic are classified. Then, obtained results are compared in term of Overall Accuracy (OA), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) for land use land cover mapping. Among the eight machine learning algorithms used for image classification based on the training and test dataset, NN ge classifier is ranked first with values of 100, 0, and 0 for Overall Accuracy, Mean Absolute Error and Root Mean Squared Error respectively. All machine learning algorithms had an Overall Accuracy of more than 99% for the training dataset. On the other hand, for the test dataset, J48 and DTNB algorithms had the worst performance with values of 88.1188 and 76.9802 respectively for the Overall Accuracy.

AN ASSESSMENT OF LAND COVER CHANGE DYNAMICS OF GAJA CYCLONE IN COASTAL TAMIL NADU, INDIA USING SENTINEL 1 SAR DATASET

Abstract. Land cover change is a dynamic phenomenon addressing environmental issues including natural calamities. Recent advancements in geospatial technology and availability of remote sensor data have fostered monitoring and mapping of land cover changes more precisely. Remote sensing is widely used where emerging research findings are focused mainly on coastal hazard studies. Tropical cyclones being an extreme weather event are more powerful and hazardous to southern parts of the Indian subcontinent. Aftermath of the cyclone is extreme causing land cover changes like defoliation, water logging, destruction of cultivable lands, plantations shrub vegetation, dissolving salt pans etc. The tropical cyclones are fierce to devastate the coastal districts of Tamil Nadu and make it a prey to these cyclones. In this paper, an attempt has been made to assess the pre and post cyclonic land cover change by utilizing potential microwave Synthetic Aperture Radar (SAR) dataset. The study portrays the occurrence of a severe cyclonic storm named Gaja that was formed over Bay of Bengal which hit Tamil Nadu on 15th of November 2018 causing high death toll and demolition. The study focuses on the pre and post damage assessment provoked by Gaja cyclone. For analysis, a methodical procedure was followed by utilizing the Sentinel 1 SAR dataset. Random Forest (RF) classifier approach was incorporated for mapping land cover types as it reduces the variance among the classes thus yielding accurate predictions. Results demonstrate that classified imagery using dual polarization SAR dataset outperforms well for RF classifier thus escalating the overall accuracy.

Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform

As more data and technologies become available, it is important that a simple method is developed for the assessment of land use changes because of the global need to understand the potential climate mitigation that could result from a reduction in deforestation and forest degradation in the tropics. Here, we determined the threshold values of vegetation types to classify land use categories in Cambodia through the analysis of phenological behaviors and the development of a robust phenology-based threshold classification (PBTC) method for the mapping and long-term monitoring of land cover changes. We accessed 2199 Landsat collections using Google Earth Engine (GEE) and applied the Enhanced Vegetation Index (EVI) and harmonic regression methods to identify phenological behaviors of land cover categories during the leaf-shedding phenology (LSP) and leaf-flushing phenology (LFS) seasons. We then generated 722 mean phenology EVI profiles for 12 major land cover categories and determined the threshold values for selected land cover categories in the mid-LSP season. The PBTC pixel-based classified map was validated using very high-resolution (VHR) imagery. We obtained a cumulative overall accuracy of more than 88% and a cumulative overall accuracy of the referenced forest cover of almost 85%. These high accuracy values suggest that the very first PBTC map can be useful for estimating the activity data, which are critically needed to assess land use changes and related carbon emissions under the Reducing Emissions from Deforestation and forest Degradation (REDD+) scheme. We found that GEE cloud-computing is an appropriate tool to use to access remote sensing big data at scale and at no cost.