scholarly journals Monitoring Mega-Crown Leaf Turnover from Space

2020 ◽  
Vol 12 (3) ◽  
pp. 429
Author(s):  
Emma R. Bush ◽  
Edward T. A. Mitchard ◽  
Thiago S. F. Silva ◽  
Edmond Dimoto ◽  
Pacôme Dimbonda ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Leaf Phenology ◽  
Optical Data ◽  
Spectral Signature ◽  
Earth System ◽  
Leaf Turnover ◽  
The Tropics

Spatial and temporal patterns of tropical leaf renewal are poorly understood and poorly parameterized in modern Earth System Models due to lack of data. Remote sensing has great potential for sampling leaf phenology across tropical landscapes but until now has been impeded by lack of ground-truthing, cloudiness, poor spatial resolution, and the cryptic nature of incremental leaf turnover in many tropical plants. To our knowledge, satellite data have never been used to monitor individual crown leaf phenology in the tropics, an innovation that would be a major breakthrough for individual and species-level ecology and improve climate change predictions for the tropics. In this paper, we assessed whether satellite data can detect leaf turnover for individual trees using ground observations of a candidate tropical tree species, Moabi (Baillonella toxisperma), which has a mega-crown visible from space. We identified and delineated Moabi crowns at Lopé NP, Gabon from satellite imagery using ground coordinates and extracted high spatial and temporal resolution, optical, and synthetic-aperture radar (SAR) timeseries data for each tree. We normalized these data relative to the surrounding forest canopy and combined them with concurrent monthly crown observations of new, mature, and senescent leaves recorded from the ground. We analyzed the relationship between satellite and ground observations using generalized linear mixed models (GLMMs). Ground observations of leaf turnover were significantly correlated with optical indices derived from Sentinel-2 optical data (the normalized difference vegetation index and the green leaf index), but not with SAR data derived from Sentinel-1. We demonstrate, perhaps for the first time, how the leaf phenology of individual large-canopied tropical trees can directly influence the spectral signature of satellite pixels through time. Additionally, while the level of uncertainty in our model predictions is still very high, we believe this study shows that we are near the threshold for orbital monitoring of individual crowns within tropical forests, even in challenging locations, such as cloudy Gabon. Further technical advances in remote sensing instruments into the spatial and temporal scales relevant to organismal biological processes will unlock great potential to improve our understanding of the Earth system.

scholarly journals Detection of Archaeological Looting from Space: Methods, Achievements and Challenges

2019 ◽  
Vol 11 (20) ◽  
pp. 2389 ◽  
Author(s):  
Deodato Tapete ◽  
Francesca Cigna
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
High Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Google Earth ◽  
Optical Data ◽  
Mena Region ◽  
Processing Methods ◽  
Optical Imagery

Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment.

scholarly journals Monitoring Land Cover Changes in the Tropics using Satellite Remote Sensing Data

2019 ◽  
Vol 8 (12) ◽  
pp. 3631-3636
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Satellite Data ◽  
Land Cover Changes ◽  
Classification Methods ◽  
Land Covers ◽  
The Tropics ◽  
Land Cover Change Detection

Changes in land cover are inevitable phenomena that occur in all parts of the world. Land cover changes can occur due to natural phenomena that include runoff, soil erosion and sedimentation besides man-made phenomena that include deforestation, urbanization and conversion of land covers to suit human needs. Several works on change detection have been carried out elsewhere, however there were lack of effort in analyzing the issues that affect the performance of existing change detection techniques. The study presented in this paper aims to detect changes of land covers by using remote sensing satellite data. The study involves detection of land cover changes using remote sensing techniques. This makes use satellite data taken at different times over a particular area of interest. The data has resolution of 30 m and records surface reflectance at approximately 0.4 to 0.7 micrometers wavelengths. The study area is located in Selangor, Malaysia and occupied with tropical land covers including coastal swamp water, sediment plumes, urban, industry, water, bare land, cleared land, oil palm, rubber and coconut. Initially, region of interests (ROI) were drawn on each of the land covers in order to extract the training pixels. Landsat satellite bands 1, 2, 3, 4, 5 and 7 were then used as the input for the three supervised classification methods namely Support Vector Machine (SVM), Maximum Likelihood (ML) and Neural Network (NN). Different sizes of training pixels were used as the input for the classification methods so that the performance can be better understood. The accuracy of the classifications was then assessed by analyzing the classifications with a set of reference pixels using a confusion matrix. The classification methods were then used to identify the conversion of land cover from year 2000 to 2005 within the study area. The outcomes of the land cover change detection were reported in terms quantitative and qualitative analyses. The study shows that SVM gives a more accurate and realistic land cover change detection compared to ML and NN mainly due to not being much influenced by the size of the training pixels. The findings of the study serve as important input for decision makers in managing natural resources and environment in the tropics systematically and efficiently.

scholarly journals Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

2017 ◽  
Vol 21 (12) ◽  
pp. 6235-6251 ◽  
Author(s):  
Guiomar Ruiz-Pérez ◽  
Julian Koch ◽  
Salvatore Manfreda ◽  
Kelly Caylor ◽  
Félix Francés
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sub Saharan Africa ◽  
Automatic Calibration ◽  
Calibration And Validation ◽  
Sensing Data ◽  
Spatio Temporal

Abstract. Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment – the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

scholarly journals Investigating Sentinel 2 Multispectral Imagery Efficiency in Describing Spectral Response of Vineyards Covered with Plastic Sheets

Agronomy ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1909
Author(s):  
Enrico Borgogno-Mondino ◽  
Laura de Palma ◽  
Vittorino Novello
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Table Grape ◽  
Optical Data ◽  
Strong Impact ◽  
Spectral Indices ◽  
Intrinsic Nature ◽  
Sentinel 2

The protection of vineyards with overhead plastic covers is a technique largely applied in table grape growing. As with other crops, remote sensing of vegetation spectral reflectance is a useful tool for improving management even for table grape viticulture. The remote sensing of the spectral signals emitted by vegetation of covered vineyards is currently an open field of investigation, given the intrinsic nature of plastic sheets that can have a strong impact on the reflection from the underlying vegetation. Baring these premises in mind, the aim of the present work was to run preliminary tests on table grape vineyards covered with polyethylene sheets, using Copernicus Sentinel 2 (Level 2A product) free optical data, and compare their spectral response with that of similar uncovered vineyards to assess if a reliable spectral signal is detectable through the plastic cover. Vine phenology, air temperature and shoot growth, were monitored during the 2016 growing cycle. Twenty-four Copernicus Sentinel 2 (S2, Level 2A product) images were used to investigate if, in spite of plastic sheets, vine phenology can be similarly described with and without plastic covers. For this purpose, time series of S2 at-the-ground reflectance calibrated bands and correspondent normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index, version two (MSAVI2) and normalized difference water index (NDWI) spectral indices were obtained and analyzed, comparing the responses of two covered vineyards with different plastic sheets in respect of two uncovered ones. Results demonstrated that no significant limitation (for both bands and spectral indices) was introduced by plastic sheets while monitoring spectral behavior of covered vineyards.

scholarly journals Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data

2021 ◽  
Vol 13 (2) ◽  
pp. 243
Author(s):  
Amal Chakhar ◽  
David Hernández-López ◽  
Rocío Ballesteros ◽  
Miguel A. Moreno
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Remote Sensing Data ◽  
Radar Data ◽  
Optical Data ◽  
Support Vector ◽  
Sensing Data ◽  
Crop Classification

The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.

scholarly journals Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model

2010 ◽  
Vol 7 (1) ◽  
pp. 387-428 ◽  
Author(s):  
S. Bathiany ◽  
M. Claussen ◽  
V. Brovkin ◽  
T. Raddatz ◽  
V. Gayler
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Forest Canopy ◽  
Forest Cover ◽  
Co2 Concentration ◽  
Earth System Model ◽  
System Model ◽  
Climate Mitigation ◽  
Earth System ◽  
The Tropics

Abstract. Afforestation and reforestation have become popular instruments of climate mitigation policy, as forests are known to store large quantities of carbon. However, they also modify the fluxes of energy, water and momentum at the land surface. Previous studies have shown that these biogeophysical effects can counteract the carbon drawdown and, in boreal latitudes, even overcompensate it due to large albedo differences between forest canopy and snow. This study investigates the role forest cover plays for global climate by conducting deforestation and afforestation experiments with the earth system model of the Max Planck Institute for Meteorology (MPI-ESM). Complete deforestation of the tropics (18.75° S–15° N) exerts a global warming of 0.4 °C due to an increase in CO2 concentration by initially 60 ppm and a decrease in evapotranspiration in the deforested areas. In the northern latitudes (45° N–90° N), complete deforestation exerts a global cooling of 0.25 °C after 100 years, while afforestation leads to an equally large warming, despite the counteracting changes in CO2 concentration. Earlier model studies are qualitatively confirmed by these findings. As the response of temperature as well as terrestrial carbon pools is not of equal sign at every land cell, considering forests as cooling in the tropics and warming in high latitudes seems to be true only for the spatial mean, but not on a local scale.

scholarly journals Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model

2010 ◽  
Vol 7 (5) ◽  
pp. 1383-1399 ◽  
Author(s):  
S. Bathiany ◽  
M. Claussen ◽  
V. Brovkin ◽  
T. Raddatz ◽  
V. Gayler
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Forest Canopy ◽  
Forest Cover ◽  
Co2 Concentration ◽  
Earth System Model ◽  
System Model ◽  
Climate Mitigation ◽  
Earth System ◽  
The Tropics

Abstract. Afforestation and reforestation have become popular instruments of climate mitigation policy, as forests are known to store large quantities of carbon. However, they also modify the fluxes of energy, water and momentum at the land surface. Previous studies have shown that these biogeophysical effects can counteract the carbon drawdown and, in boreal latitudes, even overcompensate it due to large albedo differences between forest canopy and snow. This study investigates the role forest cover plays for global climate by conducting deforestation and afforestation experiments with the earth system model of the Max Planck Institute for Meteorology (MPI-ESM). Complete deforestation of the tropics (18.75° S–15° N) exerts a global warming of 0.4 °C due to an increase in CO2 concentration by initially 60 ppm and a decrease in evapotranspiration in the deforested areas. In the northern latitudes (45° N–90° N), complete deforestation exerts a global cooling of 0.25 °C after 100 years, while afforestation leads to an equally large warming, despite the counteracting changes in CO2 concentration. Earlier model studies are qualitatively confirmed by these findings. As the response of temperature as well as terrestrial carbon pools is not of equal sign at every land cell, considering forests as cooling in the tropics and warming in high latitudes seems to be true only for the spatial mean, but not on a local scale.

scholarly journals The Sector Development at Tanjung Sakti, Mount Dempo, Bengkulu by Using Integration of Remote Sensing Data and TOPEX Gravity Satellite

2021 ◽  
Vol 873 (1) ◽  
pp. 012015
Author(s):  
Zahrah Athirah ◽  
Muhammad Dhery Mahendra
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Land Surface ◽  
Vegetation Index ◽  
Hot Springs ◽  
Gravity Data ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Volcanic Complex ◽  
Sensing Data

Abstract Mount Dempo is the highest volcano in South Sumatra, which lies between the Bukit Barisan mountains and Gumai. The mountain located in Dempo Makmur Village, Sub-district of Pagar Alam, Lahat Regency, South Sumatra is located at an altitude of 3173 meters above sea level with coordinates of 4.03 ° S 103.13 °E. Mount Dempo’s morphology is formed by pyroclastic deposits consisting of Tuff and Sand rocks. Mount Dempo’s vegetation is dominated by Cassia sp. and Camellia sinensis for upper vegetation, while Strobilanthes hamiltoniana and Strophanthus membranifolium dominate the undergrowth. The purpose of this study is to identify geological structures to predict geothermal prospect areas by integrating remote sensing data and TOPEX Gravity Satellite Data. The remote sensing data used in this study is Landsat 8. This data is used to analyze Land Surface Temperature (LST) from a single thermal infrared band, surface emissivity based on Normalization Difference Vegetation Index (NDVI) from the study area and determine structure delineation. Gravity Satellite Data is used to map gravity anomalies in the volcanic complex of Mount Dempo. Gravity data processing produces a high anomaly zone in the northern part of the study area and is predicted as a prospect area because it is assumed to be related to the plutonic body. High density contrast indicates that there is an error in that area. In line with the error, there are several hot springs because the error serves as a pathway for geothermal fluid to rise to the surface. The study believes that with all the facts stated above, the spots which are located in Tanjung Sakti, Mount Dempo district are very prospective to be developed as a geotourism complex, in which could also increase the welfare of the local citizens.

scholarly journals Assessing evapotranspiration drivers and patterns at multiple scale in the Amazon basin

2020 ◽  
Author(s):  
Adriana Aparecida Moreira ◽  
Anderson Luis Ruhoff
Keyword(s):  
Remote Sensing ◽  
Tropical Forest ◽  
Amazon Basin ◽  
Vegetation Index ◽  
Dry Season ◽  
Limiting Factors ◽  
Net Radiation ◽  
Wet Season ◽  
Basin Scale ◽  
The Tropics

<p>Evapotranspiration (ET) is a key variable to terrestrial climate system, transferring water from the surface to the atmosphere, regulating air temperature and carbon exchanges, thus, linking the water, carbon and water cycles. Despite its great importance, ET patterns in tropical biomes are not fully understood yet. Studies with eddy covariance (EC) ET measurements and remote sensing models demonstrated a huge importance over ET drivers and limiting factors. In this context, this study aimed to assess the ET process in the tropics, from local to basin scale, using EC measurements (from the LBA project) and remote sensing models (MOD16 and GLEAM). At local scale, measurements and estimates were evaluated against net radiation, precipitation and vegetation index (EVI), in order to assess how these drivers control ET patterns. Then, a Budyko approach was applied at basin scale to calculate how water and energy constrain ET in large basins, including Amazon, Solimões, Purus, Medeira, Tapajós, and Xingu rivers. Our results demonstrated disagreements between models to represent maximum and minimum ET rates at tropical forest vegetation (at K43, K67 and K83 sites), with ET measurements peaking during the dry season, in a pattern coincident with annual net radiation cycle. Moreover, deep rooting of well-established rainforests, available soil moisture and increased solar radiation allow ET processes to be maintained during the dry season. ET estimates from MOD16 algorithm agree with these patterns, however, estimates from GLEAM indicates maximum ET rates during the rainy season. At cropland/pasture vegetation (at K77 site), also located in central Amazon, EC measurements showed moderate negative agreement with net radiation (R² = -0.48) and positive with precipitation (R² = 0.53), with decreasing ET rates during the dry season. GLEAM showed ET rates reduction in dry months, but also showed a peak in during wet season, while increasing ET estimates are observed for MOD16, both presented similar behavior as in tropical forest sites. Furthermore, measurements in the southwest part (RJA and FNS sites) did not show clear seasonal patterns, and both MOD16 and GLEAM algorithms, agree with decreasing ET rates during the dry season, showing a significant relationship with precipitation and vegetation indices. Results based on the Budyko approach indicated agreement between the models, indicating a predominant energy-limited condition when evaluated whole basin (at Óbidos station), or basins located in the northern and western parts of Amazon (in Amazon, Purus, and Negro basins), which corroborates with other studies, where ET has limited energy availability. However, our results also demonstrated disagreements in basins located in the southern and eastern parts (in Madeira, Tapajós and Xingu basins), where MOD16 showed some water-limited conditions, whilst it was not observed for GLEAM algorithm. Whether the models agree in terms of seasonality and water and energy limitations, they also disagree between them and ground measurements. This study highlighted the importance to understand limitations of multi-models and multi-scale ET processes for hydroclimatological studies in the tropics.</p>

scholarly journals Land cover monitoring as part of a survey on wetland ecosystem conservation in the Negovan village area using remote sensing tools

2019 ◽  
pp. 175-188
Author(s):  
Kameliya Radeva ◽  
Emiliya Velizarova ◽  
Adlin Dancheva
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Satellite Data ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Vegetation Period ◽  
Wetland Ecosystem ◽  
Temporal Space ◽  
Temporal Intervals ◽  
Sentinel 2

The main purpose of the present survey is to apply remote sensing data to the investigation of different components of a wetland ecosystem, situated in the area of the village of Negovan (Sofia region), such as soil, vegetation and water, and their variation for certain temporal intervals including the vegetation period. This survey represents the process of interim ecological monitoring (IEM) implementation on the studied ecosystem. Data for the current condition of different ecosystem components - soil, vegetation and water components, and their variations within the selected time period of 5 years (2014-2018) have been obtained. Specific relations among wetland actual components conditions such as soil wetness and vegetation vs climate factors within the respective temporal intervals of wetland monitoring process have been established. Aerospace data with different temporal, space and spectral resolution, satellite data from Sentinel 2, MSI and aerophoto with a very high resolution have been used. The results for ?Brightness?, ?Greenness? and ?Wetness? components obtained on the basis of orthogonalization of satellite data from Sentinel 2 have been introduced. The results reflect the value of Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI 2), Normalized Difference Greenness Index (NDGI) and Normalized Difference Water Index (NDWI), which are of great importance for the relationship between soil health indexes and ecosystem sustainability. Thematic maps are generated based on the results obtained by surveying land cover components. Data received for the current condition of Negovan wetland ecosystem and established variations of different parameters, including soil component could be used while assessing wetland ecosystem services.

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