Reduction of the Long-Term Inaccuracy from the AVHRR–Based NDVI Data

This paper investigated the normalized difference vegetation index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data during 1982-2003, which was collected from five NOAA series satellites. An empirical distribution function (EDF) was developed to eliminate the long-term inaccuracy of the NDVI data derived from the AVHRR sensor on NOAA polar orbiting satellite. The instability of data results from orbit degradation as well as from the circuit drifts over the life of a satellite. Degradation of NDVI over time and shifts of NDVI between the satellites were estimated using the China data set, because it includes a wide variety of different ecosystems represented globally. It was found that the data for the years of 1988, 1992, 1993, 1994, 1995 and 2000 are not stable compared to other years because of satellite orbit drift, AVHRR sensor degradation, and satellite technical problems, including satellite electronic and mechanical satellite systems deterioration. The data for NOAA-7 (1982, 1983), NOAA-9 (1985, 1986), NOAA-11 (1989, 1990), NOAA-14 (1996, 1997), and NOAA-16 (2001, 2002) were assumed to be standard because the crossing time of satellite over the equator (between 1330 and 1500 hours) maximized the value of the coefficients. These years were considered as the standard years, while in other years the quality of satellite observations significantly deviated from the standard. The deficiency of data for the affected years were normalized or corrected by using the method of EDF and comparing with the standard years. These normalized values were then utilized to estimate new NDVI time series which show significant improvement of NDVI data for the affected years

Vegetation Dynamics in Ecuador

Global forest cover has suffered a dramatic reduction during recent decades, especially in tropical regions, which is mainly due to human activities caused by enhanced population pressures. Nevertheless, forest ecosystems, especially tropical forests, play an important role in the carbon cycle functioning as carbon stocks and sinks, which is why conservation strategies are of utmost importance respective to ongoing global warming. In South America the highest deforestation rates are observed in Ecuador, but an operational surveillance system for continuous forest monitoring, along with the determination of deforestation rates and the estimation of actual carbon socks is still missing. Therefore, the present investigation provides a functional tool based on remote sensing data to monitor forest stands at local, regional and national scales. To evaluate forest cover and deforestation rates at country level satellite data was used, whereas LiDAR data was utilized to accurately estimate the Above Ground Biomass (AGB; carbon stocks) at catchment level. Furthermore, to provide a cost-effective tool for continuous forest monitoring of the most vulnerable parts, an Unmanned Aerial Vehicle (UAV) was deployed and equipped with various sensors (RBG and multispectral camera). The results showed that in Ecuador total forest cover was reduced by about 24% during the last three decades. Moreover, deforestation rates have increased with the beginning of the new century, especially in the Andean Highland and the Amazon Basin, due to enhanced population pressures and the government supported oil and mining industries, besides illegal timber extractions. The AGB stock estimations at catchment level indicated that most of the carbon is stored in natural ecosystems (forest and páramo; AGB ~98%), whereas areas affected by anthropogenic land use changes (mostly pastureland) lost nearly all their storage capacities (AGB ~2%). Furthermore, the LiDAR data permitted the detection of the forest structure, and therefore the identification of the most vulnerable parts. To monitor these areas, it could be shown that UAVs are useful, particularly when equipped with an RGB camera (AGB correlation: R² > 0.9), because multispectral images suffer saturation of the spectral bands over dense natural forest stands, which results in high overestimations. In summary, the developed operational surveillance systems respective to forest cover at different spatial scales can be implemented in Ecuador to promote conservation/ restoration strategies and to reduce the high deforestation rates. This may also mitigate future greenhouse gas emissions and guarantee functional ecosystem services for local and regional populations

THE BIG PICTURE - SATELLITE REMOTE SENSING APPLICATIONS IN RANGELAND ASSESSMENT AND CROP INSURANCE

Livestock Production/Industries, Risk and Uncertainty,

Using datasets from the Internet for hydrological modeling: an example from the Kntnk Menderes Basin, Turkey

River basin development / Water resources / Data collection / Models / Hydrology / Land classification / Water management / Water scarcity / Water allocation / Stream flow / Water demand / Turkey / Kntnk Menderes Basin

Wetland change assessment on the Kafue Flats, Zambia : a remote sensing approach

The Kafue Flats floodplain wetland system in southern Zambia is under increasing climate and human pressures. Firstly, drought episodes appear more prevalent in recent years in the region and secondly, two dams were built on the lower and upper ends of the wetland in 1972 and 1978, respectively, across the Kafue River which flows through the wetland. The study uses multi-temporal remote sensing to assess change in extent and vigour of green vegetation, and extent of water bodies and dry land cover on the Kafue Flats. The change detection's management value is assessed. Four normalised, co-registered digital Landsat images from 24 September 1984, 3 September 1988, 12 September 1991 and 20 September 1994 were used. The main change detection method used was comparison of classifications, supplemented by Normalised Difference Vegetation Index (NDVI) and Principal Component Analysis (PCA) change detection. Ancillary land use and environmental data were used in interpreting the change in the context of cause and effect. The results indicate inconsistent trends in the changes of most land cover classes, as a result of manipulation of the wetland by man through annual variations in the timing and magnitude of regulated flows into the wetland, as well as burning. However, the results also show spatial reduction in the wetland's dry season dense green reed-grass vegetation in upstream sections which are not affected by the water backing-up above of the lower dam. Sparse green vegetation is replacing the dense green vegetation in these upstream areas. It is inferred that this dry season degradation of the wetland threatens bird species which may use the reeds for dry season nesting. It is proposed that ground surveying and monitoring work at the micro-habitat level is necessary to ascertain the implications of the losses. It is concluded that, in spite of difficulties, multi-temporal remote sensing has a potential role in wetland change assessment on the Kafue Flats at the community level, but that it needs to be supplemented by targeted, micro-habitat level ground surveys

Standardized time-series and interannual phenological deviation : new techniques for burned-area detection using long-term MODIS-NBR datase

Typically, digital image processing for burned-areas detection combines the use of a spectral index and the seasonal differencing method. However, the seasonal differencing has many errors when applied to a long-term time series. This article aims to develop and test two methods as an alternative to the traditional seasonal difference. The study area is the Chapada dos Veadeiros National Park (Central Brazil) that comprises different vegetation of the Cerrado biome. We used the MODIS/Terra Surface Reflectance 8-Day composite data, considering a 12-year period. The normalized burn ratio was calculated from the band 2 (250-meter resolution) and the band 7 (500-meter resolution reasampled to 250-meter). In this context, the normalization methods aim to eliminate all possible sources of spectral variation and highlight the burned-area features. The proposed normalization methods were the standardized time-series and the interannual phenological deviation. The standardized time-series calculate for each pixel the z-scores of its temporal curve, obtaining a mean of 0 and a standard deviation of 1. The second method establishes a reference curve for each pixel from the average interannual phenology that is subtracted for every year of its respective time series. Optimal threshold value between burned and unburned area for each method was determined from accuracy assessment curves, which compare different threshold values and its accuracy indices with a reference classification using Landsat TM. The different methods have similar accuracy for the burning event, where the standardized method has slightly better results. However, the seasonal difference method has a very false positive error, especially in the period between the rainy and dry seasons. The interannual phenological deviation method minimizes false positive errors, but some remain. In contrast, the standardized time series shows excellent results not containing this type of error. This precision is due to the design method that does not perform a subtraction with a baseline (prior year or average phenological curve). Thus, this method allows a high stability and can be implemented for the automatic detection of burned areas using long-term time series

Estimation of Surface Moisture Content and Evapotranspiration Using Weightage Approach.

Soil moisture (MC) and evapotranspiration (ET) are considered as the most significant boundary conditions controlling most of the hydrological cycle’s processes. However, monitoring them continuously over large areas using the high temporal-resolution optical satellites is very demanding. Satellites such as the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), have a coarse spatial resolution in their images. Thus it not only impedes the acquisition of an accurate MC and ET but also represents multispectral reflections from the holistic surface features. This beside their dependence on vegetation and ground coefficient when assessing MC and ET. The study aims to enhance the spatial accuracy by weighting the MC produced from different surface cover classes within the pixel. MC for each pixel is segmented into three (3) different classes namely urban, vegetation and multi surface cover according to their respective MC weightage. Secondly, to generate an improved actual ETa map by overlaying the segmented MC with a rectified ETo. Images from AVHRR and MODIS satellites were selected in order to generate MC and ET maps. Two powerful MC algorithms were used based on land Surface Temperature (Ts), vegetation Indices (VI) and field measurements of MC; which were conducted at variable depths to examine the depth influence on MC and Ts magnitudes

Sentinel-2 images for detection of wind damage in forestry

Using of Remote sensing for the sake of Earth Observation is getting more and more popular as the number of satellites that are able to measure electromagnetic radiation with a higher spatial, temporal and radiometric resolution is considerably rising. Of all usage of Earth Observation, detection of disturbances caused by natural catastrophe such as wind, earthquake and fire is highly important. On 12th of August 2017, a storm hit South and South East of Finland, bringing harsh disturbances to the forest area in which Pine and Spruce were the main types of land cover. The study area in this region contained the extent of a sentinel-2 image that covered an area of 100 km by 100 km. Two sentinel-2 images from 11th of August 2017 and 5th of September 2017 were used to measure spectra behavior of existing features before and after storm in the region. Forest use notifications data, by which damaged stands were identified, and forest-stand dataset, with which stands that were not touched by the storm (undamaged stands) were characterized, were used as ground truth data. For change extraction, univariate image differencing was used using six different indices, namely EVI, NDVI, NDMI, SATVI, TCB, and TCG. Two main approaches were taken in this thesis, namely pixelwise and average based, where in the former individual pixels were extracted (from stands) and used for training the models while in the later average of pixels inside each stand was calculated and used for training. Results achieved by average-based showed a better performance in terms of user accuracy and stability of the results than pixelwise approach did

Analysis of vegetation-activity trends in a global land degradation framework

Land degradation is a global issue on a par with climate change and loss of biodiversity, but its extent and severity are only roughly known and there is little detail on the immediate processes – let alone the drivers. Earth-observation methods enable monitoring of land resources in a consistent, physical way and on global scale by making use of vegetation activity and/or cover as proxies. A well-known spectral proxy is the normalized difference vegetation index (NDVI), which is available in high temporal resolution time series since the early 1980s. In this work, harmonic analyses and non-parametric trend tests were applied to the GIMMS NDVI dataset (1981–2008) in order to quantify positive changes (or greening) and negative changes (browning). Phenological shifts and variations in length of growing season were accounted for using analysis by vegetation development stage rather than by calendar day. This approach does not rely on temporal aggregation for elimination of seasonal variation. The latter might introduce artificial trends as demonstrated in the chapter on the modifiable temporal unit problem. Still, a major assumption underlying the analysis is that trends were invariant, i.e. linear or monotonic, over time. However, these monotonic trends in vegetation activity may consist of an alternating sequence of greening and/or browning periods. This effect and the contribution of short-term trends to longer-term change was analysed using a procedure for detection of trend breaks. Both abrupt and gradual changes were found in large parts of the world, especially in (semi-arid) shrubland and grassland. Many abrupt changes were found around large-scale natural influences like the Mt Pinatubo eruption in 1991 and the strong 1997/98 El Niño event. This marks the importance of accounting for trend changes in the analysis of long-term NDVI time series. These new change-detection techniques advance our understanding of vegetation variability at a multi-decadal scale, but do not provide links to driving processes. It is very complex to disentangle all natural and human drivers and their interactions. As a first step, the spatial relation between changes in climate parameters and changes in vegetation activity was addressed in this work. It appeared that a substantial proportion (54%) of the spatial variation in NDVI changes could be associated to climatic changes in temperature, precipitation and incident radiation, especially in forest biomes. In other regions, the lack of such associations might be interpreted as human-induced land degradation. With these steps we demonstrated the value of global satellite records for monitoring land resources, although many steps are still to be taken.</p

Multitemporal Imagery Based Analysis of Urban Land in St. Tammany Parish in Conjunction with Socioeconomic Data

The role of urbanization in the history of civilization is a profound and intricate part of human geography. By utilizing socioeconomic data and then integrating it with more technological innovations, such as remote sensing, the spread of sprawl and the urban corridor can better be mapped and quantified by researchers. Many different types of socioeconomic data were implemented in addition to the remotely sensed data. In this paper, six Landsat 5 TM images were used to create land cover classification maps of the developed or built-up land in St. Tammany Parish from 1984 to 2008. It was found that, in addition to St. Tammany expanding in population, the urban areas are becoming denser using a method called the remote method. This method is an advanced function of density that allows researchers to estimate consumption of the developed land