Remote Sensing-based Mapping Research Articles

How to best map greenery from a human perspective? Comparing computational measurements with human perception

Urban greenery has been shown to impact the quality of life in our urbanizing societies. While greenery is traditionally mapped top-down, alternative computational approaches have emerged for mapping greenery from the street level to mimic human sight. Despite the variety of these novel mapping approaches, it has remained unclear how well they reflect human perception in reality. We compared a range of both novel and traditional mapping methods with the self-reported perception of urban greenery at randomly selected study sites across Helsinki, the capital of Finland. The mapping methods included both image segmentation and point cloud-based methods to capture human perspective as well as traditional approaches taking the top-down perspective, i.e., land cover and remote sensing-based mapping methods. The results suggest that all the methods tested are strongly associated with the human perception of greenery at the street-level. However, mapped greenery values were consistently lower than the perceived values. Our results support the use of semantic image segmentation methods over color segmentation methods for greenery extraction to be closer to human perception. Point cloud-based approaches and top-down methods can be used as alternatives to image segmentation in case data coverage for the latter is limited. The results highlight a further research need for a comprehensive evaluation on how human perspective should be mimicked in different temporal and spatial conditions.

A new attempt for modeling erosion risks using remote sensing-based mapping and the index of land susceptibility to wind erosion

Digital soil maps at proper scales can support sustainable land-use planning over large areas; however, applying this approach in erosion studies is still limited. Therefore, in this work, we adopted remote sensing-based mapping and fuzzy logic techniques to develop wind erosion risk maps with a spatial resolution of 30 m. These procedures were then conducted in a newly-reclaimed area in Matruh Governorate, the Egyptian western desert. Sixty surface soil samples (0–30 cm) were collected and analyzed for soil erodibility-related properties (sand, silt, clay, organic matter, and CaCO3). The relationships of these properties with the reflectance data of Landsat 8 multispectral bands were explored. Then, the spatial predictions were performed using the stepwise multiple linear regression models. The results showed that band reflectance in the shortwave infrared spectrum had higher correlations with soil properties than in the visible and near-infrared regions. The regression models achieved reliable precision and acceptable prediction abilities for all soil properties, except for the silt content. For validation datasets (30 % of the total data), the best-fitted models had coeffect of determination (R2) and residual prediction deviation of 0.61–0.92 and 1.42–2.67, respectively. The fuzzy memberships revealed various contributions of the five drivers (climate erosivity, soil erodibility, soil crust, vegetation, and terrain roughness) to potential soil loss. The success rate curves indicated the different performances of the applied fuzzy overlay operators (AND, OR, Sum, Product, and Gamma 0.9). The best-predictive model with an overall success rate of 85.7 % was obtained by overlaying the five fuzzified layers under the fuzzy algebraic Sum operator. This model delineated five hazard classes over the study area, including severe (79.5 %), high (12.6 %), moderate (5.7 %), slight (1.8 %), and very slight (0.4 %). The developed approach would improve the insight into large-scale monitoring of wind erosion status in desert ecosystems.

Emerging mercury and methylmercury contamination from new artisanal and small-scale gold mining along the Nile Valley, Egypt

The past decade witnessed the initiation and boom of the Artisanal and Small-scale Gold Mining (ASGM) activities in the hyper-arid southern Egypt. The ores are mined in the Eastern Desert and then transported to the densely populated farming communities in the Nile Valley, where the river provides the water resources needed for ore processing. In search for economic benefits, the poorly educated farmers with limited technical resources transformed their cultivated lands into ASGM operations, exposing themselves, their families, the residents, and the Nile ecosystems to several environmental and occupational health problems. Using integrated remote sensing, field, geochemical, and isotopic analyses, we report the first inventory of ASGM-related total mercury (THg) and methylmercury (MeHg) levels in tailings, amalgamation-tailing ponds, and surface and groundwater with emphasis on the Edfu city and its surroundings. The field and remote sensing-based mapping of ASGM activities reveals clustering around the Nile waterways and suggests interaction of Hg contamination sources with their surrounding receptors. Common ASGM practices include release of contaminated water from unlined amalgamation-tailing ponds into irrigation and drainage canals, and spreading of tailings over cultivated soils. In a short period (10 years), the released Hg contaminated multiple media, including the surface water, the shallow and deep aquifers, and possibly the soil, crops, and livestock. THg levels in amalgamation-tailing ponds (1200–8470 ng/L) are fourfold higher than US EPA and eightfold the WHO thresholds. The contaminated waters released from amalgamation-tailing ponds raised THg levels in surface water (irrigation canals: 50–100 ng/L; drainage canals: THg: > 200 ng/L) and groundwater (shallow and deep aquifers: 80–500 ng/L). Our findings highlight the need to extend the adopted approach to cover the entire length of the Nile River and its valley and the importance of conducting awareness campaigns to educate residents and health care providers about potential ASGM-related environmental and health hazards.Graphical

Very high-resolution remote sensing-based mapping of urban residential districts to help combat COVID-19

Urban residential districts (URDs) are a major element in the formation of cities that are essential for urban planning. Regarding the COVID-19 virus, which remains variable in aerosols for several hours, airborne transmission tends to occur in areas of poor ventilation and high occupant density. Thus, ventilation capacity is an important factor influencing airborne transmission in URDs, which should be evaluated as part of efforts to fight COVID-19 and guide healthy city planning and implementation. Here, we develop and test systematic methods to map URDs in a typical city in northern China and quantify their ventilation capacity using very high-resolution remote sensing images. Four fundamental spatial forms of URD are identified in the research area: the point-group form, parallel form, enclosed form, and hybrid form. Our analyses indicate that the integrated ventilation capacities for well-designed URDs are nearly twice those of poorly designed URDs. Large variations in ventilation capacity are also observed within URDs, with up to 13.42 times difference between the buildings. Therefore, very high-resolution remote sensing data are fundamental for extracting building height and generating precise spatial forms, which can improve the micro-scale URD ventilation planning for the prevention of COVID-19.

Remote sensing-based mapping of the Wadi Sa’al-Wadi Zaghara basement rocks, southern Sinai, Egypt

The Wadi Sa'al-Wadi Zaghara area, southern Sinai, Egypt is a key geologic region within the framework of the Arabian-Nubian Shield (ANS). It almost documents the whole tectono-thermal evolution history of the Pan-African orogeny; hence it records the latest stages of Rodinia breakup (Late Mesoproterozoic) till the penultimate regional deformation event (D3) that impacted the northern tip of the ANS in the Late Neoproterozoic, i.e., before the Najd fault system. Since the metamorphosed volcano-sedimentary rock units in both Wadi Sa’al and Wadi Zaghara exhibit considerable differences in their geological evolution, they were dealt with as two distinct metamorphic belts. They cover a wide spectrum of basement rock varieties, which developed over a long period of time, from Late Mesoproterozoic (Wadi Sa’al metamorphosed volcano-sedimentary rocks) till Late Neoproterozoic Ediacaran period (post-collision granites). The different rock units of the study area were discriminated based on various image processing techniques such as Decorrelation Stretch (DS) Band Ratio (BR) and Principal Component (PC) of Sentinel-2, Landsat-8 and ASTER datasets. Remote sensing data analyses with the aid of petrography, previously published data and field verification enabled us to produce a new enhanced geological map of the study area. Advanced Land Observing Satellite (ALOS) digital elevation and enhanced Sentinel-2, Sobel directional filter images were successfully used for automatic lineaments extraction in the study area.

In situ hyperspectral characteristics and the discriminative ability of remote sensing to coral species in the South China Sea

ABSTRACT Knowledge about the optical features of benthic objects is essential for quantifying spectral signatures, remote sensing-based mapping, and ecological monitoring in coral reefs. However, the spectral identification of benthic species and the accurate measurement of the in situ reflectance spectra of relevant research objects remain underexplored. An underwater radiation measuring system suitable for coral reef environments was specifically designed to obtain in situ reflectance spectra and match benthic photographs of various substrate targets. This instrument has the advantages of obtaining hyperspectral, dual-channel simultaneous measurements, and automatically adjusting the integration time according to the light intensity. Based on in situ hyperspectral datasets, the linear discriminant analysis (LDA) was used for exploring and discriminating spectral characteristics from three taxonomic ranks, which include typical substrates of six community groups, nine coral families, and six Acroporidae species. In situ full-resolution (1-nm) spectra provided the best discrimination ability with mean accuracies of 97.5%, 90.9%, and 91.6% for typical substrates, coral families, and coral species, respectively. The spectral abilities of remote sensors were assessed by applying the spectral response functions of three multispectral sensors (Landsat 8 OLI, Sentinel-2A, and World View-2) to the full-resolution spectra. Discrimination analyses of the simulated spectra demonstrated that the spectral separations of typical substrates might be apparent, with overall classification accuracies of 89.6%, 88.2%, and 90.4% for the Landsat 8 OLI, Sentinel-2A, and World View-2 sensors, respectively. The spectral separation for different corals, however, may not be effective when using multispectral sensors. The discrimination analyses of families and species produced overall classification accuracies of 67.1% and 69.6%, respectively, for the Landsat 8 OLI, 56.0% and 56.0% for the Sentinel-2A sensor, and 64.5% and 61.8% for the World View-2 sensor. In summary, this method has the potential for identifing substrate targets in communities and taxonomic coral groups by applying in situ hyperspectral datasets. Furthermore, multispectral satellite sensors are currently inadequate for spectrally separate corals, while spectral discrimination is possible and practical for different substrate targets with visual spectral differences.

ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE

Abstract. Remote sensing can make seagrass aboveground carbon stock (AGCseagrass) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGCseagrass in the field to train and assess its remote sensing-based mapping. The aim of this research is to (1) analyze the Percent Cover (PCv)-AGCseagrass relationship in seagrass at the species and community levels to estimate AGCseagrass from PCv and (2) perform AGCseagrass mapping at both levels using WorldView-2 image and assess the accuracy of the resulting map. This research was conducted in Karimunjawa and Kemujan Islands, Indonesia. Support Vector Machine (SVM) classification was used to map seagrass species composition, and stepwise regression was used to model AGCseagrass using deglint, water column corrected, and principle component bands. The results were a rapid AGCseagrass estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGCseagrass map had 58.79% accuracy (SEE = 5.41 g C m−2), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m−2) and EaThCr (70.02%, SEE = 4.32 g C m−2) but decreased for ThCr (55.08%, SEE = 2.55 g C m−2). The results indicate that WorldView-2 image reflectance can accurately map AGCseagrass in the study area in the range of 15–20 g C m−2 for Ea, 10–15 g C m−2 for EaThCr, and 4–8 g C m−2 for ThCr. Based on our model, the AGCseagrass in the study area was estimated at 13.39 t C.

Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVITian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R2 can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.

Predicting catchment-scale methane fluxes with multi-source remote sensing

ContextSpatial patterns of CH4 fluxes can be modeled with remotely sensed data representing land cover, soil moisture and topography. Spatially extensive CH4 flux measurements conducted with portable analyzers have not been previously upscaled with remote sensing.ObjectivesHow well can the CH4 fluxes be predicted with plot-based vegetation measures and remote sensing? How does the predictive skill of the model change when using different combinations of predictor variables?MethodsWe measured CH4 fluxes in 279 plots in a 12.4 km2 peatland-forest-mosaic landscape in Pallas area, northern Finland in July 2019. We compared 20 different CH4 flux maps produced with vegetation field data and remote sensing data including Sentinel-1, Sentinel-2 and digital terrain model (DTM).ResultsThe landscape acted as a net source of CH4 (253–502 µg m−2 h−1) and the proportion of source areas varied considerably between maps (12–50%). The amount of explained variance was high in CH4 regressions (59–76%, nRMSE 8–10%). Regressions including remote sensing predictors had better performance than regressions with plot-based vegetation predictors. The most important remote sensing predictors included VH-polarized Sentinel-1 features together with topographic wetness index and other DTM features. Spatial patterns were most accurately predicted when the landscape was divided into sinks and sources with remote sensing-based classifications, and the fluxes were modeled for sinks and sources separately.ConclusionsCH4 fluxes can be predicted accurately with multi-source remote sensing in northern boreal peatland landscapes. High spatial resolution remote sensing-based maps constrain uncertainties related to CH4 fluxes and their spatial patterns.

Comparing canopy density measurement from UAV and hemispherical photography: an evaluation for medium resolution of remote sensing-based mapping

UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.

Remote Sensing Applications in Mapping Salt Affected Soils

The aim of this review paper is to provide a comprehensive overview of remote sensing-based mapping of salt affected soils. By providing multispectral and multi-temporal low cost data at various resolutions, remote sensing plays an important role for identifying and mapping the distribution of salt affected soils. Different bands of the multispectral data and the indices and transforms derived from them have been found useful in delineating salt affected soils. The various approaches to map salt affected soils involving remote sensing data, from visual interpretation to supervised and unsupervised classifications have been discussed. Quantitative mapping of soil salinity with remote sensing and other environmental variables have also been discussed.

Remote sensing-based mapping for the new generation of Vegetation Map of China (1:500,000)

Remote sensing-based mapping for the new generation of Vegetation Map of China (1:500,000)

Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland: An Analysis of Worldwide Research

The total area of plastic-covered crops of 3019 million hectares has been increasing steadily around the world, particularly in the form of crops maintained under plastic-covered greenhouses to control their environmental conditions and their growth, thereby increasing production. This work analyzes the worldwide research dynamics on remote sensing-based mapping of agricultural greenhouses and plastic-mulched crops throughout the 21st century. In this way, a bibliometric analysis was carried out on a total of 107 publications based on the Scopus database. Different aspects of these publications were studied, such as type of publication, characteristics, categories and journal/conference name, countries, authors, and keywords. The results showed that “articles” were the type of document mostly found, while the number of published documents has exponentially increased over the last four years, growing from only one document published in 2001 to 22 in 2019. The main Scopus categories relating to the topic analyzed were Earth and Planetary Sciences (53%), Computer Science (30%), and Agricultural and Biological Sciences (28%). The most productive journal in this field was “Remote Sensing”, with 22 documents published, while China, Italy, Spain, USA, and Turkey were the five countries with the most publications. Among the main research institutions belonging to these five most productive countries, there were eight institutions from China, four from Italy, one from Spain, two from Turkey, and one from the USA. In conclusion, the evolution of the number of publications on Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland found throughout the period 2000–2019 allows us to classify the subject studied as an emerging research topic that is attracting an increasing level of interest worldwide, although its relative significance is still very limited within the remote sensing discipline. However, the growing demand for information on the arrangement and spatio-temporal dynamics of this increasingly important model of intensive agriculture is likely to drive this line of research in the coming years.

Developing a Remote Sensing-based Mapping Method for Swidden Land Use Detection:

Developing a Remote Sensing-based Mapping Method for Swidden Land Use Detection:

Remote Sensing-Based Mapping of Senescent Leaf C:N Ratio in the Sundarbans Reserved Forest Using Machine Learning Techniques

Carbon to nitrogen ratio (C:N) of senescent leaf is a crucial functional trait and indicator of litter quality that affects belowground carbon and nitrogen cycles, especially soil decomposition. Although mapping the C:N ratio of fresh mature canopies has been attempted, few studies have attempted to map the C:N ratio of senescent leaves, particularly in mangroves. In this study, four machine learning models (Stochastic Gradient Boosting, SGB; Random Forest, RF; Support Vector Machine, SVM; and Partial Least Square Regression, PLSR) were compared for testing the predictability of using the Landsat TM 5 (LTM5) and Landsat 8 to map spatial and temporal distribution of C:N ratio of senescent leaves in Sundarbans Reserved Forest (SRF), Bangladesh. Surface reflectance of bands, texture metrics of bands and vegetation indices of LTM5 and Landsat 8 yearly composite images were extracted using Google Earth Engine for 2009–2010 and 2019. We found SGB, RF and SVM were significant different from PLSR based on MAE, RMSE, and R2 (p < 0.05). Our results indicate that remote sensing data, such as Landsat TM data, can be used to map the C:N ratio of senescent leaves in mangroves with reasonable accuracy. We also found that the mangroves had a high spatial variation of C:N ratio and the C:N ratio map developed in the current study can be used for improving the biogeochemical and ecosystem models in the mangroves.

Effect of image radiometric correction levels of Landsat images to the land cover maps resulted from maximum likelihood classification

Radiometric correction of remote sensing images is required to improve the quality of image pixel values and provide a measurable physical unit of each pixel. Selection of the appropriate image radiometric and atmospheric correction level defines the success of any remote sensing-based mapping applications. This study aims to assess the effects of radiometric correction levels applied to Landsat 8 (Operational Land Imager, OLI) image acquired in 2018 to the results of the land cover classification using the Maximum Likelihood Classifier (MLC). The image was corrected into four levels of radiometric and atmospheric correction; no correction (digital number), at-sensor radiance, at-sensor reflectance (top of atmosphere, ToA), and at-surface reflectance (bottom of atmosphere, BoA). A set of classification training sample covering five land cover classes (mangroves, inland vegetation, exposed soil, built-up area, and water body) was selected from the image. To ensure fair class comparison, the number of training sample were set to be proportional to the area of targeted classes. The results of this study show that there is no difference in the classification results of each level of correction, both in the area and distribution of the classes. This finding indicates that MLC result is invariable of image correction level.

Mitigating the effects of omission errors on area and area change estimates

Information on Earth's land surface and change over time has never been easier to obtain, but making informed decisions to manage land well necessitates that this information is accurate and precise. In recent years, due largely to the inevitability of classification errors in remote sensing-based maps and the marked effects of these errors on subsequent area estimates, sample-based area estimates of land cover and land change have increased in importance and use. Area estimation of land cover and change by sampling is often made more efficient by a priori knowledge of the study area to be analyzed (e.g., stratification). Satellite data, obtained free of cost for virtually all of Earth's land surface, provide an excellent source for constructing landscape stratifications in the form of maps. Errors of omission, defined as sample units observed as land change but mapped as a stable class, may introduce considerable uncertainty in parameter estimates obtained from the sample data (e.g., area estimates of land change). The effects of omission errors are exacerbated in situations where the area of intact forest is large relative to the area of forest change, a common situation in countries that seek results-based payments for reductions in deforestation and associated carbon emissions. The presence of omission errors in such situations can preclude the acquisition of statistically valid evidence of a reduction in deforestation, and thus prevent payments. International donors and countries concerned with mitigating the effects of climate change are looking for guidance on how to reduce the effects of omission errors on area estimates of land change. This article presents the underlying reasons for the effects of omission errors on area estimates, case studies highlighting real-world examples of these effects, and proposes potential solutions. Practicable approaches to efficiently splitting large stable strata are presented that may reduce the effects of omission errors and immediately improve the quality of estimates. However, more research is needed before further recommendations can be provided on how to contain, mitigate and potentially eliminate the effects of omissions errors.

An Effective Low-Cost Remote Sensing Approach to Reconstruct the Long-Term and Dense Time Series of Area and Storage Variations for Large Lakes.

Inland lakes are essential components of hydrological and biogeochemical water cycles, as well as indispensable water resources for human beings. To derive the long-term and continuous trajectory of lake inundation area changes is increasingly significant. Since it helps to understand how they function in the global water cycle and how they are impacted by climate change and human activities. Employing optical satellite images, as an important means of lake mapping, has been widely used in the monitoring of lakes. It is well known that one of the obvious difficulties of traditional remote sensing-based mapping methods lies in the tremendous labor and computing costs for delineating the large lakes (e.g., Caspian Sea). In this study, a novel approach of reconstructing long-term and high-frequency time series of inundation areas of large lakes is proposed. The general idea of this method is to obtain the lake inundation area at any specific observation date by referring to the mapping relationship of the water occurrence frequency (WOF) of the selected shoreline segment at relatively slight terrains and lake areas based on the pre-established lookup table. The lookup table to map the links of the WOF and lake areas is derived from the Joint Research Centre (JRC)Global Surface Water (GSW) dataset accessed in Google Earth Engine (GEE). We select five large lakes worldwide to reconstruct their long time series (1984–2018) of inundation areas using this method. The time series of lake volume variation are analyzed, and the qualitative investigations of these lake changes are eventually discussed by referring to previous studies. The results based on the case of North Aral Sea show that the mean relative error between estimated area and actually mapped value is about 0.85%. The mean R2 of all the five lakes is 0.746, which indicates that the proposed method can produce the robust estimates of area time series for these large lakes. This research sheds new light on mapping large lakes at considerably deducted time and labor costs, and be effectively applicable in other large lakes in regional and global scales.

Farms or Forests? Understanding and Mapping Shifting Cultivation Using the Case Study of West Garo Hills, India

Attempts to study shifting cultivation landscapes are fundamentally impeded by the difficulty in mapping and distinguishing shifting cultivation, settled farms and forests. There are foundational challenges in defining shifting cultivation and its constituent land-covers and land-uses, conceptualizing a suitable mapping framework, and identifying consequent methodological specifications. Our objective is to present a rigorous methodological framework and mapping protocol, couple it with extensive fieldwork and use them to undertake a two-season Landsat image analysis to map the forest-agriculture frontier of West Garo Hills district, Meghalaya, in Northeast India. We achieve an overall accuracy of ~80% and find that shifting cultivation is the most extensive land-use, followed by tree plantations and old-growth forest confined to only a few locations. We have also found that commercial plantation extent is positively correlated with shortened fallow periods and high land-use intensities. Our findings are in sharp contrast to various official reports and studies, including from the Forest Survey of India, the Wastelands Atlas of India and state government statistics that show the landscape as primarily forested with only small fractions under shifting cultivation, a consequence of the lack of clear definitions and poor understanding of what constitutes shifting cultivation and forest. Our results call for an attentive revision of India’s official land-use mapping protocols, and have wider significance for remote sensing-based mapping in other shifting cultivation landscapes.

Detection of Seasonal Inundations by Satellite Data at Shkoder Urban Area, North Albania for Sustainable Management

The European Space Agency satellites Sentinel-1 radar and Sentinel-2 optical data are widely used in water surface mapping and management. In this work, we exploit the potentials of both radar and optical images for satellite-based quick detection and extent mapping of inundations/water raising events over Shkodër area, which occurred in the two last years (2017–2018). For instance, in March 2018 the Shkodër district (North Albania) was affected twice by the overflow of the Drin and Buna (Bojana) Rivers and by the Shkodër lake plain inundation. Sentinel-1 radar data allowed a rapid mapping of seasonal fluctuations and provided flood extent maps by discriminating water surfaces (permanent water and flood areas) from land/non-flood areas over all the informal zones of Shkodër city. By means of Sentinel-2 data, two color composites maps were produced and the Normalized Difference Water Index was estimated, in order to further distinguish water/moisturized soil surfaces from built-up and vegetated areas. The obtained remote sensing-based maps were combined and discussed with the urban planning framework in order to support a sustainable urban and environmental management. The provided multi-temporal analysis could be easily exploited by the local authorities for flood prevention and management purposes in the inherited territorial context. The proposed approach outputs were validated by comparing them with official Copernicus EMS (Emergency Management Service) maps available for one of the chosen events. The comparison shows good accordance results. As for a further enhancement in the future perspective, it is worth to highlight that a more accurate result could be obtained by performing a post-processing edit to further refine the flooded areas, such as water mask application and supervised classification to filter out isolated flood elements, to remove possible water-lookalikes and weed out false positives.