Moderate Resolution Satellite Data Research Articles

Human footprint and rainfall shape Masai giraffe's habitat suitability and connectivity in a multiple‐use landscape

AbstractGiraffe populations have declined by around 40% in the last three decades. Climate change, poaching, habitat loss, and increasing human pressures are confining giraffes to smaller and more isolated patches of habitats. Masai giraffes (Giraffa tippelskirchi) have been subjected to habitat loss and fragmentation, diseases, poaching, and unpredictable calamities such as wildfires and climate change. In this study, we aimed to identify (1) suitable Masai giraffe habitats within the transboundary landscape of Tsavo‐Mkomazi in Southern Kenya and Northern Tanzania; and (2) key connecting corridors in a multiple‐use landscape for conservation prioritization. We combined Masai giraffe presence data collected through a total aerial survey with moderate resolution satellite data to model habitat suitability at 250 m resolution using species distribution models (SDMs) implemented in Google Earth Engine (GEE). Model accuracy was assessed using area under precision recall curve (AUC‐PR). We then used the habitat suitability index as a resistance surface to model functional connectivity using Circuitscape theory and cost‐weighted distance pairwise methods. Human habitat modification, rainfall, and elevation were the main model predictors of Masai giraffe habitat and corridors. On average, our 10‐fold model fitting attained a good predictive performance with an average AUC‐PR = 0.80 (SD = 0.01, range = 0.79–0.83). The model predicted an area of 15,002 km2 as potential suitable Masai giraffe habitat with over 17% outside protected areas within the landscape. Although Tsavo West National Park formed a key habitat and a key connecting corridor, nonprotected community ranches connecting Tsavo West and Tsavo East National Parks are equally important in maintaining landscape connectivity joining more than two Masai giraffe core areas with low resistance and high permeability. To maintain critical Masai giraffe's habitats and landscape functional connectivity, especially in multiple‐use landscapes, conservation‐compatible land use practices, capacity building, and land use planning should be considered at the outset of any new infrastructure development and land use changes. This modeling shows the potential of utilizing remotely sensed information and ground surveys to guide the management of habitats and their connecting corridors across important African landscapes, complementing existing efforts to identify, conserve, and protect wildlife habitats and their linkage zones.

A comparison of moderate and high spatial resolution satellite data for modeling gross primary production and transpiration of native prairie, alfalfa, and winter wheat

Although agroecosystems have a significant potential to offset carbon dioxide (CO2), the amount of CO2 captured can vary significantly depending on management practices. Accurate estimation of gross primary production (GPP) and transpiration (T) of agroecosystems at the field scale are essential for the study of food security and water resource management. To date, the carbon and water fluxes data products for commercial agroecosystems are limited, mostly at the moderate spatial resolution (MSR, hundreds of meters), which cannot be used to assess the temporal dynamics of GPP and T at the field scale. This study used the vegetation photosynthesis model (VPM) and vegetation transpiration model (VTM) to estimate field-level daily GPP (GPPVPM) and T (TVTM), respectively, in native prairie, alfalfa (Medicago sativa L.), and winter wheat (Triticum aestivum L.) in central Oklahoma, USA. We evaluated the reliability and advantages of vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI) in monitoring the land surface phenology using moderate spatial resolution data from Moderate Resolution Imaging Spectroradiometer (MODIS) and high spatial resolution (HSR, tens of meters) data from Landsat and Sentinel-2. The accuracy of GPPVPM and TVTM estimates at different spatial scales was evaluated using GPP (GPPEC) and evapotranspiration (ETEC) from the eddy flux tower sites, respectively. Results demonstrate the capacity of VPM and VTM to estimate the field-level carbon and water flux dynamics and their responses to weather conditions. The use of HSR vegetation indices helped to address certain challenges faced by MSR indices, especially in capturing the crop phenology in smaller areas with conservation measures or disturbances. The findings highlight the importance of using HSR GPP estimates to reduce uncertainty in quantifying CO2 fluxes for croplands and grasslands. The findings also demonstrate the ability of the models to track field-level vegetation phenology, carbon uptake, and water use in agroecosystems under different management practices.

Investigating the effectiveness of Landsat-8 OLI and Sentinel-2 MSI satellite data in monitoring the effects of drought on surface water resources in the Western Cape Province, South Africa

Drought frequency and magnitude are increasing, posing significant risks to surface water bodies, particularly in arid and semi-arid regions. Leveraging on moderate resolution satellite data, such as Landsat-8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI), offers unique opportunities for monitoring surface water availability and drought impacts. In this study, we assessed the extent to which Landsat-8 OLI and Sentinel-2 MSI satellite data can be used to characterise and monitor the impacts of drought on water resources in the Western Cape, South Africa. Multispectral indices including Normalised Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), and the Land Surface Water Index (LSWI+5) were computed to determine the most suitable method for surface water detection and drought monitoring. Sentinel-2-derived NDVI emerged as the most suitable index for mapping surface waterbodies, demonstrating an overall accuracy of 77.27%. However, LSWI+5 exhibited limitations, misclassifying built-up and mountainous areas as surface waterbodies. SPI, VCI, and WRSI effectively identified drought periods and correlated well with climate data, revealing low rainfall and high evapotranspiration rates during the 2016–2018 drought period. These findings provide valuable insights into surface water variability and the impacts of drought on water resources. Overall, our study underscores the feasibility of utilizing moderate resolution datasets to assess drought impacts on surface water resources, enabling improved water resource management, drought detection, and preparedness efforts in resource limited environments.

Forest status assessment in China with SDG indicators based on high-resolution satellite data

ABSTRACT To assess the status and change trend of forest in China, an indicator framework was developed using SDG sub-indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDG indicators. The main modification include the use of moderate and high spatial resolution satellite data, as well as state-of-the-art machine learning techniques for forest cover classification and estimation of forest above-ground biomass (AGB). This research employs GF-1 and GF-2 data with enhanced texture information to map forest cover, while time series Landsat data is used to estimate forest AGB across the whole territory of China. The study calculate two SDG sub-indicators: SDG15.1.1 for forest area and SDG15.2.1 for sustainable forest management. The evaluation results showed that the total forest area in China was approximately 219 million hectares at the end of 2021, accounting for about 23.51% of the land area. The average annual forest AGB from 2015 to 2021 was estimated to be 105.01Mg/ha, and the overall trend of forest AGB change in China was positive, albeit with some spatial differences.

Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites

Despite a rapid development of Nature-Based Solutions (NBS) for carbon removal in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatiotemporal variation of net ecosystem exchange from eddy-covariance stations. A Lagrangian particle dispersion model was used for upscaling satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land-cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution thanks to the use of Landsat imagery; and (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Stratification that uses separate Fluxnet stations for each fold of validation ensures that the models are accurate across the area covered by the Fluxnet sites. Overall, we present here a globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

Mapping corn and soybean phenometrics at field scales over the United States Corn Belt by fusing time series of Landsat 8 and Sentinel-2 data with VIIRS data

Accurate and timely crop phenology is essential for efficient crop management and precise modelling of crop yield and productivity. It has been commonly mapped from moderate or coarse resolution (≥500 m) satellite data at a national or global scale, which generally represents the mixture of multiple crops or between crops and natural vegetation. Crop phenological progress at field scales (≤30 m) is critical for monitoring the growth of specie-specific crop types, but it is very challenging at large scales because of limited cloud-free observations in the satellite time series. To map corn and soybean phenometrics at a field scale (30 m) over entire Corn Belt in the United States, this study for the first time develops a new approach that fuses high temporal resolution Visible Infrared Imaging Radiometer Suite (VIIRS) observations (500 m) and high spatial resolution Harmonized Landsat and Sentinel-2 (HLS) data (30 m). The fused HLS-VIIRS time series (daily 30 m) is applied to map crop phenometrics of greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. These six phenometrics are correlated to crop progress (CP) of seven corn and six soybean growth stages from planting to harvest that are observed by the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) and are further validated using PhenoCam near-surface time series imagery. The result indicates that the HLS-VIIRS crop phenometrics are capable of tracking the CP very well. Although their biophysical definitions of CP stages and sample coverage were not the same in our comparisons, the HLS-VIIRS crop phenometrics and NASS CP at a state level are closely comparable with a difference<4 days for corn and soybean emergence, 1 day for soybean dropping leaves, and 6–10 days for other corn and soybean growth stages. Their significant correlations (R2 ranging from 0.81 to 0.99, p < 0.01) suggest that the HLS-VIIRS crop phenometrics are robust proxies for predicting NASS CP with a high accuracy. Moreover, the HLS-VIIRS phenometrics aligns closely with PhenoCam phenological timings at various crop growth stages with an absolute average difference from 3 days to 5.3 days and bias from 1.5 to 3.5 days. The result demonstrates that the proposed approach could be implemented for monitoring field scale crop progress at a national or global scale.

Long-term Landsat monitoring of mining subsidence based on spatiotemporal variations in soil moisture: A case study of Shanxi Province, China

Ground subsidence caused by mining activities has extensively and chronically affected people living near mining areas. Remote sensing images provide temporally and spatially continuous observations of surface natural resources. In order to accurately understand and continuously observe subsidence areas, this study investigated the spatiotemporal dynamics of mining subsidence using long-term Landsat time-series. The study area was a typical mining region in Shanxi Province, China, with a long history of mining activity. Landsat images from 1986 to 2019, as well as reference data, were used to construct inter-annual time-series trajectories of soil moisture, which were derived from spatial normalizations of the soil moisture monitoring index(SSMMI). The LandTrendr algorithm on the Google Earth Engine platform was then used to extract the subsidence areas and their spatiotemporal dynamics. The results indicate that (i)the map of the spatial distributions of mining subsidence had an overall accuracy of 83.8% and indicate that areas that underwent subsidence were mainly concentrated in mining cites(including Taiyuan, Lvliang, and Linfen); (ii)subsidence before 1990 accounted for 39.6% of the total occurrences and a large proportion(45.18%) of the subsidence was short-term; (iii)subsidence in mountain areas that have long histories of mining and long subsidence durations require further attention by researchers. This study allows us to advance the long-term monitoring of mining subsidence times using variations in soil moisture, combined with moderate resolution satellite data such as Landsat and time-series analysis algorithms.

A Robust Atmospheric Correction Procedure for Determination of Spectral Reflectance of Terrestrial Surfaces from Satellite Spectral Measurements

In this work, we propose simple and robust technique for the retrieval of underlying surface spectral reflectance using spaceborne observations. It can be used to process both multispectral moderate resolution satellite data and also multi-zone high spatial resolution data. The technique can work automatically for different types of land surfaces without using huge databases accumulated in advance. The new cloud discrimination and retrieval of the water vapor content in atmosphere procedures are presented. The key point of the proposed atmospheric correction technique is the suggested single-wavelength method for determining the atmospheric aerosol optical thickness without reference to a specific type of underlying surface spectrum. The retrievals of spectral reflectance for various land surfaces with developed technique, performed using computer simulation and experimental data, have demonstrated a high retrieval accuracy.

Optimal Spatiotemporal Scales to Aggregate Satellite Ocean Color Data for Nearshore Reefs and Tropical Coastal Waters: Two Case Studies

Remotely sensed ocean color data are useful for monitoring water quality in coastal environments. However, moderate resolution (hundreds of meters to a few kilometers) satellite data are underutilized in these environments because of frequent data gaps from cloud cover and algorithm complexities in shallow waters. Aggregating satellite data over larger space and time scales is a common method to reduce data gaps and generate a more complete time series, but potentially smooths out the small-scale, episodic changes in water quality that can have ecological influences. By comparing aggregated satellite estimates of Kd(490) with related in-water measurements, we can understand the extent to which aggregation methods are viable for filling gaps while being able to characterize ecologically relevant water quality conditions. In this study, we tested a combination of six spatial and seven temporal scales for aggregating data from the VIIRS instrument at several coral reef locations in Maui, Hawai‘i and Puerto Rico and compared these with in situ measurements of Kd(490) and turbidity. In Maui, we found that the median value of a 5-pixels, 7-days spatiotemporal cube of satellite data yielded a robust result capable of differentiating observations across small space and time domains and had the best correlation among spatiotemporal cubes when compared with in situ Kd(490) across 11 nearshore sites (R2 = 0.84). We also found long-term averages (i.e., chronic condition) of VIIRS data using this aggregation method follow a similar spatial pattern to onshore turbidity measurements along the Maui coast over a three-year period. In Puerto Rico, we found that the median of a 13-pixels, 13-days spatiotemporal cube of satellite data yielded the best overall result with an R2 = 0.54 when compared with in situ Kd(490) measurements for one nearshore site with measurement dates spanning 2016–2019. As spatiotemporal cubes of different dimensions yielded optimum results in the two locations, we recommend local analysis of spatial and temporal optima when applying this technique elsewhere. The use of satellite data and in situ water quality measurements provide complementary information, each enhancing understanding of the issues affecting coastal ecosystems, including coral reefs, and the success of management efforts.

An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping

Generating crop type maps using satellite remote sensing requires robust data acquisition at both high spatial and temporal resolutions to resolve rapid phenological transition at the field scale. The increasing availability of freely-available, moderate-resolution satellite data such as the Landsat and Sentinel series of satellites offers an unprecedent opportunity for large-area crop type mapping. In this study, we evaluated the utility of Landsat (7&8), Sentinel-2 (A&B), Sentinel-1 (A&B) and the Moderate Resolution Imaging Spectroradiometer (MODIS) for mapping corn and soybean in the United States. We designed a series of classification experiments using these satellite data over a nationally distributed sample as input and the United States Department of Agriculture (USDA) Cropland Data Layer (CDL) as reference for training and accuracy assessment. A set of tests were performed with data from each satellite senor as input to derive the potential accuracy achievable by the satellite/sensor. In comparison, another set of tests were conducted with all data from all sensors as input to derive the combined accuracy as well as to evaluate the utility of each sensor, spectral band and acquisition date. Results showed that data from one satellite/sensor, either Landsat, Sentinel-2 or Sentinel-1, could achieve 94.8–96.8% accuracy, whereas the coarse-resolution MODIS produced about 92% accuracy, for both corn and soybean. Combing data from all sensors marginally improved the accuracy to 97.0% for both crops. Based on the criterion of deviation reduction in decision tree models, Landsat was identified as the most useful satellite/sensor for soybean classification, especially the two short-wave infrared bands, whereas Sentinel-2 was recognized as the most valuable satellite/sensor for corn classification, especially the red edge, near infrared and short-wave infrared bands. Optical data were always chosen over Synthetic Aperture Radar (SAR) data by the pixel-based supervised classification algorithm except in some persistently cloudy regions, although using SAR data alone can also achieve very high accuracy. The virtual constellation of Landsat and Sentinel-2 increased data revisit frequency to 4–7 days in the U.S. during June to September 2017. However, cloud and shadow reduced clear-view observations by half. Satellite data acquisitions in July were most critical for mapping corn whereas data in July and August were most important for mapping soybean. Our analysis suggested that, without the practical limitation of training data, current freely-available, moderate-resolution satellite data including Landsat, Sentinel-2, Sentinel-1 and MODIS, can achieve a potential accuracy of over 95% for national-scale crop type mapping over large industrial agricultural regions such as the United States. Expanding the spatial coverage and maintaining consistent acquisitions of Sentinel-1 data is a high priority to enable operational crop mapping and monitoring over large areas.

Prediction of aboveground biomass and dry‐matter content in Brachiaria pastures by combining meteorological data and satellite imagery

AbstractAboveground biomass (AGB) data are important for profitable and sustainable pasture management. In this study, we hypothesized that vegetation indexes (VIs) obtained through analysis of moderate spatial resolution satellite data (Landsat‐8 and Sentinel‐2) and meteorological data can accurately predict the AGB of Brachiaria (syn. Urochloa) pastures in Brazil. We used AGB field data obtained from pastures between 2015 and 2019 in four distinct regions of Brazil to evaluate (i) the relationship between three different VIs—normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2) and optimized soil adjusted vegetation index (OSAVI)—and meteorological data with pasture aboveground fresh biomass (AFB), aboveground dry biomass (ADB) and dry‐matter content (DMC); and (ii) the performance of simple linear regression (SLR), multiple linear regression (MLR) and random forest (RF) algorithms for the prediction of pasture AGB based on VIs obtained through satellite imagery combined with meteorological data. The results highlight a strong correlation (r) between VIs and AGB, particularly NDVI (r = 0.52 to 0.84). The MLR and RF algorithms demonstrated high potential to predict AFB (R2 = 0.76 to 0.85) and DMC (R2 = 0.78 to 0.85). We conclude that both MLR and RF algorithms improved the biomass prediction accuracy using satellite imagery combined with meteorological data to determine AFB and DMC, and can be used for Brachiaria (syn. Urochloa) AGB prediction. Additional research on tropical grasses is needed to evaluate different VIs to improve the accuracy of ADB prediction, thereby supporting pasture management in Brazil.

An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data

Accurate spatial information about irrigation is crucial to a variety of applications, such as water resources management, water exchange between the land surface and atmosphere, climate change, hydrological cycle, food security, and agricultural planning. Our study proposes a new method for extracting cropland irrigation information using statistical data, mean annual precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data and surface reflectance data. The approach is based on comparing the land surface water index (LSWI) of cropland pixels to that of adjacent forest pixels with similar normalized difference vegetation index (NDVI). In our study, we validated the approach over mainland China with 612 reference samples (231 irrigated and 381 non-irrigated) and found the accuracy of 62.09%. Validation with statistical data also showed that our method explained 86.67 and 58.87% of the spatial variation in irrigated area at the provincial and prefecture levels, respectively. We further compared our new map to existing datasets of FAO/UF, IWMI, Zhu and statistical data, and found a good agreement with the irrigated area distribution from Zhu’s dataset. Results show that our method is an effective method apply to mapping irrigated regions and monitoring their yearly changes. Because the method does not depend on training samples, it can be easily repeated to other regions.

Arctic landscape transitions: ice cap and terrestrial margins across Hofsjökull, Iceland

ABSTRACT Dramatic shifts in sea and terrestrial ice coverage across the Arctic has prompted urgent calls for systematic glacier and ice cap monitoring efforts. We use satellite-derived data from NASA’s Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) sensor to focus on landscape-level changes to Iceland’s dome-shaped Hofsjökull ice cap and its surrounding over a recent twenty-five year period, from 1992 to 2017. We offer a practicable technique using available moderate resolution satellite data with a relatively long historical record to monitor changes to Hofsjökull and its surrounding. Our analysis reveals substantial shifts in snow, ice cover, and vegetation over the study period. Observed is a twenty-three percent decrease in the spatial extent of snow and ice between the two years. The ice front margin has receded along the entire periphery of the ice cap, with an average retreat of 743 m across a sample of transects. In a few cases, the retreat is more than one kilometer from the 1992 margin. Employing an automated machine learning technique to analyze satellite data can reduce errors, allow for systematic frequent assessments and provide urgently needed finer-scale spatio-temporal records across the rapidly changing Arctic.

Use of multi-temporal satellite data for monitoring pool surface areas occurring in non-perennial rivers in semi-arid environments of the Western Cape, South Africa

Hydrological monitoring networks do not adequately cover non-perennial rivers in most parts of Africa partly because river flows do not frequently occur on these rivers. However, in the arid and semi-arid regions flows occurring on these rivers are an important resource that have to be managed judiciously. The lack of data on magnitudes, timing and frequency of non-perennial river flows constrain sustainable management of their water resources. The availability of moderate resolution satellite data (e.g. Sentinel-2 Multispectral Instrument and Landsat 8 Operational Land Imager) offers an opportunity to monitor the surface water availability specifically changes in surface areas covered with water in pools along these rivers. In this study, we investigated the use of remote sensing data to detect and monitor changes of water surface areas of pools along three non-perennial rivers and one perennial river in the Western Cape, South Africa, from 2016 to 2017, using Landsat 8 and Sentinel-2 datasets. We assessed a range of image pre-processing and image classification techniques to determine the most suitable method for surface water identification and pool area estimation along the selected rivers. The following indices were used; Normalised Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalised Difference Vegetation Index (NDVI), Automated Water Extraction Index for shadowed (AWEIsh), non-shadowed regions (AWEInsh), and the Multi-Band Water Index (MBWI). The NDWI computed from Sentinel-2 TOA reflectance datasets was found to be the most suitable index for mapping pools, with an overall accuracy ranging between 60 and 86%. This index was used successfully to establish the variations of surface areas of pools covered with water along the Breede, Nuwejaars, Tankwa and Touws Rivers during the 2016–2017 period. The temporal variations of pool areas could be explained as expected by changes of rainfall and potential evapotranspiration. Overall, the study demonstrated the feasibility of using moderate resolution datasets to monitor pool changes of areas covered with water in non-perennial rivers, which contributes to improved management of water resources particularly in arid and semi-arid environments.

A phenology-based method for identifying the planting fraction of winter wheat using moderate-resolution satellite data

ABSTRACT Winter wheat is a staple food crop for most of the world’s population, and the area and spatial distribution of winter wheat are key elements in estimating crop production and ensuring food security. However, winter wheat planting areas contain substantial spatial heterogeneity with mixed pixels for coarse- and moderate-resolution satellite data, leading to large errors in crop acreage estimation. This study has developed a phenology-based approach using moderate-resolution (1 km per pixel) satellite data to estimate sub-pixel planting fractions of winter wheat. Based on unmanned aerial vehicle (UAV) observations, the unique characteristics of winter wheat with high vegetation index values at the heading stage (May) and low values at the ripening stage (June) were investigated. The differences in vegetation index between heading and ripening stages increased with the planting fraction of winter wheat, and therefore the planting fractions were estimated by comparing the NDVI differences of a given pixel with those of predetermined pure winter wheat and non-winter wheat pixels. This approach was evaluated using aerial images and agricultural statistical data in an intensive agricultural region, Shandong Province in North China. The method explained 85% and 60% of the spatial variation in municipal- and county-level statistical data, respectively. More importantly, the predetermined pure winter wheat and non-winter wheat pixels can be automatically identified using MODIS data according to their NDVI differences, which strengthens the potential to use this method at regional and global scales without any field observations as references.

Spatiotemporal Analysis of Waterbodies in Ranchi District, Jharkhand using Satellite Data: An Input for Jal Shakti Abhiyan

Keeping in the view of water conservation the Government of India has initiated the Jal Shakti Abhiyan, which needs the inventory of waterbodies for further planning and monitoring. Lakes and Ponds in year 2005 and year 2018 were mapped in Ranchi district at 1:5000 scale using high resolution satellite data and reservoirs at 1:50,000 scale using moderate resolution satellite data. Seasonal fluctuation in water extent of reservoirs was estimated using Pre- monsoon and post-monsoon satellite data. Total number of lakes and ponds mapped in Ranchi district in year 2005 and year 2018 were 2170 and 2556, respectively. Total area under lakes and ponds was 1425 ha and 1618 ha, respectively in year 2005 and year 2018, which was 0.29% and 0.32% of total geographical area of Ranchi district. A total of 19 and 20 reservoirs mapped in Ranchi district in year 2006-07 and year 2018-19, respectively. Area recorded under reservoir category in Ranchi district was 2854 ha and 2829 ha in pre-monsoon season in the year 2007 and 2019, respectively. In Post-monsoon season the area occupied by reservoirs was 3428 ha and 3352 ha in Year 2007 and 2018, respectively. It has been suggested to undertake large scale mapping of lakes and ponds at 1:500/1000 scale using seasonal satellite data for preparation of inventory and estimation of pre and post-monsoon variation in number and area of lakes and ponds. Maps and statistics generated in present study are useful for planning, monitoring and management of waterbodies in present scenario of urbanization, environmental degradation and climate change. Keywords: High resolution data, urbanization, pre-monsoon, post-monsoon, lakes and ponds

Use of multispectral satellite datasets to improve ecological understanding of the distribution of Invasive Alien Plants in a water‐limited catchment, South Africa

AbstractInvasive Alien Plants (IAPs) pose major threats to biodiversity, ecosystem functioning and services. The availability of moderate resolution satellite data (e.g. Sentinel‐2 Multispectral Instrument and Landsat‐8 Operational Land Imager) offers an opportunity to map and monitor the occurrence and spatial distribution of IAPs. The use of two multispectral remote sensing data sets to map and monitor IAPs in the Heuningnes Catchment, South Africa, was therefore investigated using the maximum likelihood classification algorithm. It was possible to identify areas infested with IAPs using remote sensing data. Specifically, IAPs were mapped with a higher overall accuracy of 71%, using Sentinel‐2 MSI as compared to using Landsat 8 OLI, which produced 63% accuracy. However, both sensors showed similar patterns in the spatial distribution of IAPs within the hillslopes and riparian zones of the catchment. This work demonstrates the utility of the two multispectral data sets in mapping and monitoring the occurrence and distribution of IAPs, which contributes to improved ecological modelling and thus to improved management of invasions and biodiversity in the catchment.

Challenges in Estimating Tropical Forest Canopy Height from Planet Dove Imagery

Monitoring tropical forests using spaceborne and airborne remote sensing capabilities is important for informing environmental policies and conservation actions. Developing large-scale machine learning estimation models of forest structure is instrumental in bridging the gap between retrospective analysis and near-real-time monitoring. However, most approaches use moderate spatial resolution satellite data with limited capabilities of frequent updating. Here, we take advantage of the high spatial and temporal resolutions of Planet Dove images and aim to automatically estimate top-of-canopy height (TCH) for the biologically diverse country of Peru from satellite imagery at 1 ha spatial resolution by building a model that associates Planet Dove textural features with airborne light detection and ranging (LiDAR) measurements of TCH. We use and modify features derived from Fourier textural ordination (FOTO) of Planet Dove images using spectral projection and train a gradient boosted regression for TCH estimation. We discuss the technical and scientific challenges involved in the generation of reliable mechanisms for estimating TCH from Planet Dove satellite image spectral and textural features. Our developed software toolchain is a robust and generalizable regression model that provides a root mean square error (RMSE) of 4.36 m for Peru. This represents a helpful advancement towards better monitoring of tropical forests and improves efforts in reducing emissions from deforestation and forest degradation (REDD+), an important climate change mitigation approach.

A patch-based algorithm for global and daily burned area mapping

Increasing availability of dense time series of moderate spatial resolution satellite data for mapping global burned areas calls for mapping algorithms designed to easily integrate data at different spatial and temporal resolutions, irrespective of particular grid constraints. In this paper, we describe a novel hybrid approach for global burned area mapping that combines active fire data and time series of surface reflectance using graphs, which provide a flexible and efficient way of extracting spatiotemporal consistent patches.Our approach has three main steps. Firstly, we analyze burn-sensitive vegetation index time series to determine for each location a set of events, which are the dates for which the spectral-temporal signal indicates the possibility of a burn. Secondly, we explore the spatiotemporal distribution of all events and active fires to determine a subset of events with strong evidence of corresponding to burned areas. Those events are used as positive occurrences for training a one-class maximum entropy classifier and obtain, for each candidate event, a likelihood of it actually corresponding to a burn. Finally, we build a graph that combines all previous information, from which we extract spatiotemporal patches of densely connected events. Patches with strong evidence of burning determine the burned area map at any given time period.This research is part of the European Space Agency's Climate Change Initiative (ESA-CCI) and aims ultimately at generating Sentinel-3 daily global burned area products at 500 m spatial resolution. Towards that end, we test our approach with spatially and spectrally similar MODIS gridded surface reflectance (MOD/MYD09GA), as well as non-gridded active fire (MCD14ML) 2008 data and CCI global land cover maps. Using 105 independent Landsat fire reference perimeters to validate global results, we show that our algorithm applied to MOD/MYD09GA data (PT-M09) has very similar accuracy (44%) measured by the Dice coefficient compared with MCD64A1 v006 (45%). Moreover, PT-M09 exhibits a higher commission error but a lower omission error than MCD64A1. Due to their coarse resolution, this kind of product cannot capture very small burn areas. The bias relative to the reference burned area indicates that the algorithm presented in the current study underestimates burned area by 14% of the area actually burned according to reference data, which is lower than the underestimation in MCD64A1 (28%).We also analyzed the temporal accuracy of the patch based algorithm and concluded that the average time difference to active fires detection is 3.43 days, which is similar to MCD64A1 (3.75 days). Finally, we performed a sensitivity analysis which shows that total mapped burned areas varies only 3% when the main algorithm threshold that separates “burned” and “unburned” patches varies from quantile 45% to 55%.

A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

ABSTRACTFor evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1 – ‘Forest area as proportion of total land area’; 15.3.1 – ‘Proportion of land that is degraded over total land area’; and 2.4.1 – ‘Proportion of agricultural area under productive and sustainable agriculture’. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows.