Although the use of composite data already reduces the effect of noise like cloud contamination, shadow, sun angle or aerosol effects (Holben, 1986; Huete et al., 1994), data are still negatively influenced by noise, resulting in gener- ally underestimated NDVI values (Gu, 2006; Hird and Mc- Dermid, 2009). Several smoothing techniques like double logistic function-fitting (Beck, 2006; Hird and McDermid, 2009), asymmetric Gaussian function-fitting (J¨onsson and Eklundh, 2002; Hird and McDermid, 2009) or Savitzky- Golay filter (Savitzky and Golay, 1964; Chen et al., 2004; Gong et al., 2006; Doraiswamy et al., 2007; Hird and Mc- Dermid, 2009; Ren et al., 2008) are used to model a nearly noise-free NDVItimeseries following the main assumption that vegetation follows a continuous sequence drawn by in- crease, peak and decrease ofNDVI, describing a clear math- ematical function. Modelling this mathematical function, smoothing algorithms eliminate depressions of TSD. How- ever, the purpose of these algorithms is not to distinguish between depressions caused by clouds or atmospheric distur- bance nor human caused depressions and other natural dis- turbances (Evrendilek and Gulbeyaz, 2008). In order to keep responses to climate or other drivers it is assumed that noise (mostly technical or physical caused) depresses single NDVI composites while climate conditions, human impacts or other drivers depress a sequence ofNDVI composites. Thus, the simple but effective smoothing technique (Eq. 1) introduced by Gu et al. (2006) is applied, which assumes that NDVI is always depressed and never overrated by noise. The algo- rithm reduces impact of single contaminated data points and keeps the upper envelope. Following Gu et al. (2006) the algorithm was applied three times in order to remove single noise contaminations more efficiently.
Vegetationcoverand its dynamicsand trends are of interest for many: starting from herders, crop farmers and wildlife managers to decision makers, planners and wide profile of scientists. Discussed here is an attempt to assess thevegetation temporal dynamicsusingtimeseries NOAA satellite 1 km data that coverthe territory of Mongolia inthe period of 1989 – 2002. Normalized Differences Vegetation Index (NDVI) and Departure from Average methods were employed to assess thevegetationcover status and its changes and trends over 14 years. The author has used raw data from the NOAA satellite active archive for this study and processed through Erdas Imagine and ArcGIS software packages. This study can be useful tool for land, pasture, wildlife managers and others interested invegetationcover changes over vast areas of Mongolia and valuable in case where lack ofvegetationdata.
According to Zhang et al. (2003), field-based ecological studies have demonstrated that vegetation phenology tends to follow relatively well defined temporal patterns. For example, in deciduous vegetationand many crops, leaf emergence tends to be followed by a period of rapid growth, followed by a relatively stable period of maximum leaf area. Different types ofvegetation have different temporal growth patterns (i.e., different growth and senescence rates) (BRUCE et al., 2006). Vegetationdynamics indicate important short and long-term ecological process. Continuous temporal observations ofland surface parameters using remote sensing reveal seasonal and inter- annual developments. Vegetation indices have been extensively applied to characterize the state anddynamicsofvegetation, in particular multiple NDVI (Normalized Difference Vegetation Index) datasets ofthe Advanced Very High Resolution Radiometer (AVHRR) instrument used duringthelast 25 years (COLDIZ et al., 2007; JENSEN, 2000).
The Dynamic Time Warping (DTW) classifier has been demonstrated to be a capable solution to deal with some of these challenges [15,16]. Since it can “fill-in” temporal gaps inthe remote sensing timeseries (e.g., cloudy images), it has been successfully applied in satellite imagery time-seriesanalysis [17,18]. However, the distinctive phenological cycle of each LULC class requires an equilibrium between shape matching and temporal alignment [19,20], which is why Maus et al.  improved the DTW algorithm. Maus et al.  proposed theTime-Weighted Dynamic Time Warping (TWDTW) method that includes time-weighting to account for seasonality. This yields better timeseries to benefit the LULC mapping. Inthe research of Maus et al. , this method was applied to the LULC classification of a tropical forest area from MODIS enhanced vegetation index (EVI) data. It had the highest accuracy for the forest, pastures, single cropping and double cropping classes’ classification and an overall accuracy of about 87%. Belgiu and Csillik  evaluated the TWDTW method for mapping different cropland types in two European cities (in Italy and Romania) and one American city (in California) from Sentinel-2 normalized difference vegetation index (NDVI) timeseries. In their research, this method achieved a higher overall accuracy when classifying different cropland classes inthe two European study areas compared to the classification results ofthe RF method . Also, the TWDTW method proved it could produce good output in terms of mapping accuracy, even with few training samples [15,23].
Much research has been carried out usingtimeseriesof remotely sensed vegetation index (VI) data to detect land-use andland-cover change. Pioneering works relied on advanced very high resolution radiometer (AVHRR) products, but recently Modis has been used more often. Different techniques are used to perform VI timeseriesanalysis, such as Fourier transforms (Jakubauskas et al., 2002), Savitzky‑Golay filtering (Jönsson & Eklundh, 2004), wavelet transforms (Sakamoto et al., 2005), mathematical functions for filtering and smoothing (Zhang et al., 2003), and probability distribution-based analysis (White & Nemani, 2006). Other researchers have focused on the use of spectral-temporal response surfaces, analyzing not only the VI timeseries but also the temporal variation ofthe different spectral bands in
Remote sensing has long been used as a means of detecting and classifying changes on theland. Analysisof multi-year timeseriesofland surface attributes and their seasonal change indicates a complexity ofland use landcover change (LULCC). This paper explores the temporal complexity ofland change considering temporal vegetationdynamics, in other words, distinguishing the changes regarding to their properties in long-term image analysis. This study is based on the hypothesis that landcover might be dynamics; however, consistent land use has a typical, distinct and repeated temporal pattern ofvegetation index inter-annually. Therefore, pixels represent a change when the inter-annual temporal dynamics is changed. We analysed thedynamics pattern of long-term image dataof wavelet-filtered MODIS EVI from 2001 to 2007. The change of temporal vegetationdynamics was detected by differentiating distance between two successive annual EVI patterns. Moreover, we defined the type of changes usingthe clustering method, which were then validated by ground check points and secondary data sets.
The potentially altered areas of GLC2000 are determined using a classification proce- dure based on the hierarchical aggregation oflandcover classes. Initially, the GLC2000 data set is used to select 26 classes intheNDVI SPOT VGT images. TheNDVI profiles are extracted for each class usingthe year 2000 as reference. A hierarchical structuring of classes is determined, which consists ofthe aggregation ofthe two most similar, level-by- level, until only one class is left. At each level, a similarity measure is applied andthe two most similar classes are merged into a new class. Two distinct methods are used: one that preserves the initial profiles (signatures) in all hierarchical levels, and a second that uses the mean ofthe original signatures to create the signature ofthe new one. A cross-validation process (Geisser 1975; Stone 1974) is used to estimate the classification agreement for each class at each level of aggregation. The acquisition of ground truth data for classi- fication assessment of large areas and temporal continuity is an uncommon practice due to limitations such as costs, time, and logistical challenges (Loveland et al. 2000). Due to the impossibility of acquiring ground truth data for this work, the term ‘classification agreement’ used in this article is related to GLC2000, which is considered as the reference. In order to study the influence of temporal variability inthe classification results, the methodology proposed was applied for the years 1999, 2001, 2008, and 2010. Two approaches were followed: one that considers theNDVI profiles oflandcover used for training from GLC2000 unchanged over the years, using training from the year under anal- ysis; and a second that eliminates the problems related to annual coverage, using training from the reference year (2000) and control samples from the test year (1999, 2001, 2008, or 2010).
The orbital monitoring of Brazilian flora began inthe 1970s with Radam project, when were used dataofthe Side-Looking Airborne Radar (SLAR). Other programs were conducted in order to know and monitor the conservation status of Brazil’s biomes. The Programa de Cálculo de Desflorestamento da Amazônia (PRODES), the Detecção em Tempo Real (DETER), the Projeto de Conservação e Utilização Sustentável da Diversidade Biológica Brasileira (PROBIO) and Projeto de Monitoramento do Desmatamento nos Biomas Brasileiros por Satélite (PMDBBS) were some such initiatives. In addition to government actions, research is being conducted by institutions of national and international academic and scientific nature. However, most efforts were directed to the Amazon ecosystem or used techniques supported by monotemporal images, resolution space-times is not compatible with the current dynamicsof change ofthe landscape. Given this reality, we developed this work aiming to study a transition region of semi-arid ecosystems (Cerrado-Caatinga), located inthenorthof Minas Gerais and Bahia southwest, Brazil. This place has rich biodiversity, consisting of a complex flora and that has not been well studied. Its landscape begins to undergo changes due to significant growth of human activities related to agriculture, leveraged by the realization of major irrigation projects created between the 1970s and 1990s. Thus, we used the following method: use ofNDVI-MODIStime-series from 2011 to 2013, deriving from the MOD09Q1 product; use of median and Savitzky-Golay filters; selection of temporal-signature references ofland use andvegetationcoverand use of classifiers similarity and distance Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM) and Euclidian Distance Measure (ED).The results were satisfactory especially when usingthe classifier ED on timeseriesNDVI-MODIS, reaching levels of agreement, Global Accuracy and Kappa, more than 82% and 0.75. However, more studies should be made to identify the shrub and herbaceous vegetation types ofthe Cerrado biome andthe crops grown on farms of small extent, present in large numbers on the left of São Francisco river.
Suppose that the subunit denoted 1 of bainitic ferrite forms without diffusion, but any excess carbon is soon rejected into the residual austenite. Consequently, all the subunits denoted 1 were formed at the early stage of transformation from austenite whose carbon concentration is initially identical to that of bulk alloy (region of upper bainite). The subunits denoted 2 and 3 were formed from enriched austenite as a consequence of carbon redistribution occurring after the growth event (region of lower bainite). The transition between these two regions is not sharply defined. There is then the possibility ofthe reaction beginning with the growth of upper bainite but decomposing to lower bainite from the enriched austenite at the later stages of reaction. This explains why both upper and lower bainite sometimes can be found inthe same temperature.
household, the program caused an increase of more than 11 percentage points inthe probability of attending school. One possible reason for this increase inthe estimated impact is the fact that his/her family is only receiving any transfers from the Bolsa Familia program because he/she is attending school. The fear of losing access to the program, which means that it may take time to come back to it in case of harder times ahead, may stimulate parents to monitor their kids’ school attendance more strongly. When these two features were combined — i.e. male youngsters who were the youngest child — the probability of attending school increased by 16.2 percentage points and it is statistically significant at the 1 per cent level.
As várzeas amazônicas são um importante componente do bioma Amazônico, mas impactos antrópicos e climáticos têm levado à perda florestal e à interrupção de processos e serviços ecossistêmicos. O presente estudo teve como objetivos avaliar a aplicabilidade do algoritmo Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) na detecção de mudanças na cobertura florestal de várzea no Baixo Amazonas, e analisar o potencial de atributos espectrais e temporais na classificação das perdas florestais em antrópicas ou naturais. Utilizamos uma série temporal de 37 imagens Landsat TM e ETM+, adquiridas entre 1984 e 2009. Aplicamos o algoritmo LandTrendr para detectar mudanças na cobertura florestal e extrair os atributos de “duração”, “magnitude” e “ano de início” das mudanças, além de “NDVI ao final da série”. A detecção se restringiu a áreas identificadas como cobertura florestal no início e/ou final da série. Os atributos derivados da série temporal foram classificados pelo algoritmo Support Vector Machine (SVM), diferenciando as perdas florestais antrópicas e naturais. A confiabilidade da detecção dos eventos de mudança foi consistentemente alta ao longo do rio Amazonas, e mais variável no interior da várzea. As trajetórias espectrais-temporais representaram fielmente os eventos de mudança na cobertura florestal, com base em averiguações em campo. A perda da cobertura florestal por causas antrópicas foi maior (1.071 ha) do que por causas naturais (884 ha), com exatidão global de classificação de 94%. Concluímos que o algoritmo LandTrendr é uma ferramenta confiável para aplicação em estudos de dinâmica da cobertura florestal de várzea.
Abstract: The research was carried with the aim to discover the existence of securing the foremost islands and state border region ofthe Republic of Indonesia reviewed from a legal perspective, which is directly related to the existence of security and dispute resolution methods as well as the governance ofthe foremost islands and border region in Kalimantan which bordering Malaysia. This study was conducted in Nunukan district andthe surrounding provinces of Kalimantan, in this research method that used is normative legal analysisdata with juridical and qualitative descriptive approach. The results showed that the security of foremost islands and border region of law perspective in accordance with the Law No. 34 of 2004 regarding the Indonesian National Army has not been implemented to the fullest to realize the security of foremost islands and border region as the frontline ofthe Republic of Indonesia. The existence of leading islands securing andthe border region ofthe Republic of Indonesia still contain many weaknesses in terms of both governance and security.
An accurate and objective estimate on the extent of agroforestry in Europe is critical for the development of supporting policies. Despite the fact that agroforestry can be found almost everywhere it is hard to find reliable data on the extent of agroforestry, especially in Europe. However, databases that can be used to provide an estimate on the extent of agroforestry in Europe are available. The CORINE landcover classification (European Environment Agency, 1995) contains landcoverdata for Europe and includes thelandcover class “agroforestry”. Nevertheless, it is obvious from previous studies that agroforestry is practiced on a much wider scale than estimated by the CORINE database. A recent literature study summarising the currently available data sources estimated that agroforestry is practiced in Europe at least on an area of 10.6 million hectares equivalent to 6.5% ofthe utilized agricultural area (den Herder et al. 2015) which is considerably more than the 3.3 million hectares as estimated by CORINE. However, even though literature studies are useful to understand the context, data obtained from literature studies are not collected in a comparable way which makes it difficult to give a reliable estimate. For this reason, a more harmonized and uniform pan-European estimate is needed. In this report we try to answer the question: How much agroforestry is there in Europe and where is it?
The results of TerraClass 2008 Project showed the predominance of pastures (62%) and secondary vegetation (21%) among the Amazonian post-deforestation landscape. At the same time, annual crop cultivation, historically referred as one ofthe most important vectors of deforestation, was present in less than 5% ofthe deforested areas inthe Brazilian Legal Amazon in 2008. In general, results from validation with high spatial imagery (SPOT 5) indicate that the TerraClass classification methodology, usingMODISand Landsat data, appears reasonably accurate for the discrimination ofthe major land use andlandcover classes inthe Amazon region. Further editions of TerraClass Project opens up new opportunities for scientists and policy makers to track changes inlandcoverandland use inthe Brazilian Amazon at high resolution, as well as to assess the initial drivers of deforestation and monitor how deforestation dynamics may also be changing over timeand space.
In order to characterize resilience, we analyzed thetimeseries 1982–2003 of 8 km GIMMS AVHRR-NDVI maps ofthe Italian territory. Persistence probability of negative and positive trends was estimated according to thevegetationcover class, altitude, and climate. Generally, mean recovery times from negative trends were shorter than those estimated for positive trends, as expected for vegetationof healthy sta- tus. Some signatures of inefficient resilience were found in high-level mountainous areas andinthe Mediterranean sub- tropical ones. This analysis was refined by aggregating pix- els according to phenology. This multitemporal clustering synthesized information on vegetationcover, climate, and orography rather well. The consequent persistence estima- tions confirmed and detailed hints obtained from the pre- vious analyses. Under the same climatic regime, different vegetation resilience levels were found. In particular, within the Mediterranean sub-tropical climate, clustering was able to identify features with different persistence levels in ar- eas that are liable to different levels of anthropic pressure. Moreover, it was capable of enhancing reduced vegetation
This study deals with land use andlandcover changes for a 33 years period. We assessed these changes for eight landcover classes inthe south of Benin by using an integrated multi-temporal analysisusing three Landsat images (1972 Landsat MSS, 1986 Landsat TM and 2005 Landsat ETM+). Three scenarios for the future were simulated using a first-order Markovian model based on annual probability matrices. The contribution of tree plantations to compensate forest loss was assessed. The results show a strong loss of forest and savanna, mainly due to increased agricultural land. Natural woody vegetation (“forest”, “wooded savanna” and “tree and shrub savanna”) will seriously decrease by 2025 due to the expansion of agricultural activities andthe increase of settlements. Tree plantations are expected to double by 2025, but they will not compensate for the loss of natural woody vegetationcover. Consequently, we assist to a continuing woody vegetation area decrease. Policies regarding reforestation and forest conservation must be initiated to reverse the currently projected tendencies.
After 19 years of intensive soil use and negative environmental impacts (MONTEIRO; DO SUL; COSTA, 2018) and positive, there was an expansion of areas with mixed vegetation, reaching 1325.07 ha, which represents almost 70% ofthe total area archipelago. This shows an increase of approximately 46% between 1999 and 2018. However, the current vegetationcoverofthe island can transmit to visitors and residents a preserved natural beauty, but the environmental impacts caused by invasive species inthe area are diverse and negative for biodiversity ofthe region, giving the false impression of rich and diverse vegetation. According to Batistella (1996), Cleto (2013) and Mello (2014), Noronha presents exotic species that potentiate the elimination of most other species, altering biodiversity and being one ofthe main threats to the conservation of this insular environment. These were introduced from the 18th century, with the occupation ofthe Captaincy of Pernambuco. This promoted the destruction ofthevegetationcoverof all the large trees ofthe island, and was a preventive measures to avoid leaks through the construction of rafts, besides ofthe cut ofthe small trees had the objective of avoiding hiding places (CHAVES JÚNIOR, 2017).
The Tietê-Jacaré Hydrographic Basin region has a high potential for the development of agricultural activities, which were confirmed by the classification ofland use and coverage that demonstrated the predominance of such activities in all regions ofthe basin. There was a reduction in drainage areas and an increase inthe number of roads that consequently culminated in changes in connectivity and fragmentation patterns, where urban areas and sugarcane are dominant in these interconnections. This predominance must be observed in more detail about the planning ofthe hydrographic basin, mainly related to the possible negative impacts caused by them.
The convex geometry (simplex model) is intensely used to detect endmembers, as it establishes a spatial arrangement for image pixels that describes the material mixture relations (Tompkins et al., 1997; Winter, 1999; Batenson et al., 2000; Berman et al., 2004; Carvalho J´unior et al., 2005). This geom- etry is visualized in a n-dimensional space, where the axes are non-correlated and describe the inherent dimensionality, such as the Principal Component Analysis – PCA and MNF com- ponents (Smith et al., 1985, 1990; Bateson & Curtiss, 1993, 1996; Boardman, 1993). The vertices of this simplex are the pure pixels , while the other pixels inside the convex polygon are the results of their mixtures (Carvalho J´unior et al. 2003).